From 939b0b7a72bb91886420fc0acedcd8a55bf6f3da Mon Sep 17 00:00:00 2001 From: musi Date: Wed, 6 May 2026 18:38:36 +0800 Subject: [PATCH 1/8] feat(bfclv4): add instrumentation for Berkeley Function Call Leaderboard v4 Introduce loongsuite-instrumentation-bfclv4 covering BFCL v4 (bfcl_eval) per the design in llm-dev/bfclv4/execute.md: * ENTRY span around bfcl_eval._llm_response_generation.generate_results, with a narrow swap of that module's ThreadPoolExecutor name to a contextvars-propagating subclass so worker threads inherit the ENTRY trace context. * AGENT span around BaseHandler.inference (kind=AGENT, op=invoke_agent), picking up token usage from the metadata BFCL writes back. * STEP spans created reflectively for every concrete handler discovered via bfcl_eval.constants.model_config.MODEL_CONFIG_MAPPING; each STEP re-invokes the handler's _parse_query_response_* to harvest token counts and latency. * Per-call TOOL spans emitted from bfcl_eval.eval_checker.multi_turn_eval.multi_turn_utils.execute_multi_turn_func_call (one span per func_call entry in the batch). * Provider override mapping that routes OSSMODEL handlers to vllm/sglang based on args.backend, plus contextvars-based bfcl.turn_idx / gen_ai.react.round tracking. LLM spans are intentionally not created by this plugin; they continue to be produced by the downstream vendor SDK probes (OpenAI / Anthropic / DashScope / etc.). (cherry picked from commit cccf54b0b00c0d8b58c575798329268ad0d2b07f) Co-authored-by: 123liuziming <32130965+123liuziming@users.noreply.github.com> --- .../CHANGELOG.md | 22 + .../README.md | 79 ++ .../pyproject.toml | 54 ++ .../instrumentation/bfclv4/__init__.py | 297 ++++++++ .../bfclv4/internal/__init__.py | 13 + .../bfclv4/internal/attributes.py | 38 + .../bfclv4/internal/provider.py | 71 ++ .../instrumentation/bfclv4/internal/state.py | 93 +++ .../bfclv4/internal/threading_propagation.py | 43 ++ .../bfclv4/internal/wrappers.py | 691 ++++++++++++++++++ .../instrumentation/bfclv4/package.py | 17 + .../instrumentation/bfclv4/utils.py | 144 ++++ .../instrumentation/bfclv4/version.py | 15 + .../tests/__init__.py | 0 .../tests/test_instrumentor.py | 52 ++ .../tests/test_internals.py | 113 +++ 16 files changed, 1742 insertions(+) create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/CHANGELOG.md create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/README.md create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/pyproject.toml create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/__init__.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/__init__.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/attributes.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/provider.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/state.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/threading_propagation.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/wrappers.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/package.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/utils.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/version.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/tests/__init__.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/tests/test_instrumentor.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/tests/test_internals.py diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/CHANGELOG.md b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/CHANGELOG.md new file mode 100644 index 000000000..62fb6539b --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/CHANGELOG.md @@ -0,0 +1,22 @@ +# Changelog + +All notable changes to the LoongSuite BFCL v4 instrumentation are documented +in this file. + +## Unreleased + +### Added + +- Initial release of `loongsuite-instrumentation-bfclv4`. +- ENTRY span around `bfcl_eval._llm_response_generation.generate_results`. +- AGENT span around `bfcl_eval.model_handler.base_handler.BaseHandler.inference` + with cross-thread OTel context propagation via a narrow patch of + `bfcl_eval._llm_response_generation.ThreadPoolExecutor`. +- STEP spans created by reflectively wrapping each handler's + `_query_FC` / `_query_prompting` (discovered via + `bfcl_eval.constants.model_config.MODEL_CONFIG_MAPPING`). +- Per-call TOOL spans emitted by wrapping + `bfcl_eval.eval_checker.multi_turn_eval.multi_turn_utils.execute_multi_turn_func_call`. +- Provider override mapping for OSS handlers (vLLM / SGLang). +- Multi-turn `bfcl.turn_idx` and ReAct `gen_ai.react.round` tracking via + `contextvars`. diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/README.md b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/README.md new file mode 100644 index 000000000..7a4e5d69d --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/README.md @@ -0,0 +1,79 @@ +# LoongSuite BFCL v4 Instrumentation + +LoongSuite Python instrumentation for the [Berkeley Function Call +Leaderboard v4](https://github.com/ShishirPatil/gorilla/tree/main/berkeley-function-call-leaderboard) +(`bfcl-eval`, package `bfcl_eval`). + +## Span Topology + +``` +ENTRY enter_ai_application_system gen_ai.span.kind=ENTRY, op=enter +└─ AGENT invoke_agent {test_entry_id} gen_ai.span.kind=AGENT, op=invoke_agent + ├─ STEP react step gen_ai.span.kind=STEP, op=react + │ ├─ LLM chat {model} (created by downstream vendor SDK probe) + │ └─ TOOL execute_tool {fn} gen_ai.span.kind=TOOL, op=execute_tool + └─ STEP react step + └─ ... +``` + +This instrumentation deliberately does **not** create LLM spans. They are +emitted by the downstream vendor SDK probe (OpenAI / Anthropic / Google / +DashScope / LiteLLM / etc.) so that token usage and request payloads stay in +sync with the SDK that actually performed the request. + +## Installation + +```bash +pip install loongsuite-instrumentation-bfclv4 +``` + +## Usage + +```bash +opentelemetry-instrument bfcl generate \ + --model gpt-4o-2024-11-20-FC \ + --test-category simple_python \ + --num-threads 2 +``` + +Or programmatically: + +```python +from opentelemetry.instrumentation.bfclv4 import BFCLv4Instrumentor + +BFCLv4Instrumentor().instrument() +# ... run BFCL ... +BFCLv4Instrumentor().uninstrument() +``` + +## Compatibility With Downstream LLM SDK Probes + +| Scenario | Recommended downstream probe | +| --- | --- | +| OpenAI / OpenAI Responses / OSS via vLLM / SGLang / DeepSeek (OpenAI-compatible) | `opentelemetry-instrumentation-openai` | +| Anthropic / Claude | `loongsuite-instrumentation-claude-agent-sdk` | +| Gemini / Google | `loongsuite-instrumentation-google-adk` | +| Qwen / DashScope | `loongsuite-instrumentation-dashscope` | +| LiteLLM | `loongsuite-instrumentation-litellm` | + +## OSS Provider Notes + +For OSS handlers (vLLM / SGLang served via the OpenAI-compatible API), the +BFCL probe sets `gen_ai.provider.name` to `vllm` / `sglang` / `oss` and adds +`bfcl.oss.backend` for disambiguation. Downstream OpenAI probes will still +report `gen_ai.provider.name=openai` on the LLM span; this is expected. + +## Custom Attributes + +| Attribute | Where | Description | +| --- | --- | --- | +| `gen_ai.framework` = `bfclv4` | ENTRY/AGENT/STEP/TOOL | Framework tag | +| `bfcl.test_category` | ENTRY/AGENT | Test category | +| `bfcl.num_threads` | ENTRY | Configured thread pool size | +| `bfcl.test_case_count` | ENTRY | Number of test cases | +| `bfcl.run_ids` | ENTRY | Whether the run targeted specific IDs | +| `bfcl.test_entry_id` | AGENT | Test entry id | +| `bfcl.turn_idx` | STEP | Multi-turn turn index (0-based) | +| `bfcl.query_mode` | STEP | `FC` or `prompting` | +| `bfcl.oss.backend` | AGENT/STEP | `vllm` / `sglang` / `unknown` (only OSS) | +| `bfcl.tool.duration_is_estimated` | TOOL | True (latency is averaged across batch) | diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/pyproject.toml b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/pyproject.toml new file mode 100644 index 000000000..3eeb5d026 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/pyproject.toml @@ -0,0 +1,54 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "loongsuite-instrumentation-bfclv4" +dynamic = ["version"] +description = "LoongSuite BFCL v4 (Berkeley Function Call Leaderboard) instrumentation" +readme = "README.md" +license = "Apache-2.0" +requires-python = ">=3.10,<4" +authors = [ + { name = "OpenTelemetry Authors", email = "cncf-opentelemetry-contributors@lists.cncf.io" }, +] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Developers", + "License :: OSI Approved :: Apache Software License", + "Programming Language :: Python", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", +] +dependencies = [ + "opentelemetry-api >= 1.37.0", + "opentelemetry-instrumentation >= 0.58b0", + "opentelemetry-semantic-conventions >= 0.58b0", + "wrapt >= 1.0.0, < 2.0.0", + "opentelemetry-util-genai >= 0.3b0.dev0", +] + +[project.optional-dependencies] +instruments = [ + "bfcl-eval >= 4.0.0", +] + +[project.entry-points.opentelemetry_instrumentor] +bfclv4 = "opentelemetry.instrumentation.bfclv4:BFCLv4Instrumentor" + +[project.urls] +Homepage = "https://github.com/alibaba/loongsuite-python-agent/tree/main/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4" +Repository = "https://github.com/alibaba/loongsuite-python-agent" + +[tool.hatch.version] +path = "src/opentelemetry/instrumentation/bfclv4/version.py" + +[tool.hatch.build.targets.sdist] +include = [ + "/src", + "/tests", +] + +[tool.hatch.build.targets.wheel] +packages = ["src/opentelemetry"] diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/__init__.py b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/__init__.py new file mode 100644 index 000000000..34a5a9b10 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/__init__.py @@ -0,0 +1,297 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""LoongSuite BFCL v4 (Berkeley Function Call Leaderboard) instrumentation. + +Usage +----- + +.. code:: python + + from opentelemetry.instrumentation.bfclv4 import BFCLv4Instrumentor + + BFCLv4Instrumentor().instrument() + # ... run BFCL ... + BFCLv4Instrumentor().uninstrument() + +API +--- +""" + +from __future__ import annotations + +import importlib +import logging +from typing import Any, Collection, List, Tuple + +from wrapt import wrap_function_wrapper + +from opentelemetry.instrumentation.bfclv4.internal.wrappers import ( + BaseHandlerInferenceWrapper, + ExecuteFuncCallWrapper, + GenerateResultsWrapper, + QueryWrapper, + TurnBumpWrapper, +) +from opentelemetry.instrumentation.bfclv4.package import _instruments +from opentelemetry.instrumentation.bfclv4.utils import GenAIHookHelper +from opentelemetry.instrumentation.instrumentor import BaseInstrumentor +from opentelemetry.instrumentation.utils import unwrap + +logger = logging.getLogger(__name__) + +__all__ = ["BFCLv4Instrumentor"] + + +_GENERATE_RESULTS_MODULE = "bfcl_eval._llm_response_generation" +_GENERATE_RESULTS_NAME = "generate_results" + +_BASE_HANDLER_MODULE = "bfcl_eval.model_handler.base_handler" +_BASE_HANDLER_NAME = "BaseHandler.inference" + +_EXECUTE_TOOL_MODULE = ( + "bfcl_eval.eval_checker.multi_turn_eval.multi_turn_utils" +) +_EXECUTE_TOOL_NAME = "execute_multi_turn_func_call" + + +# ``MODEL_CONFIG_MAPPING`` already imports every concrete handler at module +# load time, so iterating over its values gives us the canonical handler +# class set without risking new vendor SDK imports. +def _iter_handler_classes() -> List[type]: + try: + from bfcl_eval.constants.model_config import ( # noqa: PLC0415 + MODEL_CONFIG_MAPPING, + ) + except Exception as exc: # noqa: BLE001 + logger.debug( + "bfclv4: cannot import MODEL_CONFIG_MAPPING: %s", exc + ) + return [] + + classes: List[type] = [] + seen_class_ids: set[int] = set() + for cfg in MODEL_CONFIG_MAPPING.values(): + cls = getattr(cfg, "model_handler", None) + if cls is None or not isinstance(cls, type): + continue + if id(cls) in seen_class_ids: + continue + seen_class_ids.add(id(cls)) + classes.append(cls) + return classes + + +class BFCLv4Instrumentor(BaseInstrumentor): + """An instrumentor for the BFCL v4 (``bfcl_eval``) framework.""" + + def __init__(self) -> None: + super().__init__() + if not hasattr(self, "_wrapped_query_methods"): + self._wrapped_query_methods: List[Tuple[type, str]] = [] + if not hasattr(self, "_wrapped_turn_methods"): + self._wrapped_turn_methods: List[Tuple[type, str]] = [] + if not hasattr(self, "_entry_wrapped"): + self._entry_wrapped = False + if not hasattr(self, "_inference_wrapped"): + self._inference_wrapped = False + if not hasattr(self, "_tool_wrapped"): + self._tool_wrapped = False + + def instrumentation_dependencies(self) -> Collection[str]: + return _instruments + + # ------------------------------------------------------------------ + # _instrument + + def _instrument(self, **kwargs: Any) -> None: # noqa: D401 + helper = GenAIHookHelper() + + # 1) ENTRY ----------------------------------------------------- + try: + wrap_function_wrapper( + module=_GENERATE_RESULTS_MODULE, + name=_GENERATE_RESULTS_NAME, + wrapper=GenerateResultsWrapper(helper), + ) + self._entry_wrapped = True + except Exception as exc: # noqa: BLE001 + logger.warning( + "bfclv4: failed to wrap %s.%s: %s", + _GENERATE_RESULTS_MODULE, + _GENERATE_RESULTS_NAME, + exc, + ) + + # 2) AGENT ----------------------------------------------------- + try: + wrap_function_wrapper( + module=_BASE_HANDLER_MODULE, + name=_BASE_HANDLER_NAME, + wrapper=BaseHandlerInferenceWrapper(helper), + ) + self._inference_wrapped = True + except Exception as exc: # noqa: BLE001 + logger.warning( + "bfclv4: failed to wrap %s.%s: %s", + _BASE_HANDLER_MODULE, + _BASE_HANDLER_NAME, + exc, + ) + + # 3) STEP + 4) turn maintenance -------------------------------- + self._instrument_handlers(helper) + + # 5) TOOL ------------------------------------------------------ + try: + wrap_function_wrapper( + module=_EXECUTE_TOOL_MODULE, + name=_EXECUTE_TOOL_NAME, + wrapper=ExecuteFuncCallWrapper(helper), + ) + self._tool_wrapped = True + except Exception as exc: # noqa: BLE001 + logger.warning( + "bfclv4: failed to wrap %s.%s: %s", + _EXECUTE_TOOL_MODULE, + _EXECUTE_TOOL_NAME, + exc, + ) + + def _instrument_handlers(self, helper: GenAIHookHelper) -> None: + # Reflectively wrap every concrete ``_query_FC`` / ``_query_prompting`` + # plus the turn-maintenance helpers; we de-duplicate by function id so + # subclasses that share an inherited implementation are wrapped only + # once. + seen_func_ids: set[int] = set() + + query_pairs = ( + ("_query_FC", "FC"), + ("_query_prompting", "prompting"), + ) + turn_pairs = ( + ("add_first_turn_message_FC", True), + ("add_first_turn_message_prompting", True), + ("_add_next_turn_user_message_FC", False), + ("_add_next_turn_user_message_prompting", False), + ) + + for cls in _iter_handler_classes(): + class_dict = getattr(cls, "__dict__", {}) + for method_name, mode in query_pairs: + method = class_dict.get(method_name) + if method is None or not callable(method): + continue + key = id(method) + if key in seen_func_ids: + continue + seen_func_ids.add(key) + try: + wrap_function_wrapper( + module=cls.__module__, + name=f"{cls.__name__}.{method_name}", + wrapper=QueryWrapper(helper, mode), + ) + self._wrapped_query_methods.append((cls, method_name)) + except Exception as exc: # noqa: BLE001 + logger.debug( + "bfclv4: failed to wrap %s.%s.%s: %s", + cls.__module__, + cls.__name__, + method_name, + exc, + ) + + for method_name, is_first in turn_pairs: + method = class_dict.get(method_name) + if method is None or not callable(method): + continue + key = id(method) + if key in seen_func_ids: + continue + seen_func_ids.add(key) + try: + wrap_function_wrapper( + module=cls.__module__, + name=f"{cls.__name__}.{method_name}", + wrapper=TurnBumpWrapper(reset=is_first), + ) + self._wrapped_turn_methods.append((cls, method_name)) + except Exception as exc: # noqa: BLE001 + logger.debug( + "bfclv4: failed to wrap %s.%s.%s: %s", + cls.__module__, + cls.__name__, + method_name, + exc, + ) + + # ------------------------------------------------------------------ + # _uninstrument + + def _uninstrument(self, **kwargs: Any) -> None: # noqa: D401 + if self._tool_wrapped: + try: + module = importlib.import_module(_EXECUTE_TOOL_MODULE) + unwrap(module, _EXECUTE_TOOL_NAME) + except Exception as exc: # noqa: BLE001 + logger.debug( + "bfclv4: failed to unwrap execute_multi_turn_func_call: %s", + exc, + ) + self._tool_wrapped = False + + for cls, method_name in self._wrapped_query_methods: + try: + unwrap(cls, method_name) + except Exception as exc: # noqa: BLE001 + logger.debug( + "bfclv4: failed to unwrap %s.%s: %s", + cls.__name__, + method_name, + exc, + ) + self._wrapped_query_methods = [] + + for cls, method_name in self._wrapped_turn_methods: + try: + unwrap(cls, method_name) + except Exception as exc: # noqa: BLE001 + logger.debug( + "bfclv4: failed to unwrap %s.%s: %s", + cls.__name__, + method_name, + exc, + ) + self._wrapped_turn_methods = [] + + if self._inference_wrapped: + try: + base_module = importlib.import_module(_BASE_HANDLER_MODULE) + unwrap(base_module.BaseHandler, "inference") + except Exception as exc: # noqa: BLE001 + logger.debug( + "bfclv4: failed to unwrap BaseHandler.inference: %s", exc + ) + self._inference_wrapped = False + + if self._entry_wrapped: + try: + module = importlib.import_module(_GENERATE_RESULTS_MODULE) + unwrap(module, _GENERATE_RESULTS_NAME) + except Exception as exc: # noqa: BLE001 + logger.debug( + "bfclv4: failed to unwrap generate_results: %s", exc + ) + self._entry_wrapped = False diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/__init__.py b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/__init__.py new file mode 100644 index 000000000..b0a6f4284 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/__init__.py @@ -0,0 +1,13 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/attributes.py b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/attributes.py new file mode 100644 index 000000000..774200aba --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/attributes.py @@ -0,0 +1,38 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Constant attribute keys used by the BFCL v4 instrumentation.""" + +from __future__ import annotations + +from typing import Final + +FRAMEWORK_NAME: Final = "bfclv4" + +# gen_ai.* attribute keys that are not exported by +# opentelemetry-semantic-conventions today. +GEN_AI_FRAMEWORK: Final = "gen_ai.framework" +GEN_AI_PROVIDER_NAME: Final = "gen_ai.provider.name" + +# BFCL-specific (vendor) attribute keys. +BFCL_TEST_CATEGORY: Final = "bfcl.test_category" +BFCL_NUM_THREADS: Final = "bfcl.num_threads" +BFCL_TEST_CASE_COUNT: Final = "bfcl.test_case_count" +BFCL_RUN_IDS: Final = "bfcl.run_ids" +BFCL_TEST_ENTRY_ID: Final = "bfcl.test_entry_id" +BFCL_TURN_IDX: Final = "bfcl.turn_idx" +BFCL_QUERY_MODE: Final = "bfcl.query_mode" +BFCL_OSS_BACKEND: Final = "bfcl.oss.backend" +BFCL_TOOL_DURATION_IS_ESTIMATED: Final = "bfcl.tool.duration_is_estimated" +BFCL_TOOL_INDEX: Final = "bfcl.tool.index" diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/provider.py b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/provider.py new file mode 100644 index 000000000..efa2c77dc --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/provider.py @@ -0,0 +1,71 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Map BFCL ``ModelStyle`` enum values to ``gen_ai.provider.name``.""" + +from __future__ import annotations + +import os +from typing import Any, Dict, Tuple + +from opentelemetry.instrumentation.bfclv4.internal.attributes import ( + BFCL_OSS_BACKEND, +) + +# The BFCL backend name (vllm / sglang / ...) is communicated from the ENTRY +# wrapper to the per-thread STEP/AGENT wrappers via this env var. The ENTRY +# wrapper writes to it before invoking the wrapped function and clears it in +# the ``finally`` clause. +OSS_BACKEND_ENV = "BFCL_BACKEND" + + +def infer_provider(handler: Any) -> Tuple[str, Dict[str, Any]]: + """Return ``(provider_name, extra_attributes)`` for a BFCL handler. + + Falls back to ``"unknown"`` if BFCL is not importable or if the handler + has no ``model_style`` attribute. + """ + + try: + from bfcl_eval.constants.enums import ( # noqa: PLC0415 + ModelStyle, + ) + except ImportError: + return "unknown", {} + + style = getattr(handler, "model_style", None) + if style is None: + return "unknown", {} + + if style is ModelStyle.OSSMODEL: + backend = (os.getenv(OSS_BACKEND_ENV) or "").lower() + if backend in ("vllm", "sglang"): + return backend, {BFCL_OSS_BACKEND: backend} + return "oss", {BFCL_OSS_BACKEND: "unknown"} + + mapping = { + ModelStyle.OPENAI_COMPLETIONS: "openai", + ModelStyle.OPENAI_RESPONSES: "openai", + ModelStyle.ANTHROPIC: "anthropic", + ModelStyle.GOOGLE: "gcp.gemini", + ModelStyle.MISTRAL: "mistral_ai", + ModelStyle.COHERE: "cohere", + ModelStyle.AMAZON: "aws.bedrock", + ModelStyle.FIREWORK_AI: "fireworks_ai", + ModelStyle.WRITER: "writer", + ModelStyle.NOVITA_AI: "novita", + ModelStyle.NEXUS: "nexusflow", + ModelStyle.GORILLA: "gorilla", + } + return mapping.get(style, "unknown"), {} diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/state.py b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/state.py new file mode 100644 index 000000000..ae4861035 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/state.py @@ -0,0 +1,93 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Per-thread ReAct state for the BFCL v4 instrumentation. + +We use ``contextvars.ContextVar`` so that each worker thread spawned by the +BFCL ``ThreadPoolExecutor`` gets its own copy. ``_ContextPropagatingExecutor`` +in :mod:`threading_propagation` makes sure ENTRY-time context is copied into +the worker thread; the BaseHandler.inference wrapper then initializes a fresh +state on top of that copy. +""" + +from __future__ import annotations + +import contextvars +from typing import Any, Dict, Optional + +_REACT_STATE: contextvars.ContextVar[Optional[Dict[str, Any]]] = ( + contextvars.ContextVar("bfclv4_react_state", default=None) +) + + +def init_state() -> contextvars.Token: + """Initialise per-AGENT state and return the reset token.""" + state: Dict[str, Any] = { + # ``turn_idx`` is incremented by the wrapper around + # ``_add_next_turn_user_message_*``; it stays ``0`` for single-turn + # tests. + "turn_idx": 0, + # ``fc_round`` is the ReAct round counter. We bump it on every STEP + # entry so the first STEP within a turn ends up with ``round=1``. + "fc_round": 0, + # Counter of executed tool calls within the current AGENT - useful for + # the TOOL span ``tool_call_id`` synthesis. + "tool_index": 0, + } + return _REACT_STATE.set(state) + + +def reset_state(token: contextvars.Token) -> None: + try: + _REACT_STATE.reset(token) + except (LookupError, ValueError): + # Token may have already been reset (e.g. nested error path). + pass + + +def get_state() -> Optional[Dict[str, Any]]: + return _REACT_STATE.get() + + +def bump_round() -> int: + state = _REACT_STATE.get() + if state is None: + return 1 + state["fc_round"] = state.get("fc_round", 0) + 1 + return state["fc_round"] + + +def reset_round_for_turn() -> None: + state = _REACT_STATE.get() + if state is None: + return + state["fc_round"] = 0 + + +def bump_turn() -> int: + state = _REACT_STATE.get() + if state is None: + return 0 + state["turn_idx"] = state.get("turn_idx", 0) + 1 + state["fc_round"] = 0 + return state["turn_idx"] + + +def next_tool_index() -> int: + state = _REACT_STATE.get() + if state is None: + return 0 + idx = state.get("tool_index", 0) + state["tool_index"] = idx + 1 + return idx diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/threading_propagation.py b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/threading_propagation.py new file mode 100644 index 000000000..d19c05799 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/threading_propagation.py @@ -0,0 +1,43 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Context-propagating ``ThreadPoolExecutor`` used by the ENTRY wrapper. + +``concurrent.futures.ThreadPoolExecutor`` does not automatically copy the +current ``contextvars`` context (which holds the OTel current span) into +worker threads. We subclass it and copy ``contextvars.copy_context()`` per +``submit`` so the AGENT span created inside the worker thread can attach as +a child of the ENTRY span. + +We only swap the ``ThreadPoolExecutor`` *name* in the +``bfcl_eval._llm_response_generation`` namespace; the global +``concurrent.futures.ThreadPoolExecutor`` is untouched. +""" + +from __future__ import annotations + +import contextvars +from concurrent.futures import ThreadPoolExecutor as _RealExecutor + + +class ContextPropagatingExecutor(_RealExecutor): + """``ThreadPoolExecutor`` that propagates the calling ``Context``. + + Only the ``submit`` method is overridden because BFCL only uses + ``submit`` (see ``_llm_response_generation.generate_results``). + """ + + def submit(self, fn, /, *args, **kwargs): # type: ignore[override] + ctx = contextvars.copy_context() + return super().submit(ctx.run, fn, *args, **kwargs) diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/wrappers.py b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/wrappers.py new file mode 100644 index 000000000..9683cb85b --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/wrappers.py @@ -0,0 +1,691 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Wrapper classes for the BFCL v4 instrumentation. + +Each wrapper follows the standard ``wrapt`` callable contract:: + + def __call__(self, wrapped, instance, args, kwargs): + ... + +All wrappers rely on :func:`get_extended_telemetry_handler` (LoongSuite +``util-genai``) to create the actual spans, so that ENTRY / AGENT / STEP / +TOOL spans get the canonical ``gen_ai.span.kind`` and operation-name values +that the LoongSuite semantic-validator expects. +""" + +from __future__ import annotations + +import logging +import os +import time +from typing import Any, Callable, Iterable, List, Optional + +from opentelemetry.instrumentation.bfclv4.internal.attributes import ( + BFCL_NUM_THREADS, + BFCL_OSS_BACKEND, + BFCL_QUERY_MODE, + BFCL_RUN_IDS, + BFCL_TEST_CASE_COUNT, + BFCL_TEST_CATEGORY, + BFCL_TEST_ENTRY_ID, + BFCL_TOOL_DURATION_IS_ESTIMATED, + BFCL_TOOL_INDEX, + BFCL_TURN_IDX, + FRAMEWORK_NAME, + GEN_AI_FRAMEWORK, + GEN_AI_PROVIDER_NAME, +) +from opentelemetry.instrumentation.bfclv4.internal.provider import ( + OSS_BACKEND_ENV, + infer_provider, +) +from opentelemetry.instrumentation.bfclv4.internal.state import ( + bump_round, + bump_turn, + init_state, + next_tool_index, + reset_state, +) +from opentelemetry.instrumentation.bfclv4.internal.threading_propagation import ( + ContextPropagatingExecutor, +) +from opentelemetry.instrumentation.bfclv4.utils import ( + GenAIHookHelper, + to_text_input, + to_text_output, + truncate_text, +) +from opentelemetry.util.genai.extended_handler import ( + get_extended_telemetry_handler, +) +from opentelemetry.util.genai.extended_types import ( + EntryInvocation, + ExecuteToolInvocation, + InvokeAgentInvocation, + ReactStepInvocation, +) + +logger = logging.getLogger(__name__) + + +# --------------------------------------------------------------------------- +# Helpers + + +def _safe_get(obj: Any, key: str, default: Any = None) -> Any: + if isinstance(obj, dict): + return obj.get(key, default) + return getattr(obj, key, default) + + +def _flatten_tokens(value: Any) -> Optional[int]: + """Sum a possibly nested ``int|float|list|list[list]`` BFCL token field.""" + if value is None: + return None + if isinstance(value, (int, float)): + return int(value) + if isinstance(value, Iterable): + total = 0 + any_seen = False + for item in value: + sub = _flatten_tokens(item) + if sub is not None: + total += sub + any_seen = True + if any_seen: + return total + return None + + +def _test_category_from_id(test_entry_id: Optional[str]) -> Optional[str]: + if not test_entry_id or "_" not in test_entry_id: + return None + return test_entry_id.rsplit("_", 1)[0] + + +def _join_test_category(value: Any) -> Optional[str]: + if value is None: + return None + if isinstance(value, str): + return value + if isinstance(value, (list, tuple, set)): + joined = ",".join(str(v) for v in value if v is not None) + return joined or None + return str(value) + + +# --------------------------------------------------------------------------- +# ENTRY wrapper + + +class GenerateResultsWrapper: + """Wraps ``bfcl_eval._llm_response_generation.generate_results``. + + Responsibilities: + + * Open the ENTRY span (``enter_ai_application_system``). + * Temporarily swap the ``ThreadPoolExecutor`` reference inside the BFCL + generation module to a context-propagating subclass so that AGENT spans + created in worker threads inherit the ENTRY span as parent. + * Publish ``args.backend`` to ``BFCL_BACKEND`` so that + :func:`infer_provider` can attribute OSS spans to vllm / sglang. + """ + + def __init__(self, helper: GenAIHookHelper) -> None: + self._helper = helper + + def __call__(self, wrapped: Callable, instance: Any, args, kwargs): # noqa: D401 + # ``generate_results(args, model_name, test_cases_total)`` + cli_args = args[0] if len(args) >= 1 else kwargs.get("args") + model_name = args[1] if len(args) >= 2 else kwargs.get("model_name") + test_cases_total = ( + args[2] if len(args) >= 3 else kwargs.get("test_cases_total") + ) + + try: + from bfcl_eval import ( # noqa: PLC0415 + _llm_response_generation as _bfcl_gen, + ) + except ImportError: + return wrapped(*args, **kwargs) + + original_executor = getattr(_bfcl_gen, "ThreadPoolExecutor", None) + if original_executor is not None: + _bfcl_gen.ThreadPoolExecutor = ContextPropagatingExecutor + + backend_value = ( + _safe_get(cli_args, "backend", None) if cli_args is not None else None + ) + previous_backend_env = os.environ.get(OSS_BACKEND_ENV) + if backend_value: + os.environ[OSS_BACKEND_ENV] = str(backend_value) + + session_id_default = None + if model_name is not None: + try: + session_id_default = f"{model_name}@{int(time.time())}" + except Exception: # noqa: BLE001 + session_id_default = None + session_id = ( + os.environ.get("BFCL_SESSION_ID") or session_id_default + ) + + entry_inv = EntryInvocation(session_id=session_id) + handler = get_extended_telemetry_handler() + + attributes = {GEN_AI_FRAMEWORK: FRAMEWORK_NAME} + category_value = _join_test_category( + _safe_get(cli_args, "test_category", None) + ) + if category_value: + attributes[BFCL_TEST_CATEGORY] = category_value + num_threads = _safe_get(cli_args, "num_threads", None) + if num_threads is not None: + try: + attributes[BFCL_NUM_THREADS] = int(num_threads) + except (TypeError, ValueError): + pass + if isinstance(test_cases_total, (list, tuple)): + attributes[BFCL_TEST_CASE_COUNT] = len(test_cases_total) + attributes[BFCL_RUN_IDS] = bool( + _safe_get(cli_args, "run_ids", False) + ) + + try: + with handler.entry(entry_inv) as inv: + if inv.span is not None and inv.span.is_recording(): + for key, value in attributes.items(): + try: + inv.span.set_attribute(key, value) + except Exception: # noqa: BLE001 + logger.debug( + "bfclv4 ENTRY set_attribute(%s) failed", + key, + exc_info=True, + ) + return wrapped(*args, **kwargs) + finally: + if original_executor is not None: + try: + _bfcl_gen.ThreadPoolExecutor = original_executor + except Exception: # noqa: BLE001 + logger.debug( + "bfclv4 ENTRY: failed to restore ThreadPoolExecutor", + exc_info=True, + ) + if backend_value: + if previous_backend_env is None: + os.environ.pop(OSS_BACKEND_ENV, None) + else: + os.environ[OSS_BACKEND_ENV] = previous_backend_env + + +# --------------------------------------------------------------------------- +# AGENT wrapper + + +_BFCL_INFERENCE_ERROR_PREFIX = "Error during inference:" + + +class BaseHandlerInferenceWrapper: + """Wraps ``BaseHandler.inference``. + + Creates the AGENT span (kind=AGENT, op=invoke_agent) and initialises the + per-thread ReAct state used by the STEP wrapper. + + BFCL's outer ``multi_threaded_inference`` catches every exception and + converts it into a ``"Error during inference: ..."`` string; we mirror + that behaviour by setting the AGENT span status to ERROR when the + returned ``result`` looks like an error string, instead of relying on + a re-raised exception. + """ + + def __init__(self, helper: GenAIHookHelper) -> None: + self._helper = helper + + def __call__(self, wrapped: Callable, instance: Any, args, kwargs): # noqa: D401 + # ``inference(self, test_entry, include_input_log, exclude_state_log)`` + test_entry = args[0] if args else kwargs.get("test_entry") + if not isinstance(test_entry, dict): + return wrapped(*args, **kwargs) + + provider, extra_attrs = infer_provider(instance) + request_model = getattr(instance, "model_name", None) + test_entry_id = test_entry.get("id") + category = _test_category_from_id(test_entry_id) + involved_classes = test_entry.get("involved_classes") or [] + agent_description = ( + ", ".join(str(c) for c in involved_classes) + if isinstance(involved_classes, (list, tuple)) + else None + ) + + invocation = InvokeAgentInvocation( + provider=provider or "unknown", + request_model=request_model, + agent_id=test_entry_id, + agent_name=category or "bfcl_agent", + agent_description=agent_description or None, + conversation_id=test_entry_id, + ) + + token = init_state() + handler = get_extended_telemetry_handler() + try: + with handler.invoke_agent(invocation) as inv: + if inv.span is not None and inv.span.is_recording(): + inv.span.set_attribute(GEN_AI_FRAMEWORK, FRAMEWORK_NAME) + if provider: + inv.span.set_attribute(GEN_AI_PROVIDER_NAME, provider) + if test_entry_id is not None: + inv.span.set_attribute( + BFCL_TEST_ENTRY_ID, test_entry_id + ) + if category is not None: + inv.span.set_attribute(BFCL_TEST_CATEGORY, category) + for key, value in extra_attrs.items(): + if value is not None: + inv.span.set_attribute(key, value) + + # Capture inputs for the AGENT (gated by content-capture mode). + question = test_entry.get("question") + if question is not None: + inv.input_messages = to_text_input( + "user", truncate_text(_safe_str(question)) + ) + + # Run the original inference call. + try: + result = wrapped(*args, **kwargs) + except Exception as exc: + # The CM will mark the span as failed; we leave it to + # the handler/CM to call ``fail_invoke_agent``. + raise exc + + # Detect BFCL's own captured error path (no exception raised + # but the returned result is the error string). + result_payload = ( + result[0] if isinstance(result, tuple) and result else None + ) + metadata_payload = ( + result[1] + if isinstance(result, tuple) and len(result) >= 2 + else None + ) + + if ( + isinstance(result_payload, str) + and result_payload.startswith(_BFCL_INFERENCE_ERROR_PREFIX) + and inv.span is not None + and inv.span.is_recording() + ): + try: + from opentelemetry.trace import Status, StatusCode + + inv.span.set_status( + Status(StatusCode.ERROR, result_payload[:200]) + ) + except Exception: # noqa: BLE001 + logger.debug( + "bfclv4 AGENT: failed to set ERROR status", + exc_info=True, + ) + + if isinstance(metadata_payload, dict): + input_tokens = _flatten_tokens( + metadata_payload.get("input_token_count") + ) + output_tokens = _flatten_tokens( + metadata_payload.get("output_token_count") + ) + if input_tokens is not None: + inv.input_tokens = input_tokens + if output_tokens is not None: + inv.output_tokens = output_tokens + + if result_payload is not None: + inv.output_messages = to_text_output( + "assistant", + truncate_text(_safe_str(result_payload)), + ) + + return result + finally: + reset_state(token) + + +def _safe_str(value: Any) -> str: + try: + if isinstance(value, str): + return value + import json + + return json.dumps(value, ensure_ascii=False, default=str) + except Exception: # noqa: BLE001 + try: + return str(value) + except Exception: # noqa: BLE001 + return "" + + +# --------------------------------------------------------------------------- +# STEP wrapper + + +class QueryWrapper: + """Wraps ``._query_FC`` / ``_query_prompting``. + + Creates a ReAct STEP span, attaches token usage by re-calling the + handler's matching ``_parse_query_response_*`` (which is documented as + side-effect-free). + """ + + def __init__(self, helper: GenAIHookHelper, mode: str) -> None: + self._helper = helper + self._mode = mode # "FC" or "prompting" + + def __call__(self, wrapped: Callable, instance: Any, args, kwargs): # noqa: D401 + round_idx = bump_round() + provider, extra_attrs = infer_provider(instance) + + invocation = ReactStepInvocation(round=round_idx) + handler_obj = get_extended_telemetry_handler() + with handler_obj.react_step(invocation) as step_inv: + span = step_inv.span + if span is not None and span.is_recording(): + span.set_attribute(GEN_AI_FRAMEWORK, FRAMEWORK_NAME) + span.set_attribute(BFCL_QUERY_MODE, self._mode) + if provider: + span.set_attribute(GEN_AI_PROVIDER_NAME, provider) + model_name = getattr(instance, "model_name", None) + if model_name: + span.set_attribute( + "gen_ai.request.model", str(model_name) + ) + from opentelemetry.instrumentation.bfclv4.internal.state import ( + get_state, + ) + + state = get_state() + if state is not None: + span.set_attribute(BFCL_TURN_IDX, state.get("turn_idx", 0)) + for key, value in extra_attrs.items(): + if value is not None: + span.set_attribute(key, value) + + try: + api_response, query_latency = wrapped(*args, **kwargs) + except Exception: + # Let the context-manager mark the span as failed; the BFCL + # outer try/except will turn this into an "Error during + # inference: ..." result string at the AGENT layer. + raise + + # Post-call attribute enrichment - use try/except so that any + # vendor-side parsing surprise never breaks BFCL itself. + try: + if span is not None and span.is_recording(): + parser_name = ( + "_parse_query_response_FC" + if self._mode == "FC" + else "_parse_query_response_prompting" + ) + parser = getattr(instance, parser_name, None) + if parser is not None: + parsed = parser(api_response) + if isinstance(parsed, dict): + input_token = parsed.get("input_token") + output_token = parsed.get("output_token") + if isinstance(input_token, (int, float)): + span.set_attribute( + "gen_ai.usage.input_tokens", + int(input_token), + ) + if isinstance(output_token, (int, float)): + span.set_attribute( + "gen_ai.usage.output_tokens", + int(output_token), + ) + if isinstance(input_token, (int, float)) and isinstance( + output_token, (int, float) + ): + span.set_attribute( + "gen_ai.usage.total_tokens", + int(input_token) + int(output_token), + ) + model_resp = parsed.get("model_responses") + step_inv.finish_reason = _infer_finish_reason( + model_resp + ) + if isinstance(query_latency, (int, float)): + try: + span.set_attribute( + "gen_ai.response.time_to_first_token", + int(float(query_latency) * 1e9), + ) + except Exception: # noqa: BLE001 + pass + except Exception: # noqa: BLE001 + logger.debug( + "bfclv4 STEP: post-call enrichment failed", exc_info=True + ) + + return api_response, query_latency + + +def _infer_finish_reason(model_responses: Any) -> str: + """Best-effort heuristic for ``gen_ai.react.finish_reason``.""" + if model_responses is None: + return "unknown" + if isinstance(model_responses, list): + if len(model_responses) == 0: + return "empty_response" + if len(model_responses) == 1 and not model_responses[0]: + return "empty_response" + return "tool_calls" + if isinstance(model_responses, str): + # Prompting models often return decoded strings even when there are + # no tool calls - treat as "stop" so downstream callers know there is + # no further work to do. + return "stop" + return "continue" + + +# --------------------------------------------------------------------------- +# turn_idx maintenance wrappers (no spans) + + +class TurnBumpWrapper: + """Wraps ``.add_first_turn_message_*`` and + ``._add_next_turn_user_message_*`` to keep ``bfcl.turn_idx`` in + sync. No spans are created here. + """ + + def __init__(self, *, reset: bool) -> None: + self._reset = reset + + def __call__(self, wrapped: Callable, instance: Any, args, kwargs): # noqa: D401 + try: + if self._reset: + # ``add_first_turn_message_*`` runs once at the very start of + # multi-turn / single-turn inference. We only want to reset + # to ``turn_idx=0`` here. + from opentelemetry.instrumentation.bfclv4.internal.state import ( + get_state, + ) + + state = get_state() + if state is not None: + state["turn_idx"] = 0 + state["fc_round"] = 0 + else: + bump_turn() + except Exception: # noqa: BLE001 + logger.debug( + "bfclv4: turn_idx maintenance failed", exc_info=True + ) + return wrapped(*args, **kwargs) + + +# --------------------------------------------------------------------------- +# TOOL wrapper + + +class ExecuteFuncCallWrapper: + """Wraps + ``bfcl_eval.eval_checker.multi_turn_eval.multi_turn_utils.execute_multi_turn_func_call``. + + BFCL evaluates a list of function-call strings in a single Python call; + we surface each one as its own TOOL span by post-processing the wrapped + result. Per-call latency is approximated by averaging the total elapsed + time across the batch (``bfcl.tool.duration_is_estimated=true``). + """ + + def __init__(self, helper: GenAIHookHelper) -> None: + self._helper = helper + + def __call__(self, wrapped: Callable, instance: Any, args, kwargs): # noqa: D401 + # ``execute_multi_turn_func_call(func_call_list, initial_config, + # involved_classes, model_name, + # test_entry_id, long_context=False, + # is_evaL_run=False)`` + func_call_list = ( + args[0] if args else kwargs.get("func_call_list", []) + ) + model_name = ( + args[3] + if len(args) >= 4 + else kwargs.get("model_name") + ) + test_entry_id = ( + args[4] + if len(args) >= 5 + else kwargs.get("test_entry_id") + ) + + if not isinstance(func_call_list, list) or not func_call_list: + return wrapped(*args, **kwargs) + + t0 = time.perf_counter() + try: + result = wrapped(*args, **kwargs) + finally: + elapsed = max(time.perf_counter() - t0, 0.0) + + execution_results: List[str] = [] + if isinstance(result, tuple) and result: + payload = result[0] + if isinstance(payload, list): + execution_results = list(payload) + + per_call_seconds = ( + elapsed / len(func_call_list) if func_call_list else 0.0 + ) + + handler_obj = get_extended_telemetry_handler() + for index, func_call in enumerate(func_call_list): + tool_name = _extract_tool_name(func_call) + arguments = _extract_tool_arguments(func_call) + execution_result = ( + execution_results[index] + if index < len(execution_results) + else None + ) + + tool_inv = ExecuteToolInvocation( + tool_name=tool_name, + tool_call_id=_synth_tool_call_id( + test_entry_id, model_name, index + ), + tool_type="function", + tool_call_arguments=arguments, + tool_call_result=execution_result, + ) + + try: + with handler_obj.execute_tool(tool_inv) as inv: + span = inv.span + if span is not None and span.is_recording(): + span.set_attribute(GEN_AI_FRAMEWORK, FRAMEWORK_NAME) + span.set_attribute(BFCL_TOOL_INDEX, index) + span.set_attribute( + BFCL_TOOL_DURATION_IS_ESTIMATED, True + ) + if test_entry_id is not None: + span.set_attribute( + BFCL_TEST_ENTRY_ID, str(test_entry_id) + ) + if isinstance(execution_result, str) and execution_result.startswith( + "Error during execution:" + ): + try: + from opentelemetry.trace import ( + Status, + StatusCode, + ) + + span.set_status( + Status( + StatusCode.ERROR, + execution_result[:200], + ) + ) + except Exception: # noqa: BLE001 + pass + # Approximate latency by sleeping the budgeted slice + # would distort BFCL execution; we instead rely on + # span start/end (currently both wall-clock-now). + # The ``bfcl.tool.duration_is_estimated`` attribute + # signals the limitation to consumers. + _ = per_call_seconds # unused but documented + # Bump a per-AGENT counter for downstream debugging. + next_tool_index() + except Exception: # noqa: BLE001 + logger.debug( + "bfclv4 TOOL: span emission failed for %s", + tool_name, + exc_info=True, + ) + + return result + + +def _extract_tool_name(func_call: Any) -> str: + if not isinstance(func_call, str) or "(" not in func_call: + return "unknown" + head = func_call.split("(", 1)[0] + # ``head`` may be ``module.method`` or ``instance.method`` - keep the + # last segment which is the actual callable. + return head.split(".")[-1] or "unknown" + + +def _extract_tool_arguments(func_call: Any) -> Optional[str]: + if not isinstance(func_call, str): + return None + if "(" not in func_call or not func_call.endswith(")"): + return func_call + args_part = func_call[func_call.index("(") + 1 : -1] + return args_part if args_part else None + + +def _synth_tool_call_id( + test_entry_id: Optional[Any], model_name: Optional[Any], index: int +) -> str: + parts = [ + str(test_entry_id) if test_entry_id is not None else "no_id", + str(model_name) if model_name is not None else "no_model", + str(index), + ] + return "-".join(parts) diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/package.py b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/package.py new file mode 100644 index 000000000..66e9fa6e1 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/package.py @@ -0,0 +1,17 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +_instruments = ("bfcl-eval >= 4.0.0",) + +_supports_metrics = False diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/utils.py b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/utils.py new file mode 100644 index 000000000..c63bbc62b --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/utils.py @@ -0,0 +1,144 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Helpers for the BFCL v4 instrumentation. + +The :class:`GenAIHookHelper` mirrors the helper used by the LoongSuite CrewAI +instrumentation: it gates ``gen_ai.input.messages`` / +``gen_ai.output.messages`` / ``gen_ai.system_instructions`` on the standard +LoongSuite content-capture environment knobs so that prompt content is not +exported by default. +""" + +from __future__ import annotations + +import dataclasses +import logging +from typing import Any, Dict, List, Optional + +from opentelemetry.semconv._incubating.attributes import gen_ai_attributes +from opentelemetry.trace import Span +from opentelemetry.util.genai.types import ( + ContentCapturingMode, + InputMessage, + MessagePart, + OutputMessage, + Text, +) +from opentelemetry.util.genai.utils import ( + gen_ai_json_dumps, + get_content_capturing_mode, + is_experimental_mode, +) + +logger = logging.getLogger(__name__) + + +class GenAIHookHelper: + """Conditionally write prompt / completion content to the span.""" + + def __init__(self, capture_content: bool = True) -> None: + self.capture_content = capture_content + + def on_completion( + self, + span: Span, + inputs: Optional[List[InputMessage]] = None, + outputs: Optional[List[OutputMessage]] = None, + system_instructions: Optional[List[MessagePart]] = None, + attributes: Optional[Dict[str, Any]] = None, + ) -> None: + if not span.is_recording(): + return + + if self.capture_content and is_experimental_mode(): + mode = get_content_capturing_mode() + should_capture_span = mode in ( + ContentCapturingMode.SPAN_ONLY, + ContentCapturingMode.SPAN_AND_EVENT, + ) + + if should_capture_span: + if inputs: + span.set_attribute( + gen_ai_attributes.GEN_AI_INPUT_MESSAGES, + gen_ai_json_dumps( + [dataclasses.asdict(i) for i in inputs] + ), + ) + if outputs: + span.set_attribute( + gen_ai_attributes.GEN_AI_OUTPUT_MESSAGES, + gen_ai_json_dumps( + [dataclasses.asdict(o) for o in outputs] + ), + ) + if system_instructions: + span.set_attribute( + gen_ai_attributes.GEN_AI_SYSTEM_INSTRUCTIONS, + gen_ai_json_dumps( + [dataclasses.asdict(s) for s in system_instructions] + ), + ) + + if attributes: + for key, value in attributes.items(): + if value is None: + continue + try: + span.set_attribute(key, value) + except Exception: # noqa: BLE001 + logger.debug( + "bfclv4: failed to set attribute %s", key, exc_info=True + ) + + +def to_text_input(role: str, content: Any) -> List[InputMessage]: + if content in (None, "", [], {}): + return [] + text = content if isinstance(content, str) else _to_safe_str(content) + return [InputMessage(role=role, parts=[Text(content=text)])] + + +def to_text_output( + role: str, content: Any, finish_reason: str = "stop" +) -> List[OutputMessage]: + if content in (None, "", [], {}): + return [] + text = content if isinstance(content, str) else _to_safe_str(content) + return [ + OutputMessage( + role=role, parts=[Text(content=text)], finish_reason=finish_reason + ) + ] + + +def _to_safe_str(value: Any) -> str: + """Best-effort JSON serialisation, falling back to ``str()``. + + The wrapper code never wants a serialisation failure to break a span. + """ + try: + return gen_ai_json_dumps(value) + except Exception: # noqa: BLE001 + try: + return str(value) + except Exception: # noqa: BLE001 + return "" + + +def truncate_text(value: str, limit: int = 4096) -> str: + if len(value) <= limit: + return value + return value[:limit] + f"..." diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/version.py b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/version.py new file mode 100644 index 000000000..4effd145c --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/version.py @@ -0,0 +1,15 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +__version__ = "0.1.0.dev" diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/tests/__init__.py b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/tests/test_instrumentor.py b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/tests/test_instrumentor.py new file mode 100644 index 000000000..41446ee3b --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/tests/test_instrumentor.py @@ -0,0 +1,52 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Smoke tests for ``BFCLv4Instrumentor``. + +These tests do not require ``bfcl-eval`` to be installed; they only verify +that importing the package and calling ``instrument()`` / ``uninstrument()`` +works (and degrades gracefully when ``bfcl-eval`` is missing). +""" + +import importlib + +import pytest + + +def test_import_instrumentor_package(): + module = importlib.import_module("opentelemetry.instrumentation.bfclv4") + assert hasattr(module, "BFCLv4Instrumentor") + + +def test_instrumentation_dependencies_listed(): + from opentelemetry.instrumentation.bfclv4 import BFCLv4Instrumentor + from opentelemetry.instrumentation.bfclv4.package import _instruments + + instr = BFCLv4Instrumentor() + assert tuple(instr.instrumentation_dependencies()) == _instruments + + +def test_instrument_uninstrument_no_bfcl_no_raise(): + """When ``bfcl-eval`` is missing, every wrap call logs and continues. + + The instrumentor must not raise from ``instrument()`` / + ``uninstrument()`` even if the target framework cannot be imported. + """ + + pytest.importorskip("opentelemetry.util.genai.extended_handler") + from opentelemetry.instrumentation.bfclv4 import BFCLv4Instrumentor + + instr = BFCLv4Instrumentor() + instr.instrument() + instr.uninstrument() diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/tests/test_internals.py b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/tests/test_internals.py new file mode 100644 index 000000000..21bbf6348 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/tests/test_internals.py @@ -0,0 +1,113 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Unit tests for the framework-agnostic helpers.""" + +import contextvars + +import pytest + + +def test_state_lifecycle(): + from opentelemetry.instrumentation.bfclv4.internal.state import ( + bump_round, + bump_turn, + get_state, + init_state, + next_tool_index, + reset_state, + ) + + token = init_state() + try: + state = get_state() + assert state == {"turn_idx": 0, "fc_round": 0, "tool_index": 0} + + assert bump_round() == 1 + assert bump_round() == 2 + assert bump_turn() == 1 + # bump_turn resets fc_round + state = get_state() + assert state["turn_idx"] == 1 + assert state["fc_round"] == 0 + assert next_tool_index() == 0 + assert next_tool_index() == 1 + finally: + reset_state(token) + + # After reset the state should be gone (None default). + assert get_state() is None + + +def test_context_propagating_executor_carries_contextvars(): + from opentelemetry.instrumentation.bfclv4.internal.threading_propagation import ( + ContextPropagatingExecutor, + ) + + cv: contextvars.ContextVar[str] = contextvars.ContextVar( + "bfclv4_test_cv", default="default" + ) + cv.set("from_main_thread") + + def _read(): + return cv.get() + + with ContextPropagatingExecutor(max_workers=2) as pool: + future = pool.submit(_read) + assert future.result() == "from_main_thread" + + +def test_extract_tool_name_and_arguments(): + from opentelemetry.instrumentation.bfclv4.internal.wrappers import ( + _extract_tool_arguments, + _extract_tool_name, + ) + + assert _extract_tool_name("calc.add(1, 2)") == "add" + assert _extract_tool_name("list_files()") == "list_files" + assert _extract_tool_name("not a call") == "unknown" + assert _extract_tool_arguments("foo(a=1, b=2)") == "a=1, b=2" + assert _extract_tool_arguments("foo()") is None + + +def test_infer_finish_reason_heuristic(): + from opentelemetry.instrumentation.bfclv4.internal.wrappers import ( + _infer_finish_reason, + ) + + assert _infer_finish_reason([]) == "empty_response" + assert _infer_finish_reason([[]]) == "empty_response" + assert _infer_finish_reason([{"name": "x"}]) == "tool_calls" + assert _infer_finish_reason("plain string") == "stop" + assert _infer_finish_reason(None) == "unknown" + + +def test_provider_mapping_without_bfcl(monkeypatch): + from opentelemetry.instrumentation.bfclv4.internal.provider import ( + infer_provider, + ) + + pytest.importorskip( + "opentelemetry.util.genai.extended_types", + ) + + class _Dummy: + model_style = None + + name, extras = infer_provider(_Dummy()) + # If bfcl-eval is not installed, ``ModelStyle`` import fails and we get + # ``unknown``; otherwise we still get ``unknown`` because ``model_style`` + # is None. + assert name == "unknown" + assert extras == {} From 7b144aa62a413ec5119c6c56c7b6d317c01aebfb Mon Sep 17 00:00:00 2001 From: root Date: Wed, 6 May 2026 20:05:03 +0800 Subject: [PATCH 2/8] feat: support bfclv4 (cherry picked from commit 3d08e03d0a8dd1c3e0566964b985a69c76908460) Co-authored-by: 123liuziming <32130965+123liuziming@users.noreply.github.com> --- .../instrumentation/bfclv4/__init__.py | 91 ++++++++++++------- .../bfclv4/internal/wrappers.py | 40 ++------ .../instrumentation/bfclv4/version.py | 2 +- 3 files changed, 67 insertions(+), 66 deletions(-) diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/__init__.py b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/__init__.py index 34a5a9b10..6a7729940 100644 --- a/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/__init__.py +++ b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/__init__.py @@ -108,6 +108,8 @@ def __init__(self) -> None: self._inference_wrapped = False if not hasattr(self, "_tool_wrapped"): self._tool_wrapped = False + if not hasattr(self, "_tool_targets"): + self._tool_targets: List[Tuple[str, str]] = [] def instrumentation_dependencies(self) -> Collection[str]: return _instruments @@ -121,9 +123,9 @@ def _instrument(self, **kwargs: Any) -> None: # noqa: D401 # 1) ENTRY ----------------------------------------------------- try: wrap_function_wrapper( - module=_GENERATE_RESULTS_MODULE, - name=_GENERATE_RESULTS_NAME, - wrapper=GenerateResultsWrapper(helper), + _GENERATE_RESULTS_MODULE, + _GENERATE_RESULTS_NAME, + GenerateResultsWrapper(helper), ) self._entry_wrapped = True except Exception as exc: # noqa: BLE001 @@ -137,9 +139,9 @@ def _instrument(self, **kwargs: Any) -> None: # noqa: D401 # 2) AGENT ----------------------------------------------------- try: wrap_function_wrapper( - module=_BASE_HANDLER_MODULE, - name=_BASE_HANDLER_NAME, - wrapper=BaseHandlerInferenceWrapper(helper), + _BASE_HANDLER_MODULE, + _BASE_HANDLER_NAME, + BaseHandlerInferenceWrapper(helper), ) self._inference_wrapped = True except Exception as exc: # noqa: BLE001 @@ -154,20 +156,39 @@ def _instrument(self, **kwargs: Any) -> None: # noqa: D401 self._instrument_handlers(helper) # 5) TOOL ------------------------------------------------------ - try: - wrap_function_wrapper( - module=_EXECUTE_TOOL_MODULE, - name=_EXECUTE_TOOL_NAME, - wrapper=ExecuteFuncCallWrapper(helper), - ) - self._tool_wrapped = True - except Exception as exc: # noqa: BLE001 - logger.warning( - "bfclv4: failed to wrap %s.%s: %s", - _EXECUTE_TOOL_MODULE, + # ``execute_multi_turn_func_call`` is re-exported via ``from ... import`` + # in several BFCL modules, so wrapping just the source module misses + # the call sites that use the local binding. We wrap each known + # re-export site as well to guarantee the TOOL span is always emitted. + tool_targets = [ + (_EXECUTE_TOOL_MODULE, _EXECUTE_TOOL_NAME), + ( + "bfcl_eval.model_handler.base_handler", _EXECUTE_TOOL_NAME, - exc, - ) + ), + ( + "bfcl_eval.eval_checker.multi_turn_eval.multi_turn_checker", + _EXECUTE_TOOL_NAME, + ), + ] + wrapper_instance = ExecuteFuncCallWrapper(helper) + self._tool_targets = [] + for module_name, attr_name in tool_targets: + try: + wrap_function_wrapper( + module_name, + attr_name, + wrapper_instance, + ) + self._tool_targets.append((module_name, attr_name)) + except Exception as exc: # noqa: BLE001 + logger.debug( + "bfclv4: failed to wrap %s.%s: %s", + module_name, + attr_name, + exc, + ) + self._tool_wrapped = bool(self._tool_targets) def _instrument_handlers(self, helper: GenAIHookHelper) -> None: # Reflectively wrap every concrete ``_query_FC`` / ``_query_prompting`` @@ -199,9 +220,9 @@ def _instrument_handlers(self, helper: GenAIHookHelper) -> None: seen_func_ids.add(key) try: wrap_function_wrapper( - module=cls.__module__, - name=f"{cls.__name__}.{method_name}", - wrapper=QueryWrapper(helper, mode), + cls.__module__, + f"{cls.__name__}.{method_name}", + QueryWrapper(helper, mode), ) self._wrapped_query_methods.append((cls, method_name)) except Exception as exc: # noqa: BLE001 @@ -223,9 +244,9 @@ def _instrument_handlers(self, helper: GenAIHookHelper) -> None: seen_func_ids.add(key) try: wrap_function_wrapper( - module=cls.__module__, - name=f"{cls.__name__}.{method_name}", - wrapper=TurnBumpWrapper(reset=is_first), + cls.__module__, + f"{cls.__name__}.{method_name}", + TurnBumpWrapper(reset=is_first), ) self._wrapped_turn_methods.append((cls, method_name)) except Exception as exc: # noqa: BLE001 @@ -242,14 +263,18 @@ def _instrument_handlers(self, helper: GenAIHookHelper) -> None: def _uninstrument(self, **kwargs: Any) -> None: # noqa: D401 if self._tool_wrapped: - try: - module = importlib.import_module(_EXECUTE_TOOL_MODULE) - unwrap(module, _EXECUTE_TOOL_NAME) - except Exception as exc: # noqa: BLE001 - logger.debug( - "bfclv4: failed to unwrap execute_multi_turn_func_call: %s", - exc, - ) + for module_name, attr_name in getattr(self, "_tool_targets", []): + try: + module = importlib.import_module(module_name) + unwrap(module, attr_name) + except Exception as exc: # noqa: BLE001 + logger.debug( + "bfclv4: failed to unwrap %s.%s: %s", + module_name, + attr_name, + exc, + ) + self._tool_targets = [] self._tool_wrapped = False for cls, method_name in self._wrapped_query_methods: diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/wrappers.py b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/wrappers.py index 9683cb85b..31106b9e4 100644 --- a/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/wrappers.py +++ b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/wrappers.py @@ -435,40 +435,16 @@ def __call__(self, wrapped: Callable, instance: Any, args, kwargs): # noqa: D40 # Post-call attribute enrichment - use try/except so that any # vendor-side parsing surprise never breaks BFCL itself. + # + # IMPORTANT: We must NOT re-call ``_parse_query_response_*`` here, + # because for streaming providers (e.g. Qwen DashScope) the + # ``api_response`` is a single-pass generator that the parser + # consumes; calling it twice leaves BFCL's own subsequent call to + # the parser with an exhausted iterator, which crashes inference + # with ``UnboundLocalError: chunk``. Token usage will instead be + # recovered later from the AGENT-level metadata payload. try: if span is not None and span.is_recording(): - parser_name = ( - "_parse_query_response_FC" - if self._mode == "FC" - else "_parse_query_response_prompting" - ) - parser = getattr(instance, parser_name, None) - if parser is not None: - parsed = parser(api_response) - if isinstance(parsed, dict): - input_token = parsed.get("input_token") - output_token = parsed.get("output_token") - if isinstance(input_token, (int, float)): - span.set_attribute( - "gen_ai.usage.input_tokens", - int(input_token), - ) - if isinstance(output_token, (int, float)): - span.set_attribute( - "gen_ai.usage.output_tokens", - int(output_token), - ) - if isinstance(input_token, (int, float)) and isinstance( - output_token, (int, float) - ): - span.set_attribute( - "gen_ai.usage.total_tokens", - int(input_token) + int(output_token), - ) - model_resp = parsed.get("model_responses") - step_inv.finish_reason = _infer_finish_reason( - model_resp - ) if isinstance(query_latency, (int, float)): try: span.set_attribute( diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/version.py b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/version.py index 4effd145c..3263662eb 100644 --- a/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/version.py +++ b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/version.py @@ -12,4 +12,4 @@ # See the License for the specific language governing permissions and # limitations under the License. -__version__ = "0.1.0.dev" +__version__ = "0.1.3.dev0" From b371234385925fdc173a621f39000cab887123bc Mon Sep 17 00:00:00 2001 From: musi Date: Wed, 6 May 2026 22:08:27 +0800 Subject: [PATCH 3/8] fix(bfclv4): keep STEP/LLM context detach in LIFO order for streaming responses When ``_query_FC`` / ``_query_prompting`` returns a streaming wrapper (e.g. ``openai-v2`` ``ChatStreamWrapper``), the LLM span and its OTel context attach are kept alive until the stream is consumed by BFCL's ``_parse_query_response_*`` after the STEP context manager has already exited. Non-LIFO context detach then leaves the prior LLM span as the "current" span, which causes subsequent STEP and TOOL spans to be parented under the previous STEP rather than under AGENT. Force-consume the streaming response inside the STEP context and replace it with a plain iterator over the cached chunks so that ``stop_llm`` (which detaches LLM context) runs in LIFO order before STEP detaches. (cherry picked from commit 5cbd0490d441bb3ff08dc1172ecdd822aeb285bc) Co-authored-by: 123liuziming <32130965+123liuziming@users.noreply.github.com> --- .../bfclv4/internal/wrappers.py | 27 +++++++++++++++++++ 1 file changed, 27 insertions(+) diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/wrappers.py b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/wrappers.py index 31106b9e4..cb9bd3f34 100644 --- a/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/wrappers.py +++ b/instrumentation-loongsuite/loongsuite-instrumentation-bfclv4/src/opentelemetry/instrumentation/bfclv4/internal/wrappers.py @@ -433,6 +433,33 @@ def __call__(self, wrapped: Callable, instance: Any, args, kwargs): # noqa: D40 # inference: ..." result string at the AGENT layer. raise + # When the underlying handler returns a streaming wrapper + # (e.g. ``ChatStreamWrapper`` from openai-v2), the LLM span and + # its OTel context attach are kept alive until the stream is + # consumed by BFCL's ``_parse_query_response_*`` *outside* of + # this STEP context manager. That breaks the LIFO ordering of + # context attach/detach, leaving the LLM span as the "current" + # span after the STEP CM exits, which causes the next STEP and + # any TOOL spans to be parented to the previous STEP rather + # than to the AGENT. + # + # To preserve LIFO ordering, force-consume the stream here + # (inside the STEP context) and replace it with a plain + # iterator over the cached chunks. This makes ``stop_llm`` + # (which detaches the LLM context) run *before* STEP detaches. + if api_response is not None and hasattr( + api_response, "__next__" + ) and not isinstance(api_response, (str, bytes)): + try: + chunks = list(api_response) + api_response = iter(chunks) + except Exception: # noqa: BLE001 + logger.debug( + "bfclv4 STEP: failed to materialise streaming " + "response; LLM/STEP nesting may be incorrect", + exc_info=True, + ) + # Post-call attribute enrichment - use try/except so that any # vendor-side parsing surprise never breaks BFCL itself. # From 74ab41dca70b3ff643743a920c6bdba2bdd36ea4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=89=A7=E6=80=9D?= Date: Wed, 6 May 2026 22:18:26 +0800 Subject: [PATCH 4/8] feat: support widesearch Change-Id: I84e87248e0eec61fa8f7fa68dbe85e5181ddede8 (cherry picked from commit 2071e80b63655ee9b8385bc4e72f4f5cdfd6d135) Co-authored-by: 123liuziming <32130965+123liuziming@users.noreply.github.com> --- .../README.md | 17 + .../pyproject.toml | 57 ++ .../instrumentation/widesearch/__init__.py | 164 ++++ .../instrumentation/widesearch/package.py | 2 + .../instrumentation/widesearch/patch.py | 338 +++++++++ .../instrumentation/widesearch/utils.py | 155 ++++ .../instrumentation/widesearch/version.py | 1 + .../tests/__init__.py | 0 .../tests/conftest.py | 386 ++++++++++ .../tests/test_widesearch.py | 715 ++++++++++++++++++ 10 files changed, 1835 insertions(+) create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-widesearch/README.md create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-widesearch/pyproject.toml create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-widesearch/src/opentelemetry/instrumentation/widesearch/__init__.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-widesearch/src/opentelemetry/instrumentation/widesearch/package.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-widesearch/src/opentelemetry/instrumentation/widesearch/patch.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-widesearch/src/opentelemetry/instrumentation/widesearch/utils.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-widesearch/src/opentelemetry/instrumentation/widesearch/version.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-widesearch/tests/__init__.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-widesearch/tests/conftest.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-widesearch/tests/test_widesearch.py diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/README.md b/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/README.md new file mode 100644 index 000000000..4b4aac443 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/README.md @@ -0,0 +1,17 @@ +# LoongSuite WideSearch Instrumentation + +OpenTelemetry instrumentation for the [WideSearch](https://github.com/ByteDance-Seed/WideSearch) multi-agent search framework. + +## Installation + +```bash +pip install loongsuite-instrumentation-widesearch +``` + +## Usage + +```python +from opentelemetry.instrumentation.widesearch import WideSearchInstrumentor + +WideSearchInstrumentor().instrument() +``` diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/pyproject.toml b/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/pyproject.toml new file mode 100644 index 000000000..9a819d25a --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/pyproject.toml @@ -0,0 +1,57 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "loongsuite-instrumentation-widesearch" +dynamic = ["version"] +description = "LoongSuite WideSearch Instrumentation" +readme = "README.md" +license = "Apache-2.0" +requires-python = ">=3.11" +authors = [ + { name = "LoongSuite Python Agent Authors", email = "caishipeng.csp@alibaba-inc.com" }, + { name = "OpenTelemetry Authors", email = "cncf-opentelemetry-contributors@lists.cncf.io" }, +] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Developers", + "License :: OSI Approved :: Apache Software License", + "Programming Language :: Python", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", +] +dependencies = [ + "opentelemetry-api ~= 1.37", + "opentelemetry-instrumentation >= 0.58b0", + "opentelemetry-semantic-conventions >= 0.58b0", + "opentelemetry-util-genai", + "wrapt >= 1.17.3, < 2.0.0", +] + +[project.optional-dependencies] +instruments = [ + "widesearch >= 0.1.0", +] +test = [ + "pytest ~= 8.0", + "pytest-cov ~= 4.1.0", +] + +[project.entry-points.opentelemetry_instrumentor] +widesearch = "opentelemetry.instrumentation.widesearch:WideSearchInstrumentor" + +[project.urls] +Homepage = "https://github.com/alibaba/loongsuite-python-agent/tree/main/instrumentation-loongsuite/loongsuite-instrumentation-widesearch" +Repository = "https://github.com/alibaba/loongsuite-python-agent" + +[tool.hatch.version] +path = "src/opentelemetry/instrumentation/widesearch/version.py" + +[tool.hatch.build.targets.sdist] +include = ["/src", "/tests"] + +[tool.hatch.build.targets.wheel] +packages = ["src/opentelemetry"] diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/src/opentelemetry/instrumentation/widesearch/__init__.py b/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/src/opentelemetry/instrumentation/widesearch/__init__.py new file mode 100644 index 000000000..9c441d18f --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/src/opentelemetry/instrumentation/widesearch/__init__.py @@ -0,0 +1,164 @@ +""" +WideSearch instrumentation supporting `widesearch >= 0.1.0`. + +Usage +----- +.. code:: python + + from opentelemetry.instrumentation.widesearch import WideSearchInstrumentor + + WideSearchInstrumentor().instrument() + +API +--- +""" + +from __future__ import annotations + +import logging +from typing import Any, Collection + +from wrapt import wrap_function_wrapper + +from opentelemetry.instrumentation.instrumentor import BaseInstrumentor +from opentelemetry.instrumentation.utils import unwrap +from opentelemetry.instrumentation.widesearch.package import _instruments +from opentelemetry.instrumentation.widesearch.patch import ( + wrap_create_sub_agents_factory, + wrap_invoke_tool_call, + wrap_run_single_query, + wrap_runner_run, + wrap_runner_step, +) +from opentelemetry.instrumentation.widesearch.version import __version__ +from opentelemetry.util.genai.extended_handler import ExtendedTelemetryHandler + +logger = logging.getLogger(__name__) + +_RUN_MODULE = "src.agent.run" +_MULTI_AGENT_MODULE = "src.agent.multi_agent_tools" + +__all__ = ["WideSearchInstrumentor", "__version__"] + + +class WideSearchInstrumentor(BaseInstrumentor): + """OpenTelemetry instrumentor for WideSearch framework. + + Instruments the following components: + - run_single_query(): ENTRY span + - Runner.run(): AGENT span (async generator) + - Runner._step(): STEP span + - Runner._invoke_tool_call(): TOOL spans + - create_sub_agents_wrap(): TASK span + """ + + def __init__(self): + super().__init__() + self._handler = None + + def instrumentation_dependencies(self) -> Collection[str]: + return _instruments + + def _instrument(self, **kwargs: Any) -> None: + tracer_provider = kwargs.get("tracer_provider") + meter_provider = kwargs.get("meter_provider") + logger_provider = kwargs.get("logger_provider") + + self._handler = ExtendedTelemetryHandler( + tracer_provider=tracer_provider, + meter_provider=meter_provider, + logger_provider=logger_provider, + ) + + # H1: ENTRY span + try: + wrap_function_wrapper( + module=_RUN_MODULE, + name="run_single_query", + wrapper=lambda w, i, a, k: wrap_run_single_query( + w, i, a, k, handler=self._handler + ), + ) + logger.debug("Instrumented run_single_query") + except Exception as e: + logger.warning(f"Failed to instrument run_single_query: {e}") + + # H2: AGENT span + try: + wrap_function_wrapper( + module=_RUN_MODULE, + name="Runner.run", + wrapper=lambda w, i, a, k: wrap_runner_run( + w, i, a, k, handler=self._handler + ), + ) + logger.debug("Instrumented Runner.run") + except Exception as e: + logger.warning(f"Failed to instrument Runner.run: {e}") + + # H3: STEP span + try: + wrap_function_wrapper( + module=_RUN_MODULE, + name="Runner._step", + wrapper=lambda w, i, a, k: wrap_runner_step( + w, i, a, k, handler=self._handler + ), + ) + logger.debug("Instrumented Runner._step") + except Exception as e: + logger.warning(f"Failed to instrument Runner._step: {e}") + + # H4: TOOL spans + try: + wrap_function_wrapper( + module=_RUN_MODULE, + name="Runner._invoke_tool_call", + wrapper=lambda w, i, a, k: wrap_invoke_tool_call( + w, i, a, k, handler=self._handler + ), + ) + logger.debug("Instrumented Runner._invoke_tool_call") + except Exception as e: + logger.warning( + f"Failed to instrument Runner._invoke_tool_call: {e}" + ) + + # H5: TASK span (wrap factory) + try: + wrap_function_wrapper( + module=_MULTI_AGENT_MODULE, + name="create_sub_agents_wrap", + wrapper=lambda w, i, a, k: wrap_create_sub_agents_factory( + w, i, a, k, handler=self._handler + ), + ) + logger.debug("Instrumented create_sub_agents_wrap") + except Exception as e: + logger.warning( + f"Failed to instrument create_sub_agents_wrap: {e}" + ) + + def _uninstrument(self, **kwargs: Any) -> None: + try: + import src.agent.run # noqa: PLC0415 + + unwrap(src.agent.run, "run_single_query") + unwrap(src.agent.run.Runner, "run") + unwrap(src.agent.run.Runner, "_step") + unwrap(src.agent.run.Runner, "_invoke_tool_call") + logger.debug("Uninstrumented src.agent.run") + except Exception as e: + logger.warning(f"Failed to uninstrument src.agent.run: {e}") + + try: + import src.agent.multi_agent_tools # noqa: PLC0415 + + unwrap(src.agent.multi_agent_tools, "create_sub_agents_wrap") + logger.debug("Uninstrumented src.agent.multi_agent_tools") + except Exception as e: + logger.warning( + f"Failed to uninstrument src.agent.multi_agent_tools: {e}" + ) + + self._handler = None diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/src/opentelemetry/instrumentation/widesearch/package.py b/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/src/opentelemetry/instrumentation/widesearch/package.py new file mode 100644 index 000000000..bd0572292 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/src/opentelemetry/instrumentation/widesearch/package.py @@ -0,0 +1,2 @@ +_instruments = ("widesearch >= 0.1.0",) +_supports_metrics = False diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/src/opentelemetry/instrumentation/widesearch/patch.py b/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/src/opentelemetry/instrumentation/widesearch/patch.py new file mode 100644 index 000000000..32ac6287b --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/src/opentelemetry/instrumentation/widesearch/patch.py @@ -0,0 +1,338 @@ +"""Patch functions for WideSearch instrumentation. + +Wraps key WideSearch methods to generate OpenTelemetry spans: +- run_single_query -> ENTRY span +- Runner.run -> AGENT span (async generator) +- Runner._step -> STEP span +- Runner._invoke_tool_call -> TOOL spans (one per tool_call) +- create_sub_agents_wrap -> TASK span (on returned closure) +""" + +from __future__ import annotations + +import asyncio +import json +import logging +from contextvars import ContextVar + +from opentelemetry.trace import SpanKind, StatusCode +from opentelemetry.trace.status import Status +from opentelemetry.util.genai.extended_handler import ExtendedTelemetryHandler +from opentelemetry.util.genai.extended_types import ReactStepInvocation +from opentelemetry.util.genai.types import Error + +from .utils import ( + _create_agent_invocation, + _create_entry_invocation, + _create_tool_invocation, + _extract_output_messages, + _step_to_output_messages, +) + +logger = logging.getLogger(__name__) + +_step_counter: ContextVar[int] = ContextVar("ws_step_counter", default=0) +_in_run_single_query: ContextVar[bool] = ContextVar("ws_in_rsq", default=False) + + +async def wrap_run_single_query( + wrapped, instance, args, kwargs, *, handler: ExtendedTelemetryHandler +): + """H1: ENTRY span for run_single_query.""" + if _in_run_single_query.get(): + return await wrapped(*args, **kwargs) + token = _in_run_single_query.set(True) + + query = args[0] if args else kwargs.get("query", "") + try: + invocation = _create_entry_invocation(query) + except Exception as e: + logger.debug(f"Failed to create entry invocation: {e}") + _in_run_single_query.reset(token) + return await wrapped(*args, **kwargs) + + handler.start_entry(invocation) + + try: + result = await wrapped(*args, **kwargs) + invocation.output_messages = _extract_output_messages(result) + handler.stop_entry(invocation) + return result + except Exception as e: + handler.fail_entry(invocation, Error(message=str(e), type=type(e))) + raise + finally: + _in_run_single_query.reset(token) + + +async def wrap_runner_run( + wrapped, instance, args, kwargs, *, handler: ExtendedTelemetryHandler +): + """H2: AGENT span for Runner.run (async generator).""" + starting_agent = args[0] if args else kwargs.get("starting_agent") + user_input = args[1] if len(args) > 1 else kwargs.get("user_input", "") + + try: + invocation = _create_agent_invocation(starting_agent, user_input) + except Exception as e: + logger.debug(f"Failed to create agent invocation: {e}") + async for step in wrapped(*args, **kwargs): + yield step + return + + counter_token = _step_counter.set(0) + handler.start_invoke_agent(invocation) + + try: + last_step = None + async for step in wrapped(*args, **kwargs): + last_step = step + yield step + + if last_step: + invocation.output_messages = _step_to_output_messages(last_step) + handler.stop_invoke_agent(invocation) + except GeneratorExit as e: + handler.fail_invoke_agent( + invocation, Error(message="GeneratorExit", type=GeneratorExit) + ) + raise + except Exception as e: + handler.fail_invoke_agent( + invocation, Error(message=str(e), type=type(e)) + ) + raise + finally: + _step_counter.reset(counter_token) + + +async def wrap_runner_step( + wrapped, instance, args, kwargs, *, handler: ExtendedTelemetryHandler +): + """H3: STEP span for Runner._step.""" + step_num = _step_counter.get() + 1 + _step_counter.set(step_num) + + invocation = ReactStepInvocation(round=step_num) + invocation.attributes["gen_ai.framework"] = "widesearch" + + try: + handler.start_react_step(invocation) + except Exception as e: + logger.debug(f"Failed to start react step: {e}") + return await wrapped(*args, **kwargs) + + try: + result = await wrapped(*args, **kwargs) + + from src.agent.memory import ActionStep, ActionStepError, StepStatus + + if isinstance(result, ActionStepError): + invocation.finish_reason = "error" + handler.fail_react_step( + invocation, + Error(message=result.message, type=type(result)), + ) + else: + if result.step_status == StepStatus.FINISHED: + invocation.finish_reason = "finished" + elif result.error_marker is not None: + invocation.finish_reason = "error" + else: + invocation.finish_reason = "continue" + handler.stop_react_step(invocation) + + return result + except Exception as e: + invocation.finish_reason = "error" + handler.fail_react_step( + invocation, Error(message=str(e), type=type(e)) + ) + raise + + +async def wrap_invoke_tool_call( + wrapped, instance, args, kwargs, *, handler: ExtendedTelemetryHandler +): + """H4: TOOL span for each tool_call inside Runner._invoke_tool_call.""" + agent = args[0] if args else kwargs.get("agent") + model_response = args[1] if len(args) > 1 else kwargs.get("model_response") + + if not model_response.outputs: + return await wrapped(*args, **kwargs) + + resp = model_response.outputs[0] + if not resp.tool_calls: + return await wrapped(*args, **kwargs) + + from src.agent.schema import ErrorMarker, ToolCallResult + + async def _call_with_span(tool_call): + try: + invocation = _create_tool_invocation(tool_call, agent) + except Exception as e: + logger.debug(f"Failed to create tool invocation: {e}") + return await _call_original(tool_call, agent) + + handler.start_execute_tool(invocation) + + tool_name = tool_call.tool_name + tool = agent.get_tool_by_name(tool_name) + if tool is None: + invocation.tool_call_result = f"Tool {tool_name} not found" + handler.fail_execute_tool( + invocation, + Error( + message=f"Tool {tool_name} not found", + type=ValueError, + ), + ) + return ToolCallResult( + tool_call_id=tool_call.tool_call_id, + error_marker=ErrorMarker(message=f"Tool {tool_name} not found"), + ) + + arguments = tool_call.arguments + if isinstance(arguments, str): + try: + arguments = json.loads(arguments) + except json.JSONDecodeError: + arguments = {} + + try: + response = await tool(**arguments) + except Exception as e: + invocation.tool_call_result = str(e) + handler.fail_execute_tool( + invocation, Error(message=str(e), type=type(e)) + ) + return ToolCallResult( + tool_call_id=tool_call.tool_call_id, + error_marker=ErrorMarker(message=str(e)), + ) + + error_marker = ( + ErrorMarker(message=response.error) if response.error else None + ) + system_error_marker = ( + ErrorMarker(message=response.system_error) + if response.system_error + else None + ) + + result_content = response.data + invocation.tool_call_result = ( + str(result_content) if result_content else None + ) + + if error_marker or system_error_marker: + msg = (error_marker or system_error_marker)["message"] + handler.fail_execute_tool( + invocation, Error(message=msg, type=RuntimeError) + ) + else: + handler.stop_execute_tool(invocation) + + return ToolCallResult( + tool_call_id=tool_call.tool_call_id, + content=result_content, + error_marker=error_marker, + system_error_marker=system_error_marker, + extra=response.extra if response.extra else {}, + ) + + async def _call_original(tool_call, agent): + """Fallback: execute tool without span.""" + tool_name = tool_call.tool_name + tool = agent.get_tool_by_name(tool_name) + if tool is None: + return ToolCallResult( + tool_call_id=tool_call.tool_call_id, + error_marker=ErrorMarker(message=f"Tool {tool_name} not found"), + ) + arguments = tool_call.arguments + if isinstance(arguments, str): + try: + arguments = json.loads(arguments) + except json.JSONDecodeError: + arguments = {} + try: + response = await tool(**arguments) + except Exception as e: + return ToolCallResult( + tool_call_id=tool_call.tool_call_id, + error_marker=ErrorMarker(message=str(e)), + ) + return ToolCallResult( + tool_call_id=tool_call.tool_call_id, + content=response.data, + error_marker=( + ErrorMarker(message=response.error) if response.error else None + ), + system_error_marker=( + ErrorMarker(message=response.system_error) + if response.system_error + else None + ), + extra=response.extra if response.extra else {}, + ) + + tasks = [_call_with_span(tc) for tc in resp.tool_calls] + results = await asyncio.gather(*tasks) + return [r for r in results if r is not None] + + +def wrap_create_sub_agents_factory( + wrapped, instance, args, kwargs, *, handler: ExtendedTelemetryHandler +): + """H5: TASK span wrapping the closure returned by create_sub_agents_wrap.""" + original_closure = wrapped(*args, **kwargs) + + async def closure_with_task_span(sub_agents): + tracer = handler._tracer + span_name = "run_task create_sub_agents" + + with tracer.start_as_current_span( + name=span_name, + kind=SpanKind.INTERNAL, + ) as span: + span.set_attribute("gen_ai.span.kind", "TASK") + span.set_attribute("gen_ai.operation.name", "run_task") + span.set_attribute("gen_ai.framework", "widesearch") + + try: + safe_input = json.dumps( + [ + { + "index": sa.get("index"), + "prompt": sa.get("prompt", "")[:200], + } + for sa in sub_agents + ], + ensure_ascii=False, + ) + span.set_attribute("input.value", safe_input) + except Exception: + pass + + try: + result = await original_closure(sub_agents) + + if result and hasattr(result, "data") and result.data: + output_str = ( + result.data + if isinstance(result.data, str) + else json.dumps(result.data, ensure_ascii=False) + ) + if len(output_str) > 4096: + output_str = output_str[:4096] + "...(truncated)" + span.set_attribute("output.value", output_str) + + span.set_status(Status(StatusCode.OK)) + return result + except Exception as e: + span.record_exception(e) + span.set_status(Status(StatusCode.ERROR, str(e))) + raise + + return closure_with_task_span diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/src/opentelemetry/instrumentation/widesearch/utils.py b/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/src/opentelemetry/instrumentation/widesearch/utils.py new file mode 100644 index 000000000..0a8f751f7 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/src/opentelemetry/instrumentation/widesearch/utils.py @@ -0,0 +1,155 @@ +"""Utility functions for WideSearch instrumentation.""" + +from __future__ import annotations + +import json +import logging +from typing import Any, List, Optional + +from opentelemetry.util.genai.extended_types import ( + EntryInvocation, + ExecuteToolInvocation, + InvokeAgentInvocation, + ReactStepInvocation, +) +from opentelemetry.util.genai.types import ( + FunctionToolDefinition, + InputMessage, + OutputMessage, + Text, +) + +logger = logging.getLogger(__name__) + + +_FRAMEWORK = "widesearch" + + +def _create_entry_invocation(query: str) -> EntryInvocation: + invocation = EntryInvocation() + invocation.input_messages = [ + InputMessage(role="user", parts=[Text(content=query)]) + ] + invocation.attributes["gen_ai.framework"] = _FRAMEWORK + return invocation + + +def _create_agent_invocation( + agent: Any, user_input: str +) -> InvokeAgentInvocation: + agent_name = getattr(agent, "name", None) or "widesearch-agent" + + request_model = None + model_config_name = getattr(agent, "model_config_name", None) + if model_config_name: + try: + from src.utils.config import model_config + + request_model = model_config.get(model_config_name, {}).get( + "model_name" + ) + except Exception: + pass + request_model = request_model or model_config_name + + instructions = getattr(agent, "instructions", None) or "" + + invocation = InvokeAgentInvocation( + provider="widesearch", + agent_name=agent_name, + agent_description=instructions[:200] if instructions else "", + request_model=request_model, + input_messages=[ + InputMessage(role="user", parts=[Text(content=user_input)]) + ], + ) + invocation.attributes["gen_ai.framework"] = _FRAMEWORK + + if instructions: + invocation.system_instruction = [Text(content=instructions)] + + tools_desc = getattr(agent, "tools_desc", None) + if tools_desc: + invocation.tool_definitions = _convert_tools_desc(tools_desc) + + return invocation + + +def _create_tool_invocation( + tool_call: Any, agent: Any +) -> ExecuteToolInvocation: + args = tool_call.arguments + if isinstance(args, str): + try: + args = json.loads(args) + except (json.JSONDecodeError, ValueError): + args = {"raw": args} + + description = None + if hasattr(agent, "tools_desc"): + for td in agent.tools_desc: + func = td.get("function", {}) + if func.get("name") == tool_call.tool_name: + description = func.get("description") + break + + invocation = ExecuteToolInvocation( + tool_name=tool_call.tool_name, + tool_call_id=getattr(tool_call, "tool_call_id", None), + tool_call_arguments=args, + tool_description=description, + tool_type="function", + ) + invocation.attributes["gen_ai.framework"] = _FRAMEWORK + return invocation + + +def _extract_output_messages(messages: Any) -> List[OutputMessage]: + """Extract output messages from run_single_query return value.""" + if not messages: + return [] + last_msg = messages[-1] + content = "" + if isinstance(last_msg, dict): + c = last_msg.get("content", {}) + if isinstance(c, dict): + content = c.get("content", "") + elif isinstance(c, str): + content = c + return [ + OutputMessage( + role="assistant", + parts=[Text(content=content)], + finish_reason="stop", + ) + ] + + +def _step_to_output_messages(step: Any) -> List[OutputMessage]: + """Extract output messages from an ActionStep.""" + content = getattr(step, "content", None) or "" + return [ + OutputMessage( + role="assistant", + parts=[Text(content=content)], + finish_reason="stop", + ) + ] + + +def _convert_tools_desc( + tools_desc: List[dict], +) -> Optional[List[FunctionToolDefinition]]: + """Convert WideSearch tools_desc to FunctionToolDefinition list.""" + result = [] + for td in tools_desc: + if td.get("type") == "function": + func = td.get("function", {}) + result.append( + FunctionToolDefinition( + name=func.get("name", ""), + description=func.get("description"), + parameters=func.get("parameters"), + ) + ) + return result if result else None diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/src/opentelemetry/instrumentation/widesearch/version.py b/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/src/opentelemetry/instrumentation/widesearch/version.py new file mode 100644 index 000000000..26056b5d8 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/src/opentelemetry/instrumentation/widesearch/version.py @@ -0,0 +1 @@ +__version__ = "0.5.0.dev" diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/tests/__init__.py b/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/tests/conftest.py b/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/tests/conftest.py new file mode 100644 index 000000000..fa827987c --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/tests/conftest.py @@ -0,0 +1,386 @@ +"""Test configuration for WideSearch instrumentation tests. + +Injects lightweight stub modules for `src.agent.*` into sys.modules +so that wrap_function_wrapper can find them without installing WideSearch. +""" + +from __future__ import annotations + +import os +import sys +import types +from dataclasses import dataclass, field +from enum import Enum +from typing import Any, Callable, List, Literal + +import pytest + +# --------------------------------------------------------------------------- +# Stub modules for WideSearch (src.agent.*) +# --------------------------------------------------------------------------- + + +class StepStatus(str, Enum): + USER = "USER" + FINISHED = "FINISHED" + CONTINUE = "CONTINUE" + ERROR = "ERROR" + + +@dataclass +class ActionStepError: + message: str + source: Literal["llm"] = "llm" + + +@dataclass +class ToolCall: + tool_name: str + arguments: Any + tool_call_id: str + + +@dataclass +class ErrorMarker: + message: str + + def __getitem__(self, key): + if key == "message": + return self.message + raise KeyError(key) + + +@dataclass +class ToolCallResult: + tool_call_id: str + content: str | None = None + error_marker: Any = None + system_error_marker: Any = None + extra: dict = field(default_factory=dict) + + +@dataclass +class LLMOutputItem: + role: str = "assistant" + content: str | None = None + reasoning_content: str | None = None + signature: str | None = None + tool_calls: list = field(default_factory=list) + + +@dataclass +class ModelResponse: + outputs: list = field(default_factory=list) + session_id: str | None = None + error_marker: Any = None + + +@dataclass +class ActionStep: + step_status: StepStatus = StepStatus.CONTINUE + content: str | None = None + reasoning_content: str | None = None + signature: str | None = None + tool_calls: list = field(default_factory=list) + tool_call_results: list = field(default_factory=list) + error_marker: Any = None + + +@dataclass +class UserInputStep: + user_input: str + step_status: StepStatus = StepStatus.USER + + +@dataclass +class MemoryTurn: + steps: list = field(default_factory=list) + + @property + def step_number(self): + return sum(1 for s in self.steps if isinstance(s, ActionStep)) + + def is_finished(self) -> bool: + if not self.steps: + return False + return self.steps[-1].step_status == StepStatus.FINISHED + + +@dataclass +class MemoryAgent: + system_instructions: str | None = None + turns: list = field(default_factory=list) + + def insert_user_input(self, user_input: str): + turn = MemoryTurn() + turn.steps.append(UserInputStep(user_input=user_input)) + self.turns.append(turn) + return turn + + def insert_action_step(self, action_step): + last_turn = self.turns[-1] + last_turn.steps.append(action_step) + return last_turn + + def to_message(self, **kwargs): + return [] + + +@dataclass +class InternalResponse: + data: Any = None + error: str | None = None + system_error: str | None = None + extra: dict | None = None + + +@dataclass +class Agent: + name: str = "test-agent" + instructions: str | None = "You are a helpful agent." + tools: dict = field(default_factory=dict) + tools_desc: list = field(default_factory=list) + model_config_name: str = "gpt-4o" + + def get_tool_by_name(self, tool_name: str): + return self.tools.get(tool_name) + + +DEFAULT_MAX_STEPS = 50 +DEFAULT_MAX_ERROR_COUNT = 3 + + +class Runner: + _step_override = None # Set to a callable to override _step behavior + + @classmethod + async def run( + cls, + starting_agent, + user_input: str, + memory=None, + *, + max_steps: int = DEFAULT_MAX_STEPS, + llm_error_strategy: str = "retry", + ): + if memory is None: + memory = MemoryAgent( + system_instructions=starting_agent.instructions + ) + last_turn = memory.insert_user_input(user_input) + step_result = await cls._step(agent=starting_agent, memory=memory) + if not isinstance(step_result, ActionStepError): + yield step_result + + @classmethod + async def _step(cls, *, agent, memory) -> ActionStep | ActionStepError: + if cls._step_override is not None: + return await cls._step_override(agent=agent, memory=memory) + return ActionStep(step_status=StepStatus.FINISHED, content="Done") + + @classmethod + async def _invoke_tool_call( + cls, agent, model_response + ) -> list: + return [] + + +async def run_single_query( + query: str, + agent_name: str = "", + model_config_name: str = "", + tools: dict = None, + tools_desc: list = None, + system_prompt: str = "", +): + agent = Agent( + name=agent_name, + tools=tools or {}, + tools_desc=tools_desc or [], + model_config_name=model_config_name, + ) + memory = MemoryAgent(system_instructions=system_prompt) + + # Mirrors real implementation: calls Runner.run as async generator + async for step in Runner.run(agent, query, memory): + pass + + last_content = "final answer" + if memory.turns: + last_turn = memory.turns[-1] + for s in reversed(last_turn.steps): + if isinstance(s, ActionStep) and s.content: + last_content = s.content + break + + return [ + {"role": "user", "content": query}, + {"role": "assistant", "content": {"content": last_content}}, + ] + + +def _default_tools(): + return {} + + +def get_system_prompt(language="zh"): + return "You are a helpful assistant." + + +def create_sub_agents_wrap( + agent_name, model_config_name, tools, tools_desc, system_prompt +): + async def create_sub_agents(sub_agents: list) -> InternalResponse: + import json + + results = [] + for sa in sub_agents: + results.append( + {"index": sa.get("index"), "prompt": sa.get("prompt", ""), "response": "sub result"} + ) + return InternalResponse( + data=json.dumps(results, ensure_ascii=False) + ) + + return create_sub_agents + + +def _inject_stub_modules(): + """Inject stub modules into sys.modules so that wrapt can resolve them.""" + # Create module hierarchy: src -> src.agent -> src.agent.run, etc. + src_mod = types.ModuleType("src") + src_agent_mod = types.ModuleType("src.agent") + src_agent_run_mod = types.ModuleType("src.agent.run") + src_agent_multi_agent_tools_mod = types.ModuleType("src.agent.multi_agent_tools") + src_agent_memory_mod = types.ModuleType("src.agent.memory") + src_agent_schema_mod = types.ModuleType("src.agent.schema") + src_agent_tools_mod = types.ModuleType("src.agent.tools") + src_agent_prompt_mod = types.ModuleType("src.agent.prompt") + src_utils_mod = types.ModuleType("src.utils") + src_utils_config_mod = types.ModuleType("src.utils.config") + + # Populate src.agent.run + src_agent_run_mod.Runner = Runner + src_agent_run_mod.run_single_query = run_single_query + src_agent_run_mod.run_turn = None + src_agent_run_mod.extract_messages_from_memory = None + + # Populate src.agent.multi_agent_tools + src_agent_multi_agent_tools_mod.create_sub_agents_wrap = create_sub_agents_wrap + + # Populate src.agent.memory + src_agent_memory_mod.ActionStep = ActionStep + src_agent_memory_mod.ActionStepError = ActionStepError + src_agent_memory_mod.MemoryAgent = MemoryAgent + src_agent_memory_mod.StepStatus = StepStatus + src_agent_memory_mod.UserInputStep = UserInputStep + + # Populate src.agent.schema + src_agent_schema_mod.ToolCall = ToolCall + src_agent_schema_mod.ToolCallResult = ToolCallResult + src_agent_schema_mod.ModelResponse = ModelResponse + src_agent_schema_mod.ErrorMarker = ErrorMarker + src_agent_schema_mod.LLMOutputItem = LLMOutputItem + + # Populate src.agent.tools + src_agent_tools_mod.InternalResponse = InternalResponse + src_agent_tools_mod._default_tools = {} + + # Populate src.agent.prompt + src_agent_prompt_mod.get_system_prompt = get_system_prompt + + # Populate src.agent.agent + src_agent_agent_mod = types.ModuleType("src.agent.agent") + src_agent_agent_mod.Agent = Agent + src_agent_agent_mod.DEFAULT_MAX_STEPS = DEFAULT_MAX_STEPS + src_agent_agent_mod.DEFAULT_MAX_ERROR_COUNT = DEFAULT_MAX_ERROR_COUNT + + # Populate src.utils.config + src_utils_config_mod.model_config = { + "gpt-4o": {"model_name": "gpt-4o-2024-05-13"}, + } + + # Wire up parent references + src_mod.agent = src_agent_mod + src_mod.utils = src_utils_mod + src_agent_mod.run = src_agent_run_mod + src_agent_mod.multi_agent_tools = src_agent_multi_agent_tools_mod + src_agent_mod.memory = src_agent_memory_mod + src_agent_mod.schema = src_agent_schema_mod + src_agent_mod.tools = src_agent_tools_mod + src_agent_mod.prompt = src_agent_prompt_mod + src_agent_mod.agent = src_agent_agent_mod + + # Register in sys.modules + sys.modules["src"] = src_mod + sys.modules["src.agent"] = src_agent_mod + sys.modules["src.agent.run"] = src_agent_run_mod + sys.modules["src.agent.multi_agent_tools"] = src_agent_multi_agent_tools_mod + sys.modules["src.agent.memory"] = src_agent_memory_mod + sys.modules["src.agent.schema"] = src_agent_schema_mod + sys.modules["src.agent.tools"] = src_agent_tools_mod + sys.modules["src.agent.prompt"] = src_agent_prompt_mod + sys.modules["src.agent.agent"] = src_agent_agent_mod + sys.modules["src.utils"] = src_utils_mod + sys.modules["src.utils.config"] = src_utils_config_mod + + +# Inject stubs before any test imports the instrumentation module +_inject_stub_modules() + + +# --------------------------------------------------------------------------- +# OTel test fixtures +# --------------------------------------------------------------------------- + + +def pytest_configure(config: pytest.Config): + os.environ["OTEL_SEMCONV_STABILITY_OPT_IN"] = "gen_ai_latest_experimental" + os.environ["OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT"] = "span_only" + + +from opentelemetry.instrumentation.widesearch import WideSearchInstrumentor +from opentelemetry.sdk.trace import TracerProvider +from opentelemetry.sdk.trace.export import SimpleSpanProcessor +from opentelemetry.sdk.trace.export.in_memory_span_exporter import ( + InMemorySpanExporter, +) +from opentelemetry.sdk.metrics import MeterProvider +from opentelemetry.sdk.metrics.export import InMemoryMetricReader + + +@pytest.fixture(scope="function", name="span_exporter") +def fixture_span_exporter(): + exporter = InMemorySpanExporter() + yield exporter + + +@pytest.fixture(scope="function", name="metric_reader") +def fixture_metric_reader(): + reader = InMemoryMetricReader() + yield reader + + +@pytest.fixture(scope="function", name="tracer_provider") +def fixture_tracer_provider(span_exporter): + provider = TracerProvider() + provider.add_span_processor(SimpleSpanProcessor(span_exporter)) + return provider + + +@pytest.fixture(scope="function", name="meter_provider") +def fixture_meter_provider(metric_reader): + meter_provider = MeterProvider(metric_readers=[metric_reader]) + return meter_provider + + +@pytest.fixture(scope="function") +def instrument(tracer_provider, meter_provider): + instrumentor = WideSearchInstrumentor() + instrumentor.instrument( + tracer_provider=tracer_provider, + meter_provider=meter_provider, + skip_dep_check=True, + ) + yield instrumentor + instrumentor.uninstrument() diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/tests/test_widesearch.py b/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/tests/test_widesearch.py new file mode 100644 index 000000000..7ddc04a2a --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-widesearch/tests/test_widesearch.py @@ -0,0 +1,715 @@ +"""Tests for WideSearch instrumentation. + +Covers: +- Instrumentor lifecycle (instrument/uninstrument idempotency) +- 5 span types: ENTRY, AGENT, STEP, TOOL, TASK +- Parent-child relationships +- Key attributes +- Error paths +""" + +from __future__ import annotations + +import asyncio +import json +import sys +from dataclasses import field +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest + +from opentelemetry.trace import StatusCode + +from .conftest import ( + ActionStep, + ActionStepError, + Agent, + ErrorMarker, + InternalResponse, + LLMOutputItem, + MemoryAgent, + ModelResponse, + Runner, + StepStatus, + ToolCall, + ToolCallResult, +) + + +def _run_async(coro): + """Helper to run async coroutines in tests.""" + loop = asyncio.new_event_loop() + try: + return loop.run_until_complete(coro) + finally: + loop.close() + + +def _run_async_gen(async_gen): + """Helper to consume an async generator.""" + async def _consume(): + results = [] + async for item in async_gen: + results.append(item) + return results + loop = asyncio.new_event_loop() + try: + return loop.run_until_complete(_consume()) + finally: + loop.close() + + +# --------------------------------------------------------------------------- +# Instrumentor Lifecycle Tests +# --------------------------------------------------------------------------- + + +class TestInstrumentorLifecycle: + def test_instrument_and_uninstrument(self, tracer_provider, meter_provider): + from opentelemetry.instrumentation.widesearch import WideSearchInstrumentor + + instrumentor = WideSearchInstrumentor() + instrumentor.instrument( + tracer_provider=tracer_provider, + meter_provider=meter_provider, + skip_dep_check=True, + ) + assert instrumentor._handler is not None + instrumentor.uninstrument() + assert instrumentor._handler is None + + def test_double_instrument_uninstrument(self, tracer_provider, meter_provider): + from opentelemetry.instrumentation.widesearch import WideSearchInstrumentor + + instrumentor = WideSearchInstrumentor() + instrumentor.instrument( + tracer_provider=tracer_provider, + meter_provider=meter_provider, + skip_dep_check=True, + ) + instrumentor.uninstrument() + + instrumentor2 = WideSearchInstrumentor() + instrumentor2.instrument( + tracer_provider=tracer_provider, + meter_provider=meter_provider, + skip_dep_check=True, + ) + assert instrumentor2._handler is not None + instrumentor2.uninstrument() + + def test_instrumentation_dependencies(self): + from opentelemetry.instrumentation.widesearch import WideSearchInstrumentor + + instrumentor = WideSearchInstrumentor() + deps = instrumentor.instrumentation_dependencies() + assert ("widesearch >= 0.1.0",) == deps + + +# --------------------------------------------------------------------------- +# ENTRY Span Tests (H1: run_single_query) +# --------------------------------------------------------------------------- + + +class TestEntrySpan: + def test_entry_span_created(self, span_exporter, instrument): + """run_single_query should produce an ENTRY span.""" + from src.agent.run import run_single_query + + _run_async(run_single_query("What is AI?", agent_name="searcher")) + + spans = span_exporter.get_finished_spans() + entry_spans = [ + s for s in spans if s.name == "enter_ai_application_system" + ] + assert len(entry_spans) == 1 + + entry = entry_spans[0] + attrs = dict(entry.attributes) + assert attrs.get("gen_ai.span.kind") == "ENTRY" + assert attrs.get("gen_ai.operation.name") == "enter" + assert attrs.get("gen_ai.framework") == "widesearch" + + def test_entry_span_error(self, span_exporter, instrument): + """ENTRY span should record ERROR on exception.""" + from src.agent.run import Runner, run_single_query + + async def failing_step(*, agent, memory): + raise RuntimeError("LLM connection failed") + + Runner._step_override = failing_step + + try: + with pytest.raises(RuntimeError, match="LLM connection failed"): + _run_async(run_single_query("test")) + finally: + Runner._step_override = None + + spans = span_exporter.get_finished_spans() + entry_spans = [ + s for s in spans if s.name == "enter_ai_application_system" + ] + assert len(entry_spans) == 1 + assert entry_spans[0].status.status_code == StatusCode.ERROR + + +# --------------------------------------------------------------------------- +# AGENT Span Tests (H2: Runner.run) +# --------------------------------------------------------------------------- + + +class TestAgentSpan: + def test_agent_span_created(self, span_exporter, instrument): + """Runner.run should produce an AGENT span.""" + from src.agent.run import Runner + + agent = Agent(name="search-agent", model_config_name="gpt-4o") + + async def _run(): + results = [] + async for step in Runner.run(agent, "Hello"): + results.append(step) + return results + + _run_async(_run()) + + spans = span_exporter.get_finished_spans() + agent_spans = [ + s for s in spans if "invoke_agent" in s.name + ] + assert len(agent_spans) == 1 + + span = agent_spans[0] + attrs = dict(span.attributes) + assert attrs.get("gen_ai.span.kind") == "AGENT" + assert attrs.get("gen_ai.operation.name") == "invoke_agent" + assert attrs.get("gen_ai.agent.name") == "search-agent" + assert attrs.get("gen_ai.framework") == "widesearch" + + def test_agent_span_is_child_of_entry(self, span_exporter, instrument): + """AGENT span should be a child of ENTRY span.""" + from src.agent.run import run_single_query + + _run_async(run_single_query("test query", agent_name="test")) + + spans = span_exporter.get_finished_spans() + entry_spans = [ + s for s in spans if s.name == "enter_ai_application_system" + ] + agent_spans = [s for s in spans if "invoke_agent" in s.name] + + assert len(entry_spans) == 1 + assert len(agent_spans) == 1 + + entry = entry_spans[0] + agent = agent_spans[0] + assert agent.parent.span_id == entry.context.span_id + + def test_agent_span_error(self, span_exporter, instrument): + """AGENT span should record ERROR when _step raises.""" + from src.agent.run import Runner + + async def failing_step(*, agent, memory): + raise ValueError("Step failure") + + Runner._step_override = failing_step + agent = Agent(name="fail-agent") + + async def _run(): + async for _ in Runner.run(agent, "Hello"): + pass + + try: + with pytest.raises(ValueError): + _run_async(_run()) + finally: + Runner._step_override = None + + spans = span_exporter.get_finished_spans() + agent_spans = [s for s in spans if "invoke_agent" in s.name] + assert len(agent_spans) == 1 + assert agent_spans[0].status.status_code == StatusCode.ERROR + + +# --------------------------------------------------------------------------- +# STEP Span Tests (H3: Runner._step) +# --------------------------------------------------------------------------- + + +class TestStepSpan: + def test_step_span_created(self, span_exporter, instrument): + """Runner._step should produce a STEP span.""" + from src.agent.run import Runner + + agent = Agent(name="stepper") + + async def _run(): + async for _ in Runner.run(agent, "test"): + pass + + _run_async(_run()) + + spans = span_exporter.get_finished_spans() + step_spans = [s for s in spans if s.name == "react step"] + assert len(step_spans) >= 1 + + step = step_spans[0] + attrs = dict(step.attributes) + assert attrs.get("gen_ai.span.kind") == "STEP" + assert attrs.get("gen_ai.operation.name") == "react" + assert attrs.get("gen_ai.react.round") == 1 + + def test_step_span_is_child_of_agent(self, span_exporter, instrument): + """STEP span should be child of AGENT span.""" + from src.agent.run import Runner + + agent = Agent(name="stepper") + + async def _run(): + async for _ in Runner.run(agent, "test"): + pass + + _run_async(_run()) + + spans = span_exporter.get_finished_spans() + agent_spans = [s for s in spans if "invoke_agent" in s.name] + step_spans = [s for s in spans if s.name == "react step"] + + assert len(agent_spans) == 1 + assert len(step_spans) >= 1 + + agent_span = agent_spans[0] + step_span = step_spans[0] + assert step_span.parent.span_id == agent_span.context.span_id + + def test_step_span_finish_reason_finished(self, span_exporter, instrument): + """STEP span should have finish_reason='finished' when step finishes.""" + from src.agent.run import Runner + + agent = Agent(name="stepper") + + async def _run(): + async for _ in Runner.run(agent, "test"): + pass + + _run_async(_run()) + + spans = span_exporter.get_finished_spans() + step_spans = [s for s in spans if s.name == "react step"] + assert len(step_spans) >= 1 + attrs = dict(step_spans[0].attributes) + assert attrs.get("gen_ai.react.finish_reason") == "finished" + + def test_step_span_error_on_action_step_error( + self, span_exporter, instrument + ): + """STEP span should record ERROR when _step returns ActionStepError.""" + from src.agent.run import Runner + + async def error_step(*, agent, memory): + return ActionStepError(message="LLM timeout") + + Runner._step_override = error_step + agent = Agent(name="error-agent") + + try: + async def _run(): + async for _ in Runner.run(agent, "test"): + pass + + _run_async(_run()) + finally: + Runner._step_override = None + + spans = span_exporter.get_finished_spans() + step_spans = [s for s in spans if s.name == "react step"] + assert len(step_spans) >= 1 + assert step_spans[0].status.status_code == StatusCode.ERROR + attrs = dict(step_spans[0].attributes) + assert attrs.get("gen_ai.react.finish_reason") == "error" + + +# --------------------------------------------------------------------------- +# TOOL Span Tests (H4: Runner._invoke_tool_call) +# --------------------------------------------------------------------------- + + +class TestToolSpan: + def test_tool_span_created(self, span_exporter, instrument): + """_invoke_tool_call should produce TOOL spans.""" + from src.agent.run import Runner + + async def mock_tool(**kwargs): + return InternalResponse(data="search results") + + agent = Agent( + name="tool-agent", + tools={"search_global": mock_tool}, + tools_desc=[ + { + "type": "function", + "function": { + "name": "search_global", + "description": "Search the web", + "parameters": {}, + }, + } + ], + ) + + tc = ToolCall( + tool_name="search_global", + arguments='{"q": "AI"}', + tool_call_id="call_123", + ) + model_resp = ModelResponse( + outputs=[LLMOutputItem(tool_calls=[tc])] + ) + + _run_async(Runner._invoke_tool_call(agent, model_resp)) + + spans = span_exporter.get_finished_spans() + tool_spans = [s for s in spans if "execute_tool" in s.name] + assert len(tool_spans) == 1 + + span = tool_spans[0] + attrs = dict(span.attributes) + assert attrs.get("gen_ai.span.kind") == "TOOL" + assert attrs.get("gen_ai.operation.name") == "execute_tool" + assert attrs.get("gen_ai.tool.name") == "search_global" + assert attrs.get("gen_ai.tool.call.id") == "call_123" + assert attrs.get("gen_ai.framework") == "widesearch" + + def test_tool_span_records_arguments_and_result( + self, span_exporter, instrument + ): + """TOOL span should record arguments and result.""" + from src.agent.run import Runner + + async def mock_tool(q=""): + return InternalResponse(data=f"results for: {q}") + + agent = Agent( + name="tool-agent", + tools={"search_global": mock_tool}, + ) + + tc = ToolCall( + tool_name="search_global", + arguments=json.dumps({"q": "OpenTelemetry"}), + tool_call_id="call_456", + ) + model_resp = ModelResponse( + outputs=[LLMOutputItem(tool_calls=[tc])] + ) + + results = _run_async(Runner._invoke_tool_call(agent, model_resp)) + assert len(results) == 1 + assert results[0].content == "results for: OpenTelemetry" + + spans = span_exporter.get_finished_spans() + tool_spans = [s for s in spans if "execute_tool" in s.name] + assert len(tool_spans) == 1 + attrs = dict(tool_spans[0].attributes) + assert "gen_ai.tool.call.arguments" in attrs + assert "gen_ai.tool.call.result" in attrs + + def test_tool_span_error_on_missing_tool(self, span_exporter, instrument): + """TOOL span should record ERROR when tool not found.""" + from src.agent.run import Runner + + agent = Agent(name="tool-agent", tools={}) + + tc = ToolCall( + tool_name="nonexistent_tool", + arguments="{}", + tool_call_id="call_789", + ) + model_resp = ModelResponse( + outputs=[LLMOutputItem(tool_calls=[tc])] + ) + + results = _run_async(Runner._invoke_tool_call(agent, model_resp)) + assert len(results) == 1 + assert results[0].error_marker is not None + + spans = span_exporter.get_finished_spans() + tool_spans = [s for s in spans if "execute_tool" in s.name] + assert len(tool_spans) == 1 + assert tool_spans[0].status.status_code == StatusCode.ERROR + + def test_tool_span_error_on_exception(self, span_exporter, instrument): + """TOOL span should record ERROR when tool raises exception.""" + from src.agent.run import Runner + + async def failing_tool(**kwargs): + raise ConnectionError("Network error") + + agent = Agent( + name="tool-agent", + tools={"flaky_tool": failing_tool}, + ) + + tc = ToolCall( + tool_name="flaky_tool", + arguments="{}", + tool_call_id="call_err", + ) + model_resp = ModelResponse( + outputs=[LLMOutputItem(tool_calls=[tc])] + ) + + results = _run_async(Runner._invoke_tool_call(agent, model_resp)) + assert len(results) == 1 + assert results[0].error_marker is not None + assert "Network error" in results[0].error_marker.message + + spans = span_exporter.get_finished_spans() + tool_spans = [s for s in spans if "execute_tool" in s.name] + assert len(tool_spans) == 1 + assert tool_spans[0].status.status_code == StatusCode.ERROR + + def test_multiple_tool_spans(self, span_exporter, instrument): + """Multiple tool_calls should produce multiple TOOL spans.""" + from src.agent.run import Runner + + async def mock_search(**kwargs): + return InternalResponse(data="search result") + + async def mock_browse(**kwargs): + return InternalResponse(data="page content") + + agent = Agent( + name="multi-tool", + tools={ + "search_global": mock_search, + "text_browser_view": mock_browse, + }, + ) + + tc1 = ToolCall( + tool_name="search_global", + arguments='{"q": "test"}', + tool_call_id="call_1", + ) + tc2 = ToolCall( + tool_name="text_browser_view", + arguments='{"url": "http://example.com"}', + tool_call_id="call_2", + ) + model_resp = ModelResponse( + outputs=[LLMOutputItem(tool_calls=[tc1, tc2])] + ) + + results = _run_async(Runner._invoke_tool_call(agent, model_resp)) + assert len(results) == 2 + + spans = span_exporter.get_finished_spans() + tool_spans = [s for s in spans if "execute_tool" in s.name] + assert len(tool_spans) == 2 + + +# --------------------------------------------------------------------------- +# TASK Span Tests (H5: create_sub_agents_wrap) +# --------------------------------------------------------------------------- + + +class TestTaskSpan: + def test_task_span_created(self, span_exporter, instrument): + """create_sub_agents closure should produce a TASK span.""" + from src.agent.multi_agent_tools import create_sub_agents_wrap + + closure = create_sub_agents_wrap( + "main-agent", "gpt-4o", {}, [], "system prompt" + ) + + sub_agents = [ + {"index": 0, "prompt": "Search for X"}, + {"index": 1, "prompt": "Search for Y"}, + ] + + result = _run_async(closure(sub_agents)) + assert result is not None + + spans = span_exporter.get_finished_spans() + task_spans = [ + s for s in spans if s.name == "run_task create_sub_agents" + ] + assert len(task_spans) == 1 + + span = task_spans[0] + attrs = dict(span.attributes) + assert attrs.get("gen_ai.span.kind") == "TASK" + assert attrs.get("gen_ai.operation.name") == "run_task" + assert attrs.get("gen_ai.framework") == "widesearch" + assert "input.value" in attrs + + def test_task_span_records_output(self, span_exporter, instrument): + """TASK span should record output.value.""" + from src.agent.multi_agent_tools import create_sub_agents_wrap + + closure = create_sub_agents_wrap( + "agent", "gpt-4o", {}, [], "prompt" + ) + + sub_agents = [{"index": 0, "prompt": "find info"}] + result = _run_async(closure(sub_agents)) + + spans = span_exporter.get_finished_spans() + task_spans = [ + s for s in spans if s.name == "run_task create_sub_agents" + ] + assert len(task_spans) == 1 + attrs = dict(task_spans[0].attributes) + assert "output.value" in attrs + + def test_task_span_error(self, span_exporter, instrument): + """TASK span should record ERROR when closure raises.""" + from src.agent.multi_agent_tools import create_sub_agents_wrap + + # Temporarily replace create_sub_agents_wrap's inner closure behavior + import src.agent.multi_agent_tools as mat + + original = mat.create_sub_agents_wrap + + def error_factory(*args, **kwargs): + original_closure = original(*args, **kwargs) + + async def error_closure(sub_agents): + raise RuntimeError("Sub-agent execution failed") + + return error_closure + + mat.create_sub_agents_wrap = error_factory + + # Re-instrument to pick up the new function + from opentelemetry.instrumentation.widesearch import WideSearchInstrumentor + + instrument.uninstrument() + instrument.instrument( + tracer_provider=span_exporter._tracer_provider + if hasattr(span_exporter, "_tracer_provider") + else None, + skip_dep_check=True, + ) + + # Since re-instrumentation is complex, let's just test the wrapper directly + # by calling the instrumented version + instrument.uninstrument() + + # Simpler approach: directly test the wrap function + from opentelemetry.instrumentation.widesearch.patch import ( + wrap_create_sub_agents_factory, + ) + from opentelemetry.util.genai.extended_handler import ( + ExtendedTelemetryHandler, + ) + from opentelemetry.sdk.trace import TracerProvider + from opentelemetry.sdk.trace.export import SimpleSpanProcessor + from opentelemetry.sdk.trace.export.in_memory_span_exporter import ( + InMemorySpanExporter, + ) + + exporter = InMemorySpanExporter() + tp = TracerProvider() + tp.add_span_processor(SimpleSpanProcessor(exporter)) + handler = ExtendedTelemetryHandler(tracer_provider=tp) + + def failing_factory(*args, **kwargs): + async def failing_closure(sub_agents): + raise RuntimeError("Boom") + + return failing_closure + + wrapped_factory = wrap_create_sub_agents_factory( + failing_factory, None, (), {}, handler=handler + ) + + with pytest.raises(RuntimeError, match="Boom"): + _run_async(wrapped_factory([{"index": 0, "prompt": "x"}])) + + spans = exporter.get_finished_spans() + task_spans = [ + s for s in spans if s.name == "run_task create_sub_agents" + ] + assert len(task_spans) == 1 + assert task_spans[0].status.status_code == StatusCode.ERROR + + +# --------------------------------------------------------------------------- +# Parent-Child Relationship Tests +# --------------------------------------------------------------------------- + + +class TestParentChildRelationships: + def test_full_hierarchy_entry_agent_step(self, span_exporter, instrument): + """Full call through run_single_query should produce ENTRY > AGENT > STEP.""" + from src.agent.run import run_single_query + + _run_async(run_single_query("hierarchy test", agent_name="root")) + + spans = span_exporter.get_finished_spans() + entry_spans = [ + s for s in spans if s.name == "enter_ai_application_system" + ] + agent_spans = [s for s in spans if "invoke_agent" in s.name] + step_spans = [s for s in spans if s.name == "react step"] + + assert len(entry_spans) == 1 + assert len(agent_spans) == 1 + assert len(step_spans) >= 1 + + entry = entry_spans[0] + agent = agent_spans[0] + step = step_spans[0] + + # AGENT is child of ENTRY + assert agent.parent.span_id == entry.context.span_id + # STEP is child of AGENT + assert step.parent.span_id == agent.context.span_id + + def test_tool_span_is_child_of_step(self, span_exporter, instrument): + """TOOL span should be child of the STEP span when invoked during a step.""" + from src.agent.run import Runner + + async def mock_tool(**kwargs): + return InternalResponse(data="result") + + agent = Agent( + name="hierarchy-agent", + tools={"my_tool": mock_tool}, + ) + + async def custom_step(*, agent, memory): + tc = ToolCall( + tool_name="my_tool", + arguments="{}", + tool_call_id="tc_hier", + ) + model_resp = ModelResponse( + outputs=[LLMOutputItem(tool_calls=[tc])] + ) + await Runner._invoke_tool_call(agent, model_resp) + return ActionStep(step_status=StepStatus.FINISHED, content="done") + + Runner._step_override = custom_step + + try: + async def _run(): + async for _ in Runner.run(agent, "test"): + pass + + _run_async(_run()) + finally: + Runner._step_override = None + + spans = span_exporter.get_finished_spans() + step_spans = [s for s in spans if s.name == "react step"] + tool_spans = [s for s in spans if "execute_tool" in s.name] + + assert len(step_spans) >= 1 + assert len(tool_spans) >= 1 + + step_span = step_spans[0] + tool_span = tool_spans[0] + assert tool_span.parent.span_id == step_span.context.span_id From eab943911d720b6517efc57733709c7a35aff1ee Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=89=A7=E6=80=9D?= Date: Wed, 6 May 2026 23:03:14 +0800 Subject: [PATCH 5/8] feat: support vita Change-Id: I71842eb28f7a3c8d5c0fb0e9e2caec31e69d19f0 (cherry picked from commit 9abf7a1a013f758d5a38c1ddb176bac2f9133441) Co-authored-by: 123liuziming <32130965+123liuziming@users.noreply.github.com> --- .../loongsuite-instrumentation-vita/README.md | 17 + .../examples/__init__.py | 0 .../pyproject.toml | 55 ++ .../instrumentation/vita/__init__.py | 223 ++++++++ .../instrumentation/vita/package.py | 3 + .../instrumentation/vita/patch.py | 432 ++++++++++++++++ .../instrumentation/vita/utils.py | 169 +++++++ .../instrumentation/vita/version.py | 1 + .../tests/__init__.py | 0 .../tests/conftest.py | 99 ++++ .../tests/test_instrumentor.py | 478 ++++++++++++++++++ 11 files changed, 1477 insertions(+) create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-vita/README.md create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-vita/examples/__init__.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-vita/pyproject.toml create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-vita/src/opentelemetry/instrumentation/vita/__init__.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-vita/src/opentelemetry/instrumentation/vita/package.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-vita/src/opentelemetry/instrumentation/vita/patch.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-vita/src/opentelemetry/instrumentation/vita/utils.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-vita/src/opentelemetry/instrumentation/vita/version.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-vita/tests/__init__.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-vita/tests/conftest.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-vita/tests/test_instrumentor.py diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-vita/README.md b/instrumentation-loongsuite/loongsuite-instrumentation-vita/README.md new file mode 100644 index 000000000..a722e267c --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-vita/README.md @@ -0,0 +1,17 @@ +# LoongSuite VitaBench Instrumentation + +OpenTelemetry instrumentation for the VitaBench multi-domain simulation framework. + +## Installation + +```bash +pip install loongsuite-instrumentation-vita +``` + +## Usage + +```python +from opentelemetry.instrumentation.vita import VitaInstrumentor + +VitaInstrumentor().instrument() +``` diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-vita/examples/__init__.py b/instrumentation-loongsuite/loongsuite-instrumentation-vita/examples/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-vita/pyproject.toml b/instrumentation-loongsuite/loongsuite-instrumentation-vita/pyproject.toml new file mode 100644 index 000000000..d1df8fa2e --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-vita/pyproject.toml @@ -0,0 +1,55 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "loongsuite-instrumentation-vita" +dynamic = ["version"] +description = "LoongSuite VitaBench instrumentation" +readme = "README.md" +license = "Apache-2.0" +requires-python = ">=3.10,<4" +authors = [ + { name = "Zhiyong Liu", email = "liuzhiyong.lzy@alibaba-inc.com" }, + { name = "OpenTelemetry Authors", email = "cncf-opentelemetry-contributors@lists.cncf.io" }, +] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Developers", + "License :: OSI Approved :: Apache Software License", + "Programming Language :: Python", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", +] +dependencies = [ + "opentelemetry-api >= 1.37.0", + "opentelemetry-instrumentation >= 0.58b0", + "opentelemetry-semantic-conventions >= 0.58b0", + "wrapt >= 1.0.0, < 2.0.0", + "opentelemetry-util-genai >= 0.3b0.dev0", +] + +[project.optional-dependencies] +instruments = [ + "vita >= 0.0.1", +] + +[project.entry-points.opentelemetry_instrumentor] +vita = "opentelemetry.instrumentation.vita:VitaInstrumentor" + +[project.urls] +Homepage = "https://github.com/alibaba/loongsuite-python-agent/tree/main/instrumentation-loongsuite/loongsuite-instrumentation-vita" +Repository = "https://github.com/alibaba/loongsuite-python-agent" + +[tool.hatch.version] +path = "src/opentelemetry/instrumentation/vita/version.py" + +[tool.hatch.build.targets.sdist] +include = [ + "/src", + "/tests", +] + +[tool.hatch.build.targets.wheel] +packages = ["src/opentelemetry"] diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-vita/src/opentelemetry/instrumentation/vita/__init__.py b/instrumentation-loongsuite/loongsuite-instrumentation-vita/src/opentelemetry/instrumentation/vita/__init__.py new file mode 100644 index 000000000..1e58668a6 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-vita/src/opentelemetry/instrumentation/vita/__init__.py @@ -0,0 +1,223 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +OpenTelemetry VitaBench Instrumentation + +Usage +----- +.. code:: python + + from opentelemetry.instrumentation.vita import VitaInstrumentor + + VitaInstrumentor().instrument() + + # ... run vitabench tasks ... + + VitaInstrumentor().uninstrument() + +API +--- +""" + +from __future__ import annotations + +import logging +from typing import Any, Collection + +from wrapt import wrap_function_wrapper + +from opentelemetry.instrumentation.instrumentor import BaseInstrumentor +from opentelemetry.instrumentation.utils import unwrap +from opentelemetry.instrumentation.vita.package import _instruments +from opentelemetry.instrumentation.vita.patch import ( + wrap_generate, + wrap_generate_next_message, + wrap_get_response, + wrap_orchestrator_run, + wrap_orchestrator_step, + wrap_run_task, +) +from opentelemetry.instrumentation.vita.version import __version__ +from opentelemetry.util.genai.extended_handler import ExtendedTelemetryHandler + +logger = logging.getLogger(__name__) + +__all__ = ["VitaInstrumentor", "__version__"] + + +class VitaInstrumentor(BaseInstrumentor): + """OpenTelemetry instrumentor for VitaBench framework. + + Instruments the following components: + - vita.run.run_task(): Entry spans (ENTRY) + - Orchestrator.run(): Workflow spans (CHAIN) + - Orchestrator.step(): ReAct step spans (STEP) + - LLMAgent.generate_next_message(): Agent spans (AGENT) + - generate(): LLM call spans (LLM) + - Environment.get_response(): Tool execution spans (TOOL) + """ + + def __init__(self): + super().__init__() + self._handler = None + + def instrumentation_dependencies(self) -> Collection[str]: + return _instruments + + def _instrument(self, **kwargs: Any) -> None: + """Enable VitaBench instrumentation.""" + tracer_provider = kwargs.get("tracer_provider") + meter_provider = kwargs.get("meter_provider") + logger_provider = kwargs.get("logger_provider") + + self._handler = ExtendedTelemetryHandler( + tracer_provider=tracer_provider, + meter_provider=meter_provider, + logger_provider=logger_provider, + ) + + # Hook #5: generate -> LLM. Wrap this first so modules that import + # generate directly (for example vita.agent.llm_agent) bind to the + # instrumented function during their import. + try: + wrap_function_wrapper( + module="vita.utils.llm_utils", + name="generate", + wrapper=lambda w, i, a, k: wrap_generate( + w, i, a, k, handler=self._handler + ), + ) + logger.debug("Instrumented vita.utils.llm_utils.generate") + except Exception as e: + logger.warning(f"Could not wrap vita.utils.llm_utils.generate: {e}") + + # Hook #1: run_task -> ENTRY + try: + wrap_function_wrapper( + module="vita.run", + name="run_task", + wrapper=lambda w, i, a, k: wrap_run_task( + w, i, a, k, handler=self._handler + ), + ) + logger.debug("Instrumented vita.run.run_task") + except Exception as e: + logger.warning(f"Could not wrap vita.run.run_task: {e}") + + # Hook #2: Orchestrator.run -> CHAIN + try: + wrap_function_wrapper( + module="vita.orchestrator.orchestrator", + name="Orchestrator.run", + wrapper=lambda w, i, a, k: wrap_orchestrator_run( + w, i, a, k, handler=self._handler + ), + ) + logger.debug("Instrumented Orchestrator.run") + except Exception as e: + logger.warning(f"Could not wrap Orchestrator.run: {e}") + + # Hook #3: Orchestrator.step -> STEP + try: + wrap_function_wrapper( + module="vita.orchestrator.orchestrator", + name="Orchestrator.step", + wrapper=lambda w, i, a, k: wrap_orchestrator_step( + w, i, a, k, handler=self._handler + ), + ) + logger.debug("Instrumented Orchestrator.step") + except Exception as e: + logger.warning(f"Could not wrap Orchestrator.step: {e}") + + # Hook #4a: LLMAgent.generate_next_message -> AGENT + try: + wrap_function_wrapper( + module="vita.agent.llm_agent", + name="LLMAgent.generate_next_message", + wrapper=lambda w, i, a, k: wrap_generate_next_message( + w, i, a, k, handler=self._handler + ), + ) + logger.debug("Instrumented LLMAgent.generate_next_message") + except Exception as e: + logger.warning(f"Could not wrap LLMAgent.generate_next_message: {e}") + + # Hook #4b: LLMSoloAgent.generate_next_message -> AGENT + try: + wrap_function_wrapper( + module="vita.agent.llm_agent", + name="LLMSoloAgent.generate_next_message", + wrapper=lambda w, i, a, k: wrap_generate_next_message( + w, i, a, k, handler=self._handler + ), + ) + logger.debug("Instrumented LLMSoloAgent.generate_next_message") + except Exception as e: + logger.warning(f"Could not wrap LLMSoloAgent.generate_next_message: {e}") + + # Hook #6: Environment.get_response -> TOOL + try: + wrap_function_wrapper( + module="vita.environment.environment", + name="Environment.get_response", + wrapper=lambda w, i, a, k: wrap_get_response( + w, i, a, k, handler=self._handler + ), + ) + logger.debug("Instrumented Environment.get_response") + except Exception as e: + logger.warning(f"Could not wrap Environment.get_response: {e}") + + def _uninstrument(self, **kwargs: Any) -> None: + """Disable VitaBench instrumentation.""" + try: + import vita.run # noqa: PLC0415 + + unwrap(vita.run, "run_task") + except Exception as e: + logger.debug(f"Failed to uninstrument vita.run.run_task: {e}") + + try: + import vita.orchestrator.orchestrator # noqa: PLC0415 + + unwrap(vita.orchestrator.orchestrator.Orchestrator, "run") + unwrap(vita.orchestrator.orchestrator.Orchestrator, "step") + except Exception as e: + logger.debug(f"Failed to uninstrument Orchestrator: {e}") + + try: + import vita.agent.llm_agent # noqa: PLC0415 + + unwrap(vita.agent.llm_agent.LLMAgent, "generate_next_message") + unwrap(vita.agent.llm_agent.LLMSoloAgent, "generate_next_message") + except Exception as e: + logger.debug(f"Failed to uninstrument LLMAgent: {e}") + + try: + import vita.utils.llm_utils # noqa: PLC0415 + + unwrap(vita.utils.llm_utils, "generate") + except Exception as e: + logger.debug(f"Failed to uninstrument generate: {e}") + + try: + import vita.environment.environment # noqa: PLC0415 + + unwrap(vita.environment.environment.Environment, "get_response") + except Exception as e: + logger.debug(f"Failed to uninstrument Environment: {e}") + + self._handler = None diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-vita/src/opentelemetry/instrumentation/vita/package.py b/instrumentation-loongsuite/loongsuite-instrumentation-vita/src/opentelemetry/instrumentation/vita/package.py new file mode 100644 index 000000000..a776722c9 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-vita/src/opentelemetry/instrumentation/vita/package.py @@ -0,0 +1,3 @@ +_instruments = ("vita >= 0.0.1",) + +_supports_metrics = False diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-vita/src/opentelemetry/instrumentation/vita/patch.py b/instrumentation-loongsuite/loongsuite-instrumentation-vita/src/opentelemetry/instrumentation/vita/patch.py new file mode 100644 index 000000000..182da38d6 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-vita/src/opentelemetry/instrumentation/vita/patch.py @@ -0,0 +1,432 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Patch functions for VitaBench instrumentation. + +Wraps key vitabench methods to generate OpenTelemetry spans: +- run_task() -> ENTRY spans +- Orchestrator.run() -> CHAIN spans +- Orchestrator.step() -> STEP spans (react) +- LLMAgent.generate_next_message() -> AGENT spans +- generate() -> LLM spans +- Environment.get_response() -> TOOL spans +""" + +from __future__ import annotations + +import json +import logging +import uuid +from contextvars import ContextVar +from typing import Any, Optional + +from opentelemetry import trace as trace_api +from opentelemetry.trace import SpanKind, Status, StatusCode +from opentelemetry.util.genai.extended_handler import ExtendedTelemetryHandler +from opentelemetry.util.genai.extended_semconv import gen_ai_extended_attributes +from opentelemetry.util.genai.extended_types import ( + EntryInvocation, + ExecuteToolInvocation, + InvokeAgentInvocation, + ReactStepInvocation, +) +from opentelemetry.util.genai.types import ( + Error, + InputMessage, + LLMInvocation, + OutputMessage, + Text, +) + +from .utils import ( + _convert_vita_assistant_to_output, + _convert_vita_messages_to_input, + _get_tool_definitions, + _infer_provider, + _MAX_CONTENT_LEN, +) + +logger = logging.getLogger(__name__) + +# ContextVars for ReAct step tracking +_react_step_invocation: ContextVar[Optional[ReactStepInvocation]] = ContextVar( + "vita_react_step_invocation", default=None +) +_react_step_counter: ContextVar[int] = ContextVar( + "vita_react_step_counter", default=0 +) + +# Reentrancy guard for AGENT span (LLMSoloAgent extends LLMAgent) +_in_agent_invoke: ContextVar[bool] = ContextVar( + "vita_in_agent_invoke", default=False +) + + +def _close_active_react_step(handler: ExtendedTelemetryHandler) -> None: + """Close the currently active react_step span, if any.""" + prev = _react_step_invocation.get() + if prev is not None: + try: + handler.stop_react_step(prev) + except Exception as e: + logger.debug(f"Failed to close react step: {e}") + _react_step_invocation.set(None) + + +# ==================== Hook #1: run_task -> ENTRY ==================== + + +def wrap_run_task( + wrapped, instance, args, kwargs, handler: ExtendedTelemetryHandler +): + """Wrapper for vita.run.run_task to create ENTRY span.""" + task = args[1] if len(args) > 1 else kwargs.get("task") + domain = args[0] if args else kwargs.get("domain") + + invocation = EntryInvocation( + session_id=str(uuid.uuid4()), + user_id=None, + ) + invocation.attributes["gen_ai.framework"] = "vitabench" + + if task and hasattr(task, "instructions") and task.instructions: + invocation.input_messages = [ + InputMessage(role="user", parts=[Text(content=str(task.instructions)[:_MAX_CONTENT_LEN])]) + ] + + handler.start_entry(invocation) + try: + result = wrapped(*args, **kwargs) + + if result: + output_parts = [] + if hasattr(result, "termination_reason") and result.termination_reason: + output_parts.append(Text(content=f"termination: {result.termination_reason}")) + if hasattr(result, "reward_info") and result.reward_info: + reward = getattr(result.reward_info, "reward", None) + if reward is not None: + output_parts.append(Text(content=f"reward: {reward}")) + if output_parts: + invocation.output_messages = [ + OutputMessage( + role="assistant", + parts=output_parts, + finish_reason="stop", + ) + ] + + handler.stop_entry(invocation) + return result + except Exception as e: + handler.fail_entry(invocation, Error(message=str(e), type=type(e))) + raise + + +# ==================== Hook #2: Orchestrator.run -> CHAIN ==================== + + +def wrap_orchestrator_run( + wrapped, instance, args, kwargs, handler: ExtendedTelemetryHandler +): + """Wrapper for Orchestrator.run to create CHAIN span.""" + task = getattr(instance, "task", None) + domain = getattr(instance, "domain", "unknown") + span_name = f"workflow {domain}" + + input_text = "" + if task and hasattr(task, "instructions") and task.instructions: + input_text = str(task.instructions)[:_MAX_CONTENT_LEN] + + tracer = handler._tracer + + # Reset step counter for this orchestrator run + counter_token = _react_step_counter.set(0) + step_token = _react_step_invocation.set(None) + + with tracer.start_as_current_span( + name=span_name, + kind=SpanKind.INTERNAL, + attributes={ + "gen_ai.operation.name": "workflow", + "gen_ai.system": "vitabench", + gen_ai_extended_attributes.GEN_AI_SPAN_KIND: "CHAIN", + "gen_ai.framework": "vitabench", + }, + ) as span: + if input_text: + span.set_attribute("input.value", input_text) + + try: + result = wrapped(*args, **kwargs) + + # Close any remaining open step span + _close_active_react_step(handler) + + if result and hasattr(result, "termination_reason") and result.termination_reason: + span.set_attribute("output.value", str(result.termination_reason)) + + span.set_status(Status(StatusCode.OK)) + return result + except Exception as e: + # Close any remaining open step span + _close_active_react_step(handler) + span.record_exception(e) + span.set_status(Status(StatusCode.ERROR)) + raise + finally: + _react_step_counter.reset(counter_token) + _react_step_invocation.reset(step_token) + + +# ==================== Hook #3: Orchestrator.step -> STEP ==================== + + +def wrap_orchestrator_step( + wrapped, instance, args, kwargs, handler: ExtendedTelemetryHandler +): + """Wrapper for Orchestrator.step to create STEP span on AGENT turns.""" + to_role = getattr(instance, "to_role", None) + + # Import Role enum dynamically to avoid import-time dependency + _Role = None + try: + from vita.orchestrator.orchestrator import Role + _Role = Role + except ImportError: + pass + + is_agent_turn = False + if _Role is not None: + is_agent_turn = (to_role == _Role.AGENT) + else: + is_agent_turn = (str(to_role) == "Role.AGENT" or str(to_role) == "agent") + + if is_agent_turn: + # Close previous STEP span (deferred close strategy) + _close_active_react_step(handler) + + step_num = _react_step_counter.get() + 1 + _react_step_counter.set(step_num) + + step_inv = ReactStepInvocation(round=step_num) + handler.start_react_step(step_inv) + _react_step_invocation.set(step_inv) + + try: + result = wrapped(*args, **kwargs) + + if is_agent_turn: + current_step = _react_step_invocation.get() + if current_step: + done = getattr(instance, "done", False) + if done: + term_reason = getattr(instance, "termination_reason", None) + if term_reason: + current_step.finish_reason = ( + term_reason.value + if hasattr(term_reason, "value") + else str(term_reason) + ) + else: + current_step.finish_reason = "agent_stop" + else: + message = getattr(instance, "message", None) + if message and hasattr(message, "is_tool_call") and message.is_tool_call(): + current_step.finish_reason = "tool_call" + else: + current_step.finish_reason = "assistant_text" + + return result + except Exception as e: + current_step = _react_step_invocation.get() + if current_step: + current_step.finish_reason = "error" + handler.fail_react_step(current_step, Error(message=str(e), type=type(e))) + _react_step_invocation.set(None) + raise + + +# ==================== Hook #4: generate_next_message -> AGENT ==================== + + +def wrap_generate_next_message( + wrapped, instance, args, kwargs, handler: ExtendedTelemetryHandler +): + """Wrapper for LLMAgent.generate_next_message / LLMSoloAgent.generate_next_message.""" + # Reentrancy guard + if _in_agent_invoke.get(): + return wrapped(*args, **kwargs) + token = _in_agent_invoke.set(True) + + try: + agent_name = instance.__class__.__name__ + model = getattr(instance, "llm", None) + + invocation = InvokeAgentInvocation( + provider="vitabench", + agent_name=agent_name, + request_model=model, + ) + + # input_messages + message = args[0] if args else kwargs.get("message") + state = args[1] if len(args) > 1 else kwargs.get("state") + if message: + invocation.input_messages = _convert_vita_messages_to_input([message]) + + # system_instruction + if state and hasattr(state, "system_messages") and state.system_messages: + invocation.system_instruction = [ + Text(content=str(sm.content)[:_MAX_CONTENT_LEN]) + for sm in state.system_messages + if sm and getattr(sm, "content", None) + ] + + # tool_definitions + tools = getattr(instance, "tools", None) + tool_defs = _get_tool_definitions(tools) + if tool_defs: + invocation.tool_definitions = tool_defs + + handler.start_invoke_agent(invocation) + + try: + result = wrapped(*args, **kwargs) + assistant_msg, _ = result + + # output_messages + invocation.output_messages = _convert_vita_assistant_to_output(assistant_msg) + + # token usage + usage = getattr(assistant_msg, "usage", None) + if usage and isinstance(usage, dict): + invocation.input_tokens = usage.get("prompt_tokens") + invocation.output_tokens = usage.get("completion_tokens") + + handler.stop_invoke_agent(invocation) + return result + except Exception as e: + handler.fail_invoke_agent(invocation, Error(message=str(e), type=type(e))) + raise + finally: + _in_agent_invoke.reset(token) + + +# ==================== Hook #5: generate -> LLM ==================== + + +def wrap_generate( + wrapped, instance, args, kwargs, handler: ExtendedTelemetryHandler +): + """Wrapper for vita.utils.llm_utils.generate to create LLM span.""" + model = args[0] if args else kwargs.get("model", "unknown") + messages = args[1] if len(args) > 1 else kwargs.get("messages", []) + tools = args[2] if len(args) > 2 else kwargs.get("tools") + temperature = kwargs.get("temperature") + + invocation = LLMInvocation( + request_model=model or "unknown", + provider=_infer_provider(model or ""), + temperature=temperature, + ) + invocation.max_tokens = kwargs.get("max_tokens") + + # input_messages + invocation.input_messages = _convert_vita_messages_to_input(messages) + + # tool_definitions + tool_defs = _get_tool_definitions(tools) + if tool_defs: + invocation.tool_definitions = tool_defs + + handler.start_llm(invocation) + + try: + result = wrapped(*args, **kwargs) + + if result: + # output_messages + invocation.output_messages = _convert_vita_assistant_to_output(result) + + # response_model_name + invocation.response_model_name = model + + # finish_reasons + if getattr(result, "tool_calls", None): + invocation.finish_reasons = ["tool_calls"] + else: + invocation.finish_reasons = ["stop"] + + # token usage + usage = getattr(result, "usage", None) + if usage and isinstance(usage, dict): + invocation.input_tokens = usage.get("prompt_tokens") + invocation.output_tokens = usage.get("completion_tokens") + + handler.stop_llm(invocation) + return result + except Exception as e: + handler.fail_llm(invocation, Error(message=str(e), type=type(e))) + raise + + +# ==================== Hook #6: Environment.get_response -> TOOL ==================== + + +def wrap_get_response( + wrapped, instance, args, kwargs, handler: ExtendedTelemetryHandler +): + """Wrapper for Environment.get_response to create TOOL span.""" + message = args[0] if args else kwargs.get("message") + + tool_name = getattr(message, "name", "unknown") if message else "unknown" + tool_call_id = getattr(message, "id", None) if message else None + + invocation = ExecuteToolInvocation( + tool_name=tool_name, + tool_call_id=tool_call_id, + provider="vitabench", + ) + + # tool_call_arguments + if message and hasattr(message, "arguments") and message.arguments: + try: + invocation.tool_call_arguments = json.dumps( + message.arguments, ensure_ascii=False, default=str + )[:_MAX_CONTENT_LEN] + except Exception: + invocation.tool_call_arguments = str(message.arguments)[:_MAX_CONTENT_LEN] + + handler.start_execute_tool(invocation) + + try: + result = wrapped(*args, **kwargs) + + # tool_call_result + if result and getattr(result, "content", None): + invocation.tool_call_result = str(result.content)[:_MAX_CONTENT_LEN] + + # Check if tool reported an error + if result and getattr(result, "error", False): + handler.fail_execute_tool( + invocation, + Error(message=f"Tool error: {getattr(result, 'content', '')}", type=RuntimeError), + ) + else: + handler.stop_execute_tool(invocation) + + return result + except Exception as e: + handler.fail_execute_tool(invocation, Error(message=str(e), type=type(e))) + raise diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-vita/src/opentelemetry/instrumentation/vita/utils.py b/instrumentation-loongsuite/loongsuite-instrumentation-vita/src/opentelemetry/instrumentation/vita/utils.py new file mode 100644 index 000000000..0793a6cc0 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-vita/src/opentelemetry/instrumentation/vita/utils.py @@ -0,0 +1,169 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Utility functions for VitaBench instrumentation. + +Handles conversion between vitabench Message types and +OpenTelemetry GenAI semantic convention types. +""" + +from __future__ import annotations + +import json +import logging +from typing import Any, List, Optional + +from opentelemetry.util.genai.types import ( + FunctionToolDefinition, + InputMessage, + OutputMessage, + Text, + ToolCall as OTelToolCall, + ToolCallResponse, +) + +logger = logging.getLogger(__name__) + +_MAX_CONTENT_LEN = 4096 + + +def _convert_vita_messages_to_input(messages: Any) -> List[InputMessage]: + """Convert vita Message list to OTel InputMessage list.""" + if not messages: + return [] + + if not isinstance(messages, list): + messages = [messages] + + result = [] + for msg in messages: + try: + role = getattr(msg, "role", None) + if role is None: + continue + + parts = [] + content = getattr(msg, "content", None) + tool_calls = getattr(msg, "tool_calls", None) + + if role == "tool": + msg_id = getattr(msg, "id", None) or "" + if content: + parts.append( + ToolCallResponse( + id=msg_id, + response=str(content)[:_MAX_CONTENT_LEN], + ) + ) + else: + if content: + parts.append(Text(content=str(content)[:_MAX_CONTENT_LEN])) + if tool_calls: + for tc in tool_calls: + tc_args = getattr(tc, "arguments", {}) + if isinstance(tc_args, dict): + tc_args = json.dumps(tc_args, ensure_ascii=False, default=str) + parts.append( + OTelToolCall( + name=getattr(tc, "name", ""), + id=getattr(tc, "id", None), + arguments=tc_args, + ) + ) + + if parts: + result.append(InputMessage(role=role, parts=parts)) + except Exception as e: + logger.debug(f"Error converting vita message: {e}") + continue + + return result + + +def _convert_vita_assistant_to_output(msg: Any) -> List[OutputMessage]: + """Convert vita AssistantMessage to OTel OutputMessage list.""" + if not msg: + return [] + + parts = [] + content = getattr(msg, "content", None) + tool_calls = getattr(msg, "tool_calls", None) + + if content: + parts.append(Text(content=str(content)[:_MAX_CONTENT_LEN])) + if tool_calls: + for tc in tool_calls: + tc_args = getattr(tc, "arguments", {}) + if isinstance(tc_args, dict): + tc_args = json.dumps(tc_args, ensure_ascii=False, default=str) + parts.append( + OTelToolCall( + name=getattr(tc, "name", ""), + id=getattr(tc, "id", None), + arguments=tc_args, + ) + ) + + finish_reason = "tool_calls" if tool_calls else "stop" + + if not parts: + parts.append(Text(content="")) + + return [OutputMessage(role="assistant", parts=parts, finish_reason=finish_reason)] + + +def _infer_provider(model_name: str) -> str: + """Infer provider from model name string.""" + if not model_name: + return "unknown" + m = model_name.lower() + if "gpt" in m or "o1" in m or "o3" in m: + return "openai" + if "claude" in m: + return "anthropic" + if "qwen" in m: + return "alibaba_cloud" + if "deepseek" in m: + return "deepseek" + if "gemini" in m: + return "google" + return "unknown" + + +def _get_tool_definitions(tools: Any) -> Optional[List[FunctionToolDefinition]]: + """Extract tool definitions from vita Tool list.""" + if not tools: + return None + + try: + defs = [] + for t in tools: + name = getattr(t, "name", None) + if not name: + continue + parameters = None + openai_schema = getattr(t, "openai_schema", None) + if isinstance(openai_schema, dict): + function_schema = openai_schema.get("function", openai_schema) + parameters = function_schema.get("parameters") + defs.append( + FunctionToolDefinition( + name=name, + description=getattr(t, "short_desc", None), + parameters=parameters, + ) + ) + return defs if defs else None + except Exception: + return None diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-vita/src/opentelemetry/instrumentation/vita/version.py b/instrumentation-loongsuite/loongsuite-instrumentation-vita/src/opentelemetry/instrumentation/vita/version.py new file mode 100644 index 000000000..26056b5d8 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-vita/src/opentelemetry/instrumentation/vita/version.py @@ -0,0 +1 @@ +__version__ = "0.5.0.dev" diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-vita/tests/__init__.py b/instrumentation-loongsuite/loongsuite-instrumentation-vita/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-vita/tests/conftest.py b/instrumentation-loongsuite/loongsuite-instrumentation-vita/tests/conftest.py new file mode 100644 index 000000000..0d2ab7221 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-vita/tests/conftest.py @@ -0,0 +1,99 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Test configuration for VitaBench instrumentation tests.""" + +import os + +import pytest + +from opentelemetry.instrumentation.vita import VitaInstrumentor +from opentelemetry.sdk._logs import LoggerProvider +from opentelemetry.sdk._logs.export import ( + InMemoryLogExporter, + SimpleLogRecordProcessor, +) +from opentelemetry.sdk.metrics import MeterProvider +from opentelemetry.sdk.metrics.export import InMemoryMetricReader +from opentelemetry.sdk.trace import TracerProvider +from opentelemetry.sdk.trace.export import SimpleSpanProcessor +from opentelemetry.sdk.trace.export.in_memory_span_exporter import ( + InMemorySpanExporter, +) + + +def pytest_configure(config: pytest.Config): + os.environ["OTEL_SEMCONV_STABILITY_OPT_IN"] = "gen_ai_latest_experimental" + + +# ==================== Exporters ==================== + + +@pytest.fixture(scope="function", name="span_exporter") +def fixture_span_exporter(): + exporter = InMemorySpanExporter() + yield exporter + + +@pytest.fixture(scope="function", name="log_exporter") +def fixture_log_exporter(): + exporter = InMemoryLogExporter() + yield exporter + + +@pytest.fixture(scope="function", name="metric_reader") +def fixture_metric_reader(): + reader = InMemoryMetricReader() + yield reader + + +# ==================== Providers ==================== + + +@pytest.fixture(scope="function", name="tracer_provider") +def fixture_tracer_provider(span_exporter): + provider = TracerProvider() + provider.add_span_processor(SimpleSpanProcessor(span_exporter)) + return provider + + +@pytest.fixture(scope="function", name="logger_provider") +def fixture_logger_provider(log_exporter): + provider = LoggerProvider() + provider.add_log_record_processor(SimpleLogRecordProcessor(log_exporter)) + return provider + + +@pytest.fixture(scope="function", name="meter_provider") +def fixture_meter_provider(metric_reader): + meter_provider = MeterProvider( + metric_readers=[metric_reader], + ) + return meter_provider + + +# ==================== Instrumentation ==================== + + +@pytest.fixture(scope="function") +def instrument(tracer_provider, logger_provider, meter_provider): + instrumentor = VitaInstrumentor() + instrumentor.instrument( + tracer_provider=tracer_provider, + logger_provider=logger_provider, + meter_provider=meter_provider, + skip_dep_check=True, + ) + yield instrumentor + instrumentor.uninstrument() diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-vita/tests/test_instrumentor.py b/instrumentation-loongsuite/loongsuite-instrumentation-vita/tests/test_instrumentor.py new file mode 100644 index 000000000..a6a2339f8 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-vita/tests/test_instrumentor.py @@ -0,0 +1,478 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Tests for VitaBench instrumentation. + +The suite exercises all execute.md hook points. External I/O is replaced at the +HTTP/tool boundary, while the Vita agent/orchestrator call chain runs through +the real framework methods. +""" + +from __future__ import annotations + +from types import SimpleNamespace +from unittest.mock import MagicMock, patch + +import pytest + +from opentelemetry.instrumentation.vita import VitaInstrumentor + + +FAKE_MODELS_CONFIG = { + "qwen-max": { + "base_url": "http://fake-api.example.com/v1/chat/completions", + "headers": {"Authorization": "Bearer test-key"}, + }, + "gpt-4": { + "base_url": "http://fake-api.example.com/v1/chat/completions", + "headers": {"Authorization": "Bearer test-key"}, + }, + "claude-3-opus": { + "base_url": "http://fake-api.example.com/v1/chat/completions", + "headers": {"Authorization": "Bearer test-key"}, + }, +} + + +def _make_openai_response(content=None, tool_calls=None, usage=None): + message = {"role": "assistant", "content": content} + if tool_calls: + message["tool_calls"] = tool_calls + return { + "id": "chatcmpl-test", + "model": "test-model", + "choices": [{"message": message, "finish_reason": "stop"}], + "usage": usage + or {"prompt_tokens": 100, "completion_tokens": 50, "total_tokens": 150}, + } + + +def _mock_requests_post(response_dict): + mock_resp = MagicMock() + mock_resp.status_code = 200 + mock_resp.json.return_value = response_dict + return mock_resp + + +def _tool_call_response(): + return _make_openai_response( + tool_calls=[ + { + "id": "call_1", + "type": "function", + "function": { + "name": "get_order", + "arguments": '{"order_id": "123"}', + }, + } + ], + usage={"prompt_tokens": 100, "completion_tokens": 20, "total_tokens": 120}, + ) + + +def _text_response(content="Order 123 has been delivered. ###STOP###"): + return _make_openai_response( + content=content, + usage={"prompt_tokens": 200, "completion_tokens": 30, "total_tokens": 230}, + ) + + +class FakeTool: + name = "get_order" + short_desc = "Get order details" + openai_schema = { + "type": "function", + "function": { + "name": "get_order", + "description": "Get order details", + "parameters": { + "type": "object", + "properties": {"order_id": {"type": "string"}}, + }, + }, + } + + +class FakeTools: + def __init__(self): + self.db = SimpleNamespace(time="2026-01-01 00:00:00") + self._tools = {"get_order": FakeTool()} + + def get_tools(self): + return self._tools + + def use_tool(self, tool_name, **kwargs): + return {"tool": tool_name, "arguments": kwargs, "status": "delivered"} + + def get_db_hash(self): + return "fake-db-hash" + + +class DeterministicUser: + def get_init_state(self, message_history=None): + return SimpleNamespace(messages=message_history or []) + + def generate_next_message(self, message, state): + from vita.data_model.message import UserMessage + + user_message = UserMessage(role="user", content="Check order 123") + state.messages.append(user_message) + return user_message, state + + +def _make_agent(): + from vita.agent.llm_agent import LLMAgent + + return LLMAgent( + tools=[FakeTool()], + domain_policy="You are helpful at {time}.", + llm="qwen-max", + llm_args={}, + time="2026-01-01 00:00:00", + language="english", + ) + + +def _make_orchestrator(): + from vita.environment.environment import Environment + from vita.orchestrator.orchestrator import Orchestrator + + return Orchestrator( + domain="delivery", + agent=_make_agent(), + user=DeterministicUser(), + environment=Environment(domain_name="delivery", tools=FakeTools()), + task=SimpleNamespace( + id="task_001", + instructions="Check order 123", + message_history=None, + ), + max_steps=6, + max_errors=3, + language="english", + ) + + +def _span_attrs(spans, name): + span = next(s for s in spans if s.name == name) + return dict(span.attributes) + + +class TestVitaInstrumentor: + def test_instrument_and_uninstrument( + self, tracer_provider, logger_provider, meter_provider + ): + instrumentor = VitaInstrumentor() + instrumentor.instrument( + tracer_provider=tracer_provider, + logger_provider=logger_provider, + meter_provider=meter_provider, + skip_dep_check=True, + ) + assert instrumentor._handler is not None + instrumentor.uninstrument() + assert instrumentor._handler is None + + def test_instrumentation_dependencies(self): + assert VitaInstrumentor().instrumentation_dependencies() == ( + "vita >= 0.0.1", + ) + + +class TestLLMSpan: + def test_llm_span_text_response(self, instrument, span_exporter): + from vita.data_model.message import UserMessage + from vita.utils.llm_utils import generate + + with patch("vita.utils.llm_utils.models", FAKE_MODELS_CONFIG), patch( + "requests.post", + return_value=_mock_requests_post( + _make_openai_response( + content="The order has been delivered.", + usage={ + "prompt_tokens": 150, + "completion_tokens": 30, + "total_tokens": 180, + }, + ) + ), + ): + result = generate( + model="qwen-max", + messages=[UserMessage(role="user", content="Where is my order?")], + ) + + assert result.content == "The order has been delivered." + spans = span_exporter.get_finished_spans() + attrs = _span_attrs(spans, "chat qwen-max") + assert attrs["gen_ai.operation.name"] == "chat" + assert attrs["gen_ai.span.kind"] == "LLM" + assert attrs["gen_ai.request.model"] == "qwen-max" + assert attrs["gen_ai.provider.name"] == "alibaba_cloud" + assert attrs["gen_ai.usage.input_tokens"] == 150 + assert attrs["gen_ai.usage.output_tokens"] == 30 + assert attrs["gen_ai.response.finish_reasons"] == ("stop",) + + def test_llm_span_tool_call_response(self, instrument, span_exporter): + from vita.data_model.message import UserMessage + from vita.utils.llm_utils import generate + + with patch("vita.utils.llm_utils.models", FAKE_MODELS_CONFIG), patch( + "requests.post", return_value=_mock_requests_post(_tool_call_response()) + ): + result = generate( + model="gpt-4", + messages=[UserMessage(role="user", content="Check my order")], + ) + + assert result.tool_calls is not None + attrs = _span_attrs(span_exporter.get_finished_spans(), "chat gpt-4") + assert attrs["gen_ai.response.finish_reasons"] == ("tool_calls",) + assert attrs["gen_ai.provider.name"] == "openai" + + def test_llm_span_captures_positional_tools(self, instrument, span_exporter): + from vita.data_model.message import UserMessage + from vita.utils.llm_utils import generate + + with patch("vita.utils.llm_utils.models", FAKE_MODELS_CONFIG), patch( + "requests.post", return_value=_mock_requests_post(_text_response("Done.")) + ): + generate( + "qwen-max", + [UserMessage(role="user", content="Check my order")], + [FakeTool()], + ) + + attrs = _span_attrs(span_exporter.get_finished_spans(), "chat qwen-max") + assert "gen_ai.tool.definitions" in attrs + assert "get_order" in attrs["gen_ai.tool.definitions"] + + +class TestToolSpan: + def test_tool_span_created(self, instrument, span_exporter): + from vita.data_model.message import ToolCall + from vita.environment.environment import Environment + + env = Environment(domain_name="delivery", tools=FakeTools()) + result = env.get_response( + ToolCall(id="tc_42", name="get_order", arguments={"order_id": "999"}) + ) + + assert result.content is not None + attrs = _span_attrs( + span_exporter.get_finished_spans(), "execute_tool get_order" + ) + assert attrs["gen_ai.operation.name"] == "execute_tool" + assert attrs["gen_ai.span.kind"] == "TOOL" + assert attrs["gen_ai.tool.name"] == "get_order" + assert attrs["gen_ai.tool.call.id"] == "tc_42" + + def test_tool_span_on_error(self, instrument, span_exporter): + from vita.data_model.message import ToolCall + from vita.environment.environment import Environment + + tools = FakeTools() + tools.use_tool = MagicMock(side_effect=RuntimeError("Tool failed")) + env = Environment(domain_name="delivery", tools=tools) + result = env.get_response( + ToolCall(id="tc_err", name="get_order", arguments={}) + ) + + assert result.error is True + tool_span = next( + s + for s in span_exporter.get_finished_spans() + if s.name == "execute_tool get_order" + ) + assert tool_span.status.status_code.name == "ERROR" + + +class TestAgentSpan: + def test_agent_span_created_for_llm_agent(self, instrument, span_exporter): + from vita.data_model.message import UserMessage + + agent = _make_agent() + state = agent.get_init_state([]) + + with patch("vita.utils.llm_utils.models", FAKE_MODELS_CONFIG), patch( + "requests.post", return_value=_mock_requests_post(_text_response("Sure.")) + ): + assistant_msg, _ = agent.generate_next_message( + UserMessage(role="user", content="I need help"), state + ) + + assert assistant_msg.content == "Sure." + spans = span_exporter.get_finished_spans() + agent_span = next(s for s in spans if s.name == "invoke_agent LLMAgent") + llm_span = next(s for s in spans if s.name == "chat qwen-max") + attrs = dict(agent_span.attributes) + assert attrs["gen_ai.operation.name"] == "invoke_agent" + assert attrs["gen_ai.span.kind"] == "AGENT" + assert attrs["gen_ai.agent.name"] == "LLMAgent" + assert attrs["gen_ai.request.model"] == "qwen-max" + assert llm_span.parent.span_id == agent_span.context.span_id + + def test_agent_span_created_for_llm_solo_agent(self, instrument, span_exporter): + from vita.agent.llm_agent import LLMSoloAgent + + agent = LLMSoloAgent( + tools=[FakeTool()], + domain_policy="unused", + llm="qwen-max", + llm_args={}, + time="2026-01-01 00:00:00", + language="english", + ) + state = agent.get_init_state([]) + + with patch("vita.utils.llm_utils.models", FAKE_MODELS_CONFIG), patch( + "requests.post", return_value=_mock_requests_post(_tool_call_response()) + ): + agent.generate_next_message(None, state) + + attrs = _span_attrs( + span_exporter.get_finished_spans(), "invoke_agent LLMSoloAgent" + ) + assert attrs["gen_ai.span.kind"] == "AGENT" + assert attrs["gen_ai.agent.name"] == "LLMSoloAgent" + + +class TestStepAndChainSpans: + def test_orchestrator_run_creates_chain_steps_agents_llms_and_tools( + self, instrument, span_exporter + ): + responses = [ + _mock_requests_post(_tool_call_response()), + _mock_requests_post(_text_response()), + ] + + with patch("vita.utils.llm_utils.models", FAKE_MODELS_CONFIG), patch( + "requests.post", side_effect=responses + ): + result = _make_orchestrator().run() + + assert result.termination_reason == "agent_stop" + spans = span_exporter.get_finished_spans() + chain = next(s for s in spans if s.name == "workflow delivery") + steps = sorted( + [s for s in spans if s.name == "react step"], key=lambda s: s.start_time + ) + agents = sorted( + [s for s in spans if s.name == "invoke_agent LLMAgent"], + key=lambda s: s.start_time, + ) + llms = sorted( + [s for s in spans if s.name == "chat qwen-max"], + key=lambda s: s.start_time, + ) + tools = [s for s in spans if s.name == "execute_tool get_order"] + + assert len(steps) == 2 + assert len(agents) == 2 + assert len(llms) == 2 + assert len(tools) == 1 + + chain_attrs = dict(chain.attributes) + assert chain_attrs["gen_ai.operation.name"] == "workflow" + assert chain_attrs["gen_ai.span.kind"] == "CHAIN" + assert chain_attrs["gen_ai.framework"] == "vitabench" + + assert dict(steps[0].attributes)["gen_ai.react.round"] == 1 + assert dict(steps[1].attributes)["gen_ai.react.round"] == 2 + for step in steps: + assert step.parent.span_id == chain.context.span_id + assert agents[0].parent.span_id == steps[0].context.span_id + assert agents[1].parent.span_id == steps[1].context.span_id + assert llms[0].parent.span_id == agents[0].context.span_id + assert llms[1].parent.span_id == agents[1].context.span_id + assert tools[0].parent.span_id == steps[0].context.span_id + + def test_open_step_fails_when_env_turn_raises(self, instrument, span_exporter): + with patch("vita.utils.llm_utils.models", FAKE_MODELS_CONFIG), patch( + "requests.post", return_value=_mock_requests_post(_tool_call_response()) + ), patch( + "vita.environment.environment.Environment.get_response", + side_effect=RuntimeError("env broke"), + ): + with pytest.raises(RuntimeError, match="env broke"): + _make_orchestrator().run() + + spans = span_exporter.get_finished_spans() + step = next(s for s in spans if s.name == "react step") + chain = next(s for s in spans if s.name == "workflow delivery") + step_attrs = dict(step.attributes) + assert step.status.status_code.name == "ERROR" + assert step_attrs["gen_ai.react.finish_reason"] == "error" + assert chain.status.status_code.name == "ERROR" + + +class TestEntrySpan: + def test_run_task_entry_wraps_orchestrator_trace(self, instrument, span_exporter): + from vita.run import run_task + + def fake_internal(**kwargs): + return _make_orchestrator().run() + + responses = [ + _mock_requests_post(_tool_call_response()), + _mock_requests_post(_text_response()), + ] + task = SimpleNamespace( + id="task_001", + instructions="Check order 123", + message_history=None, + ) + + with patch("vita.run._run_task_internal", side_effect=fake_internal), patch( + "vita.utils.llm_utils.models", FAKE_MODELS_CONFIG + ), patch("requests.post", side_effect=responses): + result = run_task("delivery", task, "llm_agent", "user_simulator") + + assert result.termination_reason == "agent_stop" + spans = span_exporter.get_finished_spans() + entry = next(s for s in spans if s.name == "enter_ai_application_system") + chain = next(s for s in spans if s.name == "workflow delivery") + attrs = dict(entry.attributes) + assert attrs["gen_ai.operation.name"] == "enter" + assert attrs["gen_ai.span.kind"] == "ENTRY" + assert attrs["gen_ai.framework"] == "vitabench" + assert "gen_ai.session.id" in attrs + assert chain.parent.span_id == entry.context.span_id + + +class TestProviderInference: + def test_common_provider_names(self, instrument, span_exporter): + from vita.data_model.message import UserMessage + from vita.utils.llm_utils import generate + + for model in ("gpt-4", "claude-3-opus", "qwen-max"): + with patch("vita.utils.llm_utils.models", FAKE_MODELS_CONFIG), patch( + "requests.post", + return_value=_mock_requests_post(_make_openai_response(content="Hi")), + ): + generate( + model=model, + messages=[UserMessage(role="user", content="Hi")], + ) + + providers = { + dict(s.attributes)["gen_ai.request.model"]: dict(s.attributes)[ + "gen_ai.provider.name" + ] + for s in span_exporter.get_finished_spans() + if s.name.startswith("chat ") + } + assert providers["gpt-4"] == "openai" + assert providers["claude-3-opus"] == "anthropic" + assert providers["qwen-max"] == "alibaba_cloud" From 4f2b0497a69eb5674cf476a2ffaa0019c3de0f93 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=89=A7=E6=80=9D?= Date: Thu, 7 May 2026 09:30:40 +0800 Subject: [PATCH 6/8] feat: support slop code Change-Id: Ieea04708467272866f5b7d9b905a2a648e6adb2d (cherry picked from commit 80e202c8e97f38a064ae2a36f4ce699827e60d12) Co-authored-by: 123liuziming <32130965+123liuziming@users.noreply.github.com> --- .../README.md | 32 +++ .../pyproject.toml | 61 +++++ .../instrumentation/slop_code/__init__.py | 211 ++++++++++++++++++ .../instrumentation/slop_code/package.py | 17 ++ .../instrumentation/slop_code/utils.py | 51 +++++ .../instrumentation/slop_code/version.py | 15 ++ .../slop_code/wrappers/__init__.py | 13 ++ .../slop_code/wrappers/agent.py | 91 ++++++++ .../slop_code/wrappers/entry.py | 58 +++++ .../instrumentation/slop_code/wrappers/llm.py | 104 +++++++++ .../slop_code/wrappers/step.py | 110 +++++++++ .../slop_code/wrappers/task.py | 91 ++++++++ .../slop_code/wrappers/workflow.py | 120 ++++++++++ .../test-requirements.txt | 8 + .../tests/__init__.py | 0 .../tests/conftest.py | 209 +++++++++++++++++ .../tests/test_agent_span.py | 102 +++++++++ .../tests/test_entry_span.py | 74 ++++++ .../tests/test_hierarchy.py | 118 ++++++++++ .../tests/test_llm_span.py | 142 ++++++++++++ .../tests/test_step_span.py | 133 +++++++++++ .../tests/test_task_span.py | 110 +++++++++ .../tests/test_workflow_span.py | 117 ++++++++++ 23 files changed, 1987 insertions(+) create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-slop-code/README.md create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-slop-code/pyproject.toml create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/__init__.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/package.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/utils.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/version.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/__init__.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/agent.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/entry.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/llm.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/step.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/task.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/workflow.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-slop-code/test-requirements.txt create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/__init__.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/conftest.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_agent_span.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_entry_span.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_hierarchy.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_llm_span.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_step_span.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_task_span.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_workflow_span.py diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/README.md b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/README.md new file mode 100644 index 000000000..4d4f4d7b1 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/README.md @@ -0,0 +1,32 @@ +# LoongSuite slop-code-bench Instrumentation + +OpenTelemetry instrumentation for the [slop-code-bench](https://github.com/SprocketLab/slop-code-bench) benchmark orchestrator. + +## Span Tree + +``` +ENTRY "slop-code.enter" +└── CHAIN "workflow.{problem_name}" + ├── TASK "task.{checkpoint_name}" + │ └── AGENT "agent.{agent_type}" + │ ├── STEP "react.step.{N}" [MiniSWE only] + │ └── ... + ├── TASK "task.{checkpoint_name}" + │ └── AGENT "agent.{agent_type}" + └── ... +LLM "chat {model_name}" [Rubric Judge] +``` + +## Installation + +```bash +pip install loongsuite-instrumentation-slop-code +``` + +## Usage + +```python +from opentelemetry.instrumentation.slop_code import SlopCodeInstrumentor + +SlopCodeInstrumentor().instrument() +``` diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/pyproject.toml b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/pyproject.toml new file mode 100644 index 000000000..b443381c2 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/pyproject.toml @@ -0,0 +1,61 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "loongsuite-instrumentation-slop-code" +dynamic = ["version"] +description = "LoongSuite slop-code-bench instrumentation" +readme = "README.md" +license = "Apache-2.0" +requires-python = ">=3.10,<4" +authors = [ + { name = "Zhiyong Liu", email = "liuzhiyong.lzy@alibaba-inc.com" }, + { name = "OpenTelemetry Authors", email = "cncf-opentelemetry-contributors@lists.cncf.io" }, +] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Developers", + "License :: OSI Approved :: Apache Software License", + "Programming Language :: Python", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", +] +dependencies = [ + "opentelemetry-api >= 1.37.0", + "opentelemetry-instrumentation >= 0.58b0", + "opentelemetry-semantic-conventions >= 0.58b0", + "wrapt >= 1.14.0, < 2.0.0", + "opentelemetry-util-genai >= 0.3b0.dev0", +] + +[project.optional-dependencies] +instruments = [ + "slop-code-bench >= 0.1", +] +test = [ + "pytest", + "pytest-asyncio", + "pytest-forked", + "opentelemetry-sdk", +] + +[project.entry-points.opentelemetry_instrumentor] +slop_code = "opentelemetry.instrumentation.slop_code:SlopCodeInstrumentor" + +[project.urls] +Homepage = "https://github.com/alibaba/loongsuite-python-agent/tree/main/instrumentation-loongsuite/loongsuite-instrumentation-slop-code" +Repository = "https://github.com/alibaba/loongsuite-python-agent" + +[tool.hatch.version] +path = "src/opentelemetry/instrumentation/slop_code/version.py" + +[tool.hatch.build.targets.sdist] +include = [ + "/src", + "/tests", +] + +[tool.hatch.build.targets.wheel] +packages = ["src/opentelemetry"] diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/__init__.py b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/__init__.py new file mode 100644 index 000000000..973cd969e --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/__init__.py @@ -0,0 +1,211 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +OpenTelemetry slop-code-bench Instrumentation + +Instruments the slop-code benchmark orchestrator lifecycle: +- ENTRY: run_agent (CLI entrypoint) +- CHAIN/workflow: run_agent_on_problem (per-problem) +- TASK: AgentRunner._run_checkpoint (per-checkpoint) +- AGENT: Agent.run_checkpoint (concrete agent invocation) +- STEP: MiniSWEAgent.agent_step (ReAct iteration) +- LLM: grade_file_async (Rubric Judge) +""" + +import logging +from typing import Any, Collection + +from wrapt import wrap_function_wrapper + +from opentelemetry import trace as trace_api +from opentelemetry.instrumentation.instrumentor import BaseInstrumentor +from opentelemetry.instrumentation.slop_code.package import _instruments +from opentelemetry.instrumentation.slop_code.version import __version__ +from opentelemetry.instrumentation.slop_code.wrappers.agent import ( + _AgentRunCheckpointWrapper, +) +from opentelemetry.instrumentation.slop_code.wrappers.entry import ( + _EntryWrapper, +) +from opentelemetry.instrumentation.slop_code.wrappers.llm import ( + _RubricGradeWrapper, +) +from opentelemetry.instrumentation.slop_code.wrappers.step import ( + _MiniSWEStepWrapper, +) +from opentelemetry.instrumentation.slop_code.wrappers.task import ( + _TaskRunCheckpointWrapper, +) +from opentelemetry.instrumentation.slop_code.wrappers.workflow import ( + _WorkflowWrapper, +) +from opentelemetry.instrumentation.utils import unwrap + +logger = logging.getLogger(__name__) + +__all__ = ["SlopCodeInstrumentor", "__version__"] + +_MODULE_ENTRY = "slop_code.entrypoints.commands.run_agent" +_MODULE_WORKER = "slop_code.entrypoints.problem_runner.worker" +# slop_code.entrypoints.problem_runner.driver re-imports +# `run_agent_on_problem` via `from .worker import run_agent_on_problem` +# at package-load time, capturing the original function reference. Because +# our wrap happens after that bind, we must additionally replace the local +# binding inside `driver` itself, otherwise the worker subprocess still +# calls the un-wrapped original and the CHAIN span never fires. +_MODULE_DRIVER = "slop_code.entrypoints.problem_runner.driver" +_MODULE_RUNNER = "slop_code.agent_runner.runner" +_MODULE_AGENT = "slop_code.agent_runner.agent" +_MODULE_MINISWE = "slop_code.agent_runner.agents.miniswe" +_MODULE_RUBRIC = "slop_code.metrics.rubric.router" + + +class SlopCodeInstrumentor(BaseInstrumentor): + """OpenTelemetry instrumentor for slop-code-bench framework.""" + + def instrumentation_dependencies(self) -> Collection[str]: + return _instruments + + def _instrument(self, **kwargs: Any) -> None: + tracer_provider = kwargs.get("tracer_provider") + tracer = trace_api.get_tracer( + __name__, + __version__, + tracer_provider=tracer_provider, + ) + + # 3.1 ENTRY span: run_agent + try: + wrap_function_wrapper( + module=_MODULE_ENTRY, + name="run_agent", + wrapper=_EntryWrapper(tracer), + ) + except Exception as e: + logger.warning(f"Could not wrap run_agent: {e}") + + # 3.2 CHAIN span: run_agent_on_problem + workflow_wrapper = _WorkflowWrapper(tracer) + try: + wrap_function_wrapper( + module=_MODULE_WORKER, + name="run_agent_on_problem", + wrapper=workflow_wrapper, + ) + except Exception as e: + logger.warning(f"Could not wrap run_agent_on_problem: {e}") + # Also wrap the re-bound name inside driver. driver.py imports + # run_agent_on_problem at module-load time via `from .worker import ...`, + # so the local name escapes our worker-module patch. The worker + # subprocess inherits this stale reference via fork(), and CHAIN + # spans never fire unless we patch the local re-bind too. + try: + wrap_function_wrapper( + module=_MODULE_DRIVER, + name="run_agent_on_problem", + wrapper=workflow_wrapper, + ) + except Exception as e: + logger.warning(f"Could not wrap driver.run_agent_on_problem: {e}") + + # 3.3 TASK span: AgentRunner._run_checkpoint + try: + wrap_function_wrapper( + module=_MODULE_RUNNER, + name="AgentRunner._run_checkpoint", + wrapper=_TaskRunCheckpointWrapper(tracer), + ) + except Exception as e: + logger.warning(f"Could not wrap AgentRunner._run_checkpoint: {e}") + + # 3.4 AGENT span: Agent.run_checkpoint + try: + wrap_function_wrapper( + module=_MODULE_AGENT, + name="Agent.run_checkpoint", + wrapper=_AgentRunCheckpointWrapper(tracer), + ) + except Exception as e: + logger.warning(f"Could not wrap Agent.run_checkpoint: {e}") + + # 3.5 STEP span: MiniSWEAgent.agent_step + try: + wrap_function_wrapper( + module=_MODULE_MINISWE, + name="MiniSWEAgent.agent_step", + wrapper=_MiniSWEStepWrapper(tracer), + ) + except Exception as e: + logger.debug(f"Could not wrap MiniSWEAgent.agent_step: {e}") + + # 3.6 LLM span: grade_file_async + try: + wrap_function_wrapper( + module=_MODULE_RUBRIC, + name="grade_file_async", + wrapper=_RubricGradeWrapper(tracer), + ) + except Exception as e: + logger.debug(f"Could not wrap grade_file_async: {e}") + + def _uninstrument(self, **kwargs: Any) -> None: + try: + import slop_code.entrypoints.commands.run_agent as mod_entry + + unwrap(mod_entry, "run_agent") + except Exception: + pass + + try: + import slop_code.entrypoints.problem_runner.worker as mod_worker + + unwrap(mod_worker, "run_agent_on_problem") + except Exception: + pass + + try: + import slop_code.entrypoints.problem_runner.driver as mod_driver + + unwrap(mod_driver, "run_agent_on_problem") + except Exception: + pass + + try: + import slop_code.agent_runner.runner as mod_runner + + unwrap(mod_runner.AgentRunner, "_run_checkpoint") + except Exception: + pass + + try: + import slop_code.agent_runner.agent as mod_agent + + unwrap(mod_agent.Agent, "run_checkpoint") + except Exception: + pass + + try: + import slop_code.agent_runner.agents.miniswe as mod_miniswe + + unwrap(mod_miniswe.MiniSWEAgent, "agent_step") + except Exception: + pass + + try: + import slop_code.metrics.rubric.router as mod_rubric + + unwrap(mod_rubric, "grade_file_async") + except Exception: + pass diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/package.py b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/package.py new file mode 100644 index 000000000..13b6fe785 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/package.py @@ -0,0 +1,17 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +_instruments = ("slop-code-bench >= 0.1",) + +_supports_metrics = True diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/utils.py b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/utils.py new file mode 100644 index 000000000..ee7fce73f --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/utils.py @@ -0,0 +1,51 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Utility functions for slop-code instrumentation.""" + +from typing import Any, Optional + +from opentelemetry.trace import Span + +SYSTEM_NAME = "slop-code" +MAX_ATTR_LEN = 1024 + + +def safe_get(obj: Any, attr: str, default: Any = None) -> Any: + """Safely get an attribute from an object, returning default on failure.""" + try: + return getattr(obj, attr, default) + except Exception: + return default + + +def safe_get_nested(obj: Any, *attrs: str, default: Any = None) -> Any: + """Safely traverse nested attributes.""" + current = obj + for attr in attrs: + try: + current = getattr(current, attr) + if current is None: + return default + except (AttributeError, TypeError): + return default + return current + + +def set_optional_attr(span: Span, key: str, value: Optional[Any]) -> None: + """Set a span attribute only if value is not None.""" + if value is not None: + if isinstance(value, str) and len(value) > MAX_ATTR_LEN: + value = value[:MAX_ATTR_LEN] + span.set_attribute(key, value) diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/version.py b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/version.py new file mode 100644 index 000000000..7bee975f0 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/version.py @@ -0,0 +1,15 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +__version__ = "0.5.0.dev" diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/__init__.py b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/__init__.py new file mode 100644 index 000000000..b0a6f4284 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/__init__.py @@ -0,0 +1,13 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/agent.py b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/agent.py new file mode 100644 index 000000000..94cb2b88a --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/agent.py @@ -0,0 +1,91 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""AGENT span wrapper for Agent.run_checkpoint.""" + +import logging + +from opentelemetry import trace as trace_api +from opentelemetry.instrumentation.slop_code.utils import ( + SYSTEM_NAME, + safe_get, + safe_get_nested, + set_optional_attr, +) +from opentelemetry.semconv._incubating.attributes import gen_ai_attributes +from opentelemetry.trace import SpanKind, Status, StatusCode +from opentelemetry.util.genai.extended_semconv import gen_ai_extended_attributes + +logger = logging.getLogger(__name__) + + +class _AgentRunCheckpointWrapper: + """Wrapper for Agent.run_checkpoint to create AGENT span.""" + + def __init__(self, tracer: trace_api.Tracer): + self._tracer = tracer + + def __call__(self, wrapped, instance, args, kwargs): + agent_name = type(instance).__name__ + problem_name = safe_get(instance, "problem_name", "unknown") + + span_name = f"agent.{agent_name}" + + attrs = { + gen_ai_attributes.GEN_AI_OPERATION_NAME: "invoke_agent", + gen_ai_attributes.GEN_AI_SYSTEM: SYSTEM_NAME, + gen_ai_extended_attributes.GEN_AI_SPAN_KIND: gen_ai_extended_attributes.GenAiSpanKindValues.AGENT.value, + "gen_ai.agent.name": agent_name, + "slop_code.problem.name": str(problem_name), + } + + with self._tracer.start_as_current_span( + name=span_name, + kind=SpanKind.INTERNAL, + attributes=attrs, + ) as span: + try: + result = wrapped(*args, **kwargs) + + # Extract after-call attributes from result + if result is not None: + usage = safe_get(result, "usage") + if usage is not None: + net_tokens = safe_get(usage, "net_tokens") + if net_tokens is not None: + set_optional_attr( + span, + gen_ai_attributes.GEN_AI_USAGE_INPUT_TOKENS, + safe_get(net_tokens, "input"), + ) + set_optional_attr( + span, + gen_ai_attributes.GEN_AI_USAGE_OUTPUT_TOKENS, + safe_get(net_tokens, "output"), + ) + cost = safe_get(usage, "cost") + set_optional_attr(span, "slop_code.usage.cost", cost) + steps = safe_get(usage, "steps") + set_optional_attr(span, "slop_code.usage.steps", steps) + + elapsed = safe_get(result, "elapsed") + set_optional_attr(span, "slop_code.elapsed_seconds", elapsed) + + span.set_status(Status(StatusCode.OK)) + return result + except Exception as e: + span.record_exception(e) + span.set_status(Status(StatusCode.ERROR, str(e))) + span.set_attribute("error.type", type(e).__name__) + raise diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/entry.py b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/entry.py new file mode 100644 index 000000000..d31e666f1 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/entry.py @@ -0,0 +1,58 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""ENTRY span wrapper for slop_code.entrypoints.commands.run_agent.run_agent.""" + +import logging + +from opentelemetry import trace as trace_api +from opentelemetry.instrumentation.slop_code.utils import ( + SYSTEM_NAME, + safe_get, + safe_get_nested, + set_optional_attr, +) +from opentelemetry.semconv._incubating.attributes import gen_ai_attributes +from opentelemetry.trace import SpanKind, Status, StatusCode +from opentelemetry.util.genai.extended_semconv import gen_ai_extended_attributes + +logger = logging.getLogger(__name__) + + +class _EntryWrapper: + """Wrapper for run_agent to create ENTRY span.""" + + def __init__(self, tracer: trace_api.Tracer): + self._tracer = tracer + + def __call__(self, wrapped, instance, args, kwargs): + span_name = "slop-code.enter" + + with self._tracer.start_as_current_span( + name=span_name, + kind=SpanKind.INTERNAL, + attributes={ + gen_ai_attributes.GEN_AI_OPERATION_NAME: "enter", + gen_ai_attributes.GEN_AI_SYSTEM: SYSTEM_NAME, + gen_ai_extended_attributes.GEN_AI_SPAN_KIND: gen_ai_extended_attributes.GenAiSpanKindValues.ENTRY.value, + }, + ) as span: + try: + result = wrapped(*args, **kwargs) + span.set_status(Status(StatusCode.OK)) + return result + except Exception as e: + span.record_exception(e) + span.set_status(Status(StatusCode.ERROR, str(e))) + raise diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/llm.py b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/llm.py new file mode 100644 index 000000000..0aaba20b8 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/llm.py @@ -0,0 +1,104 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""LLM span wrapper for grade_file_async (Rubric Judge).""" + +import logging + +from opentelemetry import trace as trace_api +from opentelemetry.instrumentation.slop_code.utils import ( + SYSTEM_NAME, + set_optional_attr, +) +from opentelemetry.semconv._incubating.attributes import gen_ai_attributes +from opentelemetry.trace import SpanKind, Status, StatusCode +from opentelemetry.util.genai.extended_semconv import gen_ai_extended_attributes + +logger = logging.getLogger(__name__) + + +class _RubricGradeWrapper: + """Wrapper for grade_file_async to create LLM span.""" + + def __init__(self, tracer: trace_api.Tracer): + self._tracer = tracer + + async def __call__(self, wrapped, instance, args, kwargs): + # grade_file_async(prompt_prefix, criteria_text, file_name, model, provider, temperature, ...) + model = kwargs.get("model") or (args[3] if len(args) > 3 else "unknown") + provider = kwargs.get("provider") or (args[4] if len(args) > 4 else None) + temperature = kwargs.get("temperature") or (args[5] if len(args) > 5 else None) + + # Determine system name from provider + system_name = SYSTEM_NAME + if provider is not None: + provider_val = provider.value if hasattr(provider, "value") else str(provider) + system_name = provider_val.lower() + + span_name = f"chat {model}" + + attrs = { + gen_ai_attributes.GEN_AI_OPERATION_NAME: "chat", + gen_ai_attributes.GEN_AI_SYSTEM: system_name, + gen_ai_extended_attributes.GEN_AI_SPAN_KIND: gen_ai_extended_attributes.GenAiSpanKindValues.LLM.value, + gen_ai_attributes.GEN_AI_REQUEST_MODEL: str(model), + } + + if temperature is not None: + attrs[gen_ai_attributes.GEN_AI_REQUEST_TEMPERATURE] = float(temperature) + + with self._tracer.start_as_current_span( + name=span_name, + kind=SpanKind.CLIENT, + attributes=attrs, + ) as span: + try: + result = await wrapped(*args, **kwargs) + + # result is tuple[list[dict], dict[str, Any]] + if isinstance(result, tuple) and len(result) >= 2: + response_data = result[1] + if isinstance(response_data, dict): + _set_usage_from_response(span, response_data) + response_id = response_data.get("id") + set_optional_attr(span, "gen_ai.response.id", response_id) + + span.set_status(Status(StatusCode.OK)) + return result + except Exception as e: + span.record_exception(e) + span.set_status(Status(StatusCode.ERROR, str(e))) + raise + + +def _set_usage_from_response(span, response_data: dict) -> None: + """Extract and set token usage attributes from response_data.""" + usage = response_data.get("usage") + if not isinstance(usage, dict): + return + + # OpenRouter format: prompt_tokens / completion_tokens + # Bedrock format (normalized): input_tokens / output_tokens + input_tokens = usage.get("prompt_tokens") or usage.get("input_tokens") + output_tokens = usage.get("completion_tokens") or usage.get("output_tokens") + + set_optional_attr(span, gen_ai_attributes.GEN_AI_USAGE_INPUT_TOKENS, input_tokens) + set_optional_attr(span, gen_ai_attributes.GEN_AI_USAGE_OUTPUT_TOKENS, output_tokens) + + # Cache tokens (OpenRouter specific) + cache_read = usage.get("cache_read_input_tokens") + set_optional_attr(span, gen_ai_extended_attributes.GEN_AI_USAGE_CACHE_READ_INPUT_TOKENS, cache_read) + + cache_creation = usage.get("cache_creation_input_tokens") + set_optional_attr(span, gen_ai_extended_attributes.GEN_AI_USAGE_CACHE_CREATION_INPUT_TOKENS, cache_creation) diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/step.py b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/step.py new file mode 100644 index 000000000..93219fe89 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/step.py @@ -0,0 +1,110 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""STEP span wrapper for MiniSWEAgent.agent_step.""" + +import logging + +from opentelemetry import trace as trace_api +from opentelemetry.instrumentation.slop_code.utils import ( + SYSTEM_NAME, + safe_get, + safe_get_nested, + set_optional_attr, +) +from opentelemetry.semconv._incubating.attributes import gen_ai_attributes +from opentelemetry.trace import SpanKind, Status, StatusCode +from opentelemetry.util.genai.extended_semconv import gen_ai_extended_attributes + +logger = logging.getLogger(__name__) + + +class _MiniSWEStepWrapper: + """Wrapper for MiniSWEAgent.agent_step to create STEP span.""" + + def __init__(self, tracer: trace_api.Tracer): + self._tracer = tracer + + def __call__(self, wrapped, instance, args, kwargs): + # Determine current step number (1-based) + usage = safe_get(instance, "usage") + current_steps = safe_get(usage, "steps", 0) if usage else 0 + step_num = current_steps + 1 + + span_name = f"react.step.{step_num}" + + attrs = { + gen_ai_attributes.GEN_AI_OPERATION_NAME: "react", + gen_ai_attributes.GEN_AI_SYSTEM: SYSTEM_NAME, + gen_ai_extended_attributes.GEN_AI_SPAN_KIND: gen_ai_extended_attributes.GenAiSpanKindValues.STEP.value, + gen_ai_extended_attributes.GEN_AI_REACT_ROUND: step_num, + } + + with self._tracer.start_as_current_span( + name=span_name, + kind=SpanKind.INTERNAL, + attributes=attrs, + ) as span: + try: + result = wrapped(*args, **kwargs) + + # Extract token usage from result if available + if isinstance(result, dict): + token_usage = result.get("token_usage") + if token_usage is not None: + set_optional_attr( + span, + gen_ai_attributes.GEN_AI_USAGE_INPUT_TOKENS, + safe_get(token_usage, "input"), + ) + set_optional_attr( + span, + gen_ai_attributes.GEN_AI_USAGE_OUTPUT_TOKENS, + safe_get(token_usage, "output"), + ) + set_optional_attr( + span, + gen_ai_extended_attributes.GEN_AI_USAGE_CACHE_READ_INPUT_TOKENS, + safe_get(token_usage, "cache_read"), + ) + set_optional_attr( + span, + gen_ai_extended_attributes.GEN_AI_USAGE_CACHE_CREATION_INPUT_TOKENS, + safe_get(token_usage, "cache_write"), + ) + step_cost = result.get("step_cost") + set_optional_attr(span, "slop_code.step.cost", step_cost) + elif result is not None: + # Result might be a tuple or object; try attribute access + token_usage = safe_get(result, "token_usage") + if token_usage is not None: + set_optional_attr( + span, + gen_ai_attributes.GEN_AI_USAGE_INPUT_TOKENS, + safe_get(token_usage, "input"), + ) + set_optional_attr( + span, + gen_ai_attributes.GEN_AI_USAGE_OUTPUT_TOKENS, + safe_get(token_usage, "output"), + ) + + span.set_status(Status(StatusCode.OK)) + span.set_attribute(gen_ai_extended_attributes.GEN_AI_REACT_FINISH_REASON, "stop") + return result + except Exception as e: + span.record_exception(e) + span.set_status(Status(StatusCode.ERROR, str(e))) + span.set_attribute(gen_ai_extended_attributes.GEN_AI_REACT_FINISH_REASON, "error") + raise diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/task.py b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/task.py new file mode 100644 index 000000000..b0f60a4fc --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/task.py @@ -0,0 +1,91 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""TASK span wrapper for AgentRunner._run_checkpoint.""" + +import logging + +from opentelemetry import trace as trace_api +from opentelemetry.instrumentation.slop_code.utils import ( + SYSTEM_NAME, + safe_get, + safe_get_nested, + set_optional_attr, +) +from opentelemetry.semconv._incubating.attributes import gen_ai_attributes +from opentelemetry.trace import SpanKind, Status, StatusCode +from opentelemetry.util.genai.extended_semconv import gen_ai_extended_attributes + +logger = logging.getLogger(__name__) + + +class _TaskRunCheckpointWrapper: + """Wrapper for AgentRunner._run_checkpoint to create TASK span.""" + + def __init__(self, tracer: trace_api.Tracer): + self._tracer = tracer + + def __call__(self, wrapped, instance, args, kwargs): + # _run_checkpoint(self, checkpoint, checkpoint_save_dir, is_first_checkpoint) + checkpoint = args[0] if args else kwargs.get("checkpoint") + is_first_checkpoint = args[2] if len(args) > 2 else kwargs.get("is_first_checkpoint", False) + + checkpoint_name = safe_get(checkpoint, "name", "unknown") + checkpoint_order = safe_get(checkpoint, "order") + + span_name = f"task.{checkpoint_name}" + + attrs = { + gen_ai_attributes.GEN_AI_OPERATION_NAME: "run_task", + gen_ai_attributes.GEN_AI_SYSTEM: SYSTEM_NAME, + gen_ai_extended_attributes.GEN_AI_SPAN_KIND: "TASK", + "slop_code.checkpoint.name": str(checkpoint_name), + } + + if checkpoint_order is not None: + attrs["slop_code.checkpoint.order"] = checkpoint_order + attrs["slop_code.is_first_checkpoint"] = bool(is_first_checkpoint) + + with self._tracer.start_as_current_span( + name=span_name, + kind=SpanKind.INTERNAL, + attributes=attrs, + ) as span: + try: + result = wrapped(*args, **kwargs) + + # Extract after-call attributes from summary + if result is not None: + had_error = safe_get(result, "had_error") + set_optional_attr(span, "slop_code.had_error", had_error) + + passed_policy = safe_get(result, "passed_policy") + set_optional_attr(span, "slop_code.passed_policy", passed_policy) + + # Token usage from agent + agent = safe_get(instance, "agent") + if agent is not None: + net_tokens = safe_get_nested(agent, "usage", "net_tokens") + if net_tokens is not None: + input_tokens = safe_get(net_tokens, "input") + output_tokens = safe_get(net_tokens, "output") + set_optional_attr(span, gen_ai_attributes.GEN_AI_USAGE_INPUT_TOKENS, input_tokens) + set_optional_attr(span, gen_ai_attributes.GEN_AI_USAGE_OUTPUT_TOKENS, output_tokens) + + span.set_status(Status(StatusCode.OK)) + return result + except Exception as e: + span.record_exception(e) + span.set_status(Status(StatusCode.ERROR, str(e))) + raise diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/workflow.py b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/workflow.py new file mode 100644 index 000000000..4793d4286 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/src/opentelemetry/instrumentation/slop_code/wrappers/workflow.py @@ -0,0 +1,120 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""CHAIN/workflow span wrapper for run_agent_on_problem.""" + +import logging + +from opentelemetry import trace as trace_api +from opentelemetry.instrumentation.slop_code.utils import ( + SYSTEM_NAME, + safe_get, + safe_get_nested, + set_optional_attr, +) +from opentelemetry.semconv._incubating.attributes import gen_ai_attributes +from opentelemetry.trace import SpanKind, Status, StatusCode +from opentelemetry.util.genai.extended_semconv import gen_ai_extended_attributes + +logger = logging.getLogger(__name__) + + +class _WorkflowWrapper: + """Wrapper for run_agent_on_problem to create workflow (CHAIN) span.""" + + def __init__(self, tracer: trace_api.Tracer): + self._tracer = tracer + + def __call__(self, wrapped, instance, args, kwargs): + # run_agent_on_problem(problem_config, problem_name, config, progress_queue, output_path) + problem_name = args[1] if len(args) > 1 else kwargs.get("problem_name", "unknown") + config = args[2] if len(args) > 2 else kwargs.get("config") + + span_name = f"workflow.{problem_name}" + + attrs = { + gen_ai_attributes.GEN_AI_OPERATION_NAME: "workflow", + gen_ai_attributes.GEN_AI_SYSTEM: SYSTEM_NAME, + gen_ai_extended_attributes.GEN_AI_SPAN_KIND: "CHAIN", + "slop_code.problem.name": str(problem_name), + } + + # Extract optional attributes from config + if config is not None: + model_name = safe_get_nested(config, "model_def", "name") + set_optional_attr_dict(attrs, gen_ai_attributes.GEN_AI_REQUEST_MODEL, model_name) + + agent_type = safe_get_nested(config, "agent_config", "type") + set_optional_attr_dict(attrs, "slop_code.agent.type", agent_type) + + pass_policy = safe_get_nested(config, "pass_policy", "value") + if pass_policy is None: + pass_policy_obj = safe_get(config, "pass_policy") + if pass_policy_obj is not None and hasattr(pass_policy_obj, "value"): + pass_policy = pass_policy_obj.value + set_optional_attr_dict(attrs, "slop_code.pass_policy", pass_policy) + + try: + with self._tracer.start_as_current_span( + name=span_name, + kind=SpanKind.INTERNAL, + attributes={k: v for k, v in attrs.items() if v is not None}, + ) as span: + try: + result = wrapped(*args, **kwargs) + + if isinstance(result, dict): + summary = result.get("summary") + if isinstance(summary, dict): + set_optional_attr( + span, "slop_code.state", summary.get("state") + ) + set_optional_attr( + span, + "slop_code.passed_policy", + summary.get("passed_policy"), + ) + + span.set_status(Status(StatusCode.OK)) + return result + except Exception as e: + span.record_exception(e) + span.set_status(Status(StatusCode.ERROR, str(e))) + raise + finally: + # Flush AFTER the `with` block so the workflow span itself + # is `on_end`-delivered to the SpanProcessor before we ask it + # to drain. run_agent_on_problem is the last meaningful work + # item inside the per-problem worker subprocess; once it + # returns, the process is reaped by ProcessPoolExecutor's + # shutdown which can short-circuit BatchSpanProcessor's + # atexit handler. Without this explicit flush the CHAIN span + # (and the tail batch of TASK/AGENT/STEP spans) gets dropped. + try: + provider = trace_api.get_tracer_provider() + flush = getattr(provider, "force_flush", None) + if callable(flush): + flush(timeout_millis=5000) + except Exception as flush_err: # noqa: BLE001 + logger.debug( + "force_flush after workflow span failed: %s", flush_err + ) + + +def set_optional_attr_dict(attrs: dict, key: str, value) -> None: + """Add to attrs dict only if value is not None.""" + if value is not None: + if isinstance(value, str) and len(value) > 1024: + value = value[:1024] + attrs[key] = value diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/test-requirements.txt b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/test-requirements.txt new file mode 100644 index 000000000..9facd6bc9 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/test-requirements.txt @@ -0,0 +1,8 @@ +pytest +pytest-asyncio +pytest-forked==1.6.0 +opentelemetry-api +opentelemetry-sdk +opentelemetry-instrumentation +opentelemetry-semantic-conventions +wrapt diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/__init__.py b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/conftest.py b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/conftest.py new file mode 100644 index 000000000..dcda695d0 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/conftest.py @@ -0,0 +1,209 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Test configuration for slop-code instrumentation tests.""" + +import os +import sys +import types +from unittest.mock import MagicMock + +import pytest + +os.environ["OTEL_SEMCONV_STABILITY_OPT_IN"] = "gen_ai_latest_experimental" + + +def _make_module(name): + """Create a real module object.""" + mod = types.ModuleType(name) + mod.__package__ = name.rsplit(".", 1)[0] if "." in name else name + return mod + + +def _create_mock_slop_code_modules(): + """Create mock modules for slop_code so instrumentation can wrap them.""" + # Create all parent modules + mod_slop_code = _make_module("slop_code") + mod_entrypoints = _make_module("slop_code.entrypoints") + mod_commands = _make_module("slop_code.entrypoints.commands") + mod_run_agent = _make_module("slop_code.entrypoints.commands.run_agent") + mod_problem_runner = _make_module("slop_code.entrypoints.problem_runner") + mod_worker = _make_module("slop_code.entrypoints.problem_runner.worker") + mod_driver = _make_module("slop_code.entrypoints.problem_runner.driver") + mod_agent_runner = _make_module("slop_code.agent_runner") + mod_runner = _make_module("slop_code.agent_runner.runner") + mod_agent = _make_module("slop_code.agent_runner.agent") + mod_agents = _make_module("slop_code.agent_runner.agents") + mod_miniswe = _make_module("slop_code.agent_runner.agents.miniswe") + mod_metrics = _make_module("slop_code.metrics") + mod_rubric = _make_module("slop_code.metrics.rubric") + mod_router = _make_module("slop_code.metrics.rubric.router") + + # --- ENTRY: run_agent --- + def run_agent(*args, **kwargs): + return {"status": "completed"} + + mod_run_agent.run_agent = run_agent + + # --- WORKFLOW: run_agent_on_problem --- + def run_agent_on_problem(*args, **kwargs): + return {"summary": {"state": "completed", "passed_policy": True}} + + mod_worker.run_agent_on_problem = run_agent_on_problem + # driver re-imports the worker name at module load time. This mock mirrors + # the same pattern so the instrumentor's driver-side patch has a target. + mod_driver.run_agent_on_problem = run_agent_on_problem + + # --- TASK: AgentRunner._run_checkpoint --- + class AgentRunner: + def __init__(self): + self.agent = MagicMock() + self.agent.usage = MagicMock() + self.agent.usage.net_tokens = MagicMock() + self.agent.usage.net_tokens.input = 100 + self.agent.usage.net_tokens.output = 50 + + def _run_checkpoint(self, checkpoint, checkpoint_save_dir, is_first_checkpoint=False): + result = MagicMock() + result.had_error = False + result.passed_policy = True + return result + + mod_runner.AgentRunner = AgentRunner + + # --- AGENT: Agent.run_checkpoint --- + class Agent: + def __init__(self, problem_name="test_problem"): + self.problem_name = problem_name + self.usage = MagicMock() + self.usage.net_tokens = MagicMock() + self.usage.net_tokens.input = 100 + self.usage.net_tokens.output = 50 + self.usage.steps = 0 + self.usage.cost = 0.05 + + def run_checkpoint(self, task): + result = MagicMock() + result.usage = self.usage + result.elapsed = 10.5 + return result + + mod_agent.Agent = Agent + + # --- STEP: MiniSWEAgent.agent_step --- + class MiniSWEAgent(Agent): + def __init__(self, problem_name="test_problem"): + super().__init__(problem_name) + + def agent_step(self): + return { + "token_usage": MagicMock(input=200, output=80, cache_read=50, cache_write=10), + "step_cost": 0.01, + } + + mod_miniswe.MiniSWEAgent = MiniSWEAgent + + # --- LLM: grade_file_async --- + async def grade_file_async(*args, **kwargs): + grades = [{"score": 8, "reasoning": "Good code"}] + response_data = { + "id": "resp-123", + "usage": { + "prompt_tokens": 500, + "completion_tokens": 200, + "cache_read_input_tokens": 100, + "cache_creation_input_tokens": 50, + }, + } + return grades, response_data + + mod_router.grade_file_async = grade_file_async + + # Wire parent-child relationships + mod_slop_code.entrypoints = mod_entrypoints + mod_slop_code.agent_runner = mod_agent_runner + mod_slop_code.metrics = mod_metrics + mod_entrypoints.commands = mod_commands + mod_entrypoints.problem_runner = mod_problem_runner + mod_commands.run_agent = mod_run_agent + mod_problem_runner.worker = mod_worker + mod_problem_runner.driver = mod_driver + mod_agent_runner.runner = mod_runner + mod_agent_runner.agent = mod_agent + mod_agent_runner.agents = mod_agents + mod_agents.miniswe = mod_miniswe + mod_metrics.rubric = mod_rubric + mod_rubric.router = mod_router + + # Register all modules in sys.modules + modules = { + "slop_code": mod_slop_code, + "slop_code.entrypoints": mod_entrypoints, + "slop_code.entrypoints.commands": mod_commands, + "slop_code.entrypoints.commands.run_agent": mod_run_agent, + "slop_code.entrypoints.problem_runner": mod_problem_runner, + "slop_code.entrypoints.problem_runner.worker": mod_worker, + "slop_code.entrypoints.problem_runner.driver": mod_driver, + "slop_code.agent_runner": mod_agent_runner, + "slop_code.agent_runner.runner": mod_runner, + "slop_code.agent_runner.agent": mod_agent, + "slop_code.agent_runner.agents": mod_agents, + "slop_code.agent_runner.agents.miniswe": mod_miniswe, + "slop_code.metrics": mod_metrics, + "slop_code.metrics.rubric": mod_rubric, + "slop_code.metrics.rubric.router": mod_router, + } + + for name, mod in modules.items(): + sys.modules[name] = mod + + return modules + + +# Install mock modules before any instrumentation imports +_mock_modules = _create_mock_slop_code_modules() + + +@pytest.fixture(scope="function") +def span_exporter(): + from opentelemetry.sdk.trace.export.in_memory_span_exporter import ( + InMemorySpanExporter, + ) + + exporter = InMemorySpanExporter() + yield exporter + exporter.clear() + + +@pytest.fixture(scope="function") +def tracer_provider(span_exporter): + from opentelemetry.sdk.trace import TracerProvider + from opentelemetry.sdk.trace.export import SimpleSpanProcessor + + provider = TracerProvider() + provider.add_span_processor(SimpleSpanProcessor(span_exporter)) + return provider + + +@pytest.fixture(scope="function") +def instrument(tracer_provider): + from opentelemetry.instrumentation.slop_code import SlopCodeInstrumentor + + instrumentor = SlopCodeInstrumentor() + instrumentor.instrument( + tracer_provider=tracer_provider, + skip_dep_check=True, + ) + yield instrumentor + instrumentor.uninstrument() diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_agent_span.py b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_agent_span.py new file mode 100644 index 000000000..d372ba220 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_agent_span.py @@ -0,0 +1,102 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Tests for AGENT span (Agent.run_checkpoint).""" + +from unittest.mock import MagicMock + +import pytest + +from opentelemetry.trace import StatusCode + + +class TestAgentSpan: + """Verify that Agent.run_checkpoint produces an AGENT span.""" + + def test_agent_span_created(self, span_exporter, instrument): + """Agent.run_checkpoint should create an AGENT span.""" + import slop_code.agent_runner.agent as mod + + agent = mod.Agent(problem_name="file_backup") + result = agent.run_checkpoint("solve the bug") + + spans = span_exporter.get_finished_spans() + agent_spans = [ + s for s in spans + if s.attributes.get("gen_ai.operation.name") == "invoke_agent" + ] + assert len(agent_spans) == 1 + + span = agent_spans[0] + assert span.name == "agent.Agent" + assert span.attributes["gen_ai.system"] == "slop-code" + assert span.attributes["gen_ai.span.kind"] == "AGENT" + assert span.attributes["gen_ai.agent.name"] == "Agent" + assert span.attributes["slop_code.problem.name"] == "file_backup" + assert span.status.status_code == StatusCode.OK + + def test_agent_span_captures_usage(self, span_exporter, instrument): + """AGENT span should capture token usage from result.""" + import slop_code.agent_runner.agent as mod + + agent = mod.Agent(problem_name="test_prob") + agent.run_checkpoint("task") + + spans = span_exporter.get_finished_spans() + agent_spans = [ + s for s in spans + if s.attributes.get("gen_ai.operation.name") == "invoke_agent" + ] + assert len(agent_spans) == 1 + span = agent_spans[0] + + assert "gen_ai.usage.input_tokens" in span.attributes + assert "gen_ai.usage.output_tokens" in span.attributes + assert span.attributes["gen_ai.usage.input_tokens"] == 100 + assert span.attributes["gen_ai.usage.output_tokens"] == 50 + + def test_agent_span_error(self, span_exporter, tracer_provider): + """Exception in Agent.run_checkpoint should produce error span.""" + import slop_code.agent_runner.agent as mod + + from opentelemetry.instrumentation.slop_code import SlopCodeInstrumentor + + class FailingAgent(mod.Agent): + def run_checkpoint(self, task): + raise TimeoutError("Agent timeout") + + OriginalAgent = mod.Agent + mod.Agent = FailingAgent + + instrumentor = SlopCodeInstrumentor() + instrumentor.instrument(tracer_provider=tracer_provider, skip_dep_check=True) + + try: + agent = mod.Agent(problem_name="test_prob") + + with pytest.raises(TimeoutError, match="Agent timeout"): + agent.run_checkpoint("task") + + spans = span_exporter.get_finished_spans() + agent_spans = [ + s for s in spans + if s.attributes.get("gen_ai.operation.name") == "invoke_agent" + ] + assert len(agent_spans) == 1 + span = agent_spans[0] + assert span.status.status_code == StatusCode.ERROR + assert span.attributes.get("error.type") == "TimeoutError" + finally: + instrumentor.uninstrument() + mod.Agent = OriginalAgent diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_entry_span.py b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_entry_span.py new file mode 100644 index 000000000..2f7c1751f --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_entry_span.py @@ -0,0 +1,74 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Tests for ENTRY span (run_agent).""" + +import pytest + +from opentelemetry.trace import StatusCode + + +class TestEntrySpan: + """Verify that run_agent produces an ENTRY span.""" + + def test_entry_span_created(self, span_exporter, instrument): + """run_agent should create an ENTRY span with correct attributes.""" + import slop_code.entrypoints.commands.run_agent as mod + + mod.run_agent() + + spans = span_exporter.get_finished_spans() + entry_spans = [ + s for s in spans + if s.attributes.get("gen_ai.span.kind") == "ENTRY" + ] + assert len(entry_spans) == 1 + + span = entry_spans[0] + assert span.name == "slop-code.enter" + assert span.attributes["gen_ai.system"] == "slop-code" + assert span.attributes["gen_ai.operation.name"] == "enter" + assert span.status.status_code == StatusCode.OK + + def test_entry_span_error(self, span_exporter, tracer_provider): + """run_agent raising an exception should produce an error ENTRY span.""" + import slop_code.entrypoints.commands.run_agent as mod + + from opentelemetry.instrumentation.slop_code import SlopCodeInstrumentor + + # Store original and replace with failing function + original = mod.run_agent + + def failing_run_agent(*args, **kwargs): + raise RuntimeError("Config error") + + mod.run_agent = failing_run_agent + + instrumentor = SlopCodeInstrumentor() + instrumentor.instrument(tracer_provider=tracer_provider, skip_dep_check=True) + + try: + with pytest.raises(RuntimeError, match="Config error"): + mod.run_agent() + + spans = span_exporter.get_finished_spans() + entry_spans = [ + s for s in spans + if s.attributes.get("gen_ai.span.kind") == "ENTRY" + ] + assert len(entry_spans) == 1 + assert entry_spans[0].status.status_code == StatusCode.ERROR + finally: + instrumentor.uninstrument() + mod.run_agent = original diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_hierarchy.py b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_hierarchy.py new file mode 100644 index 000000000..d33cc3568 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_hierarchy.py @@ -0,0 +1,118 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Tests for span hierarchy and parent-child relationships.""" + +from unittest.mock import MagicMock + +import pytest + +from opentelemetry.trace import StatusCode + + +class TestSpanHierarchy: + """Verify parent-child relationships between spans.""" + + def test_entry_is_parent_of_workflow(self, span_exporter, instrument): + """ENTRY span should be parent of workflow span when called inline.""" + import slop_code.entrypoints.commands.run_agent as entry_mod + import slop_code.entrypoints.problem_runner.worker as worker_mod + + # Patch run_agent to call run_agent_on_problem internally + original = entry_mod.run_agent.__wrapped__ + + def run_with_workflow(*args, **kwargs): + config = MagicMock() + config.model_def = None + config.agent_config = None + config.pass_policy = None + return worker_mod.run_agent_on_problem( + MagicMock(), "test_problem", config, MagicMock(), "/tmp" + ) + + entry_mod.run_agent.__wrapped__ = run_with_workflow + + try: + entry_mod.run_agent() + + spans = span_exporter.get_finished_spans() + entry_spans = [ + s for s in spans + if s.attributes.get("gen_ai.span.kind") == "ENTRY" + ] + workflow_spans = [ + s for s in spans + if s.attributes.get("gen_ai.operation.name") == "workflow" + ] + + assert len(entry_spans) == 1 + assert len(workflow_spans) == 1 + + entry_span = entry_spans[0] + workflow_span = workflow_spans[0] + + # workflow should be child of entry + assert workflow_span.context.trace_id == entry_span.context.trace_id + assert workflow_span.parent is not None + assert workflow_span.parent.span_id == entry_span.context.span_id + finally: + entry_mod.run_agent.__wrapped__ = original + + def test_workflow_is_parent_of_task(self, span_exporter, instrument): + """Workflow span should be parent of task span when called inline.""" + import slop_code.agent_runner.runner as runner_mod + import slop_code.entrypoints.problem_runner.worker as worker_mod + + original = worker_mod.run_agent_on_problem.__wrapped__ + + def workflow_with_task(*args, **kwargs): + r = runner_mod.AgentRunner() + checkpoint = MagicMock() + checkpoint.name = "cp1" + checkpoint.order = 1 + r._run_checkpoint(checkpoint, "/tmp", True) + return {"summary": {"state": "completed", "passed_policy": True}} + + worker_mod.run_agent_on_problem.__wrapped__ = workflow_with_task + + try: + config = MagicMock() + config.model_def = None + config.agent_config = None + config.pass_policy = None + worker_mod.run_agent_on_problem( + MagicMock(), "prob1", config, MagicMock(), "/tmp" + ) + + spans = span_exporter.get_finished_spans() + workflow_spans = [ + s for s in spans + if s.attributes.get("gen_ai.operation.name") == "workflow" + ] + task_spans = [ + s for s in spans + if s.attributes.get("gen_ai.operation.name") == "run_task" + ] + + assert len(workflow_spans) == 1 + assert len(task_spans) == 1 + + workflow_span = workflow_spans[0] + task_span = task_spans[0] + + assert task_span.context.trace_id == workflow_span.context.trace_id + assert task_span.parent is not None + assert task_span.parent.span_id == workflow_span.context.span_id + finally: + worker_mod.run_agent_on_problem.__wrapped__ = original diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_llm_span.py b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_llm_span.py new file mode 100644 index 000000000..c88e46430 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_llm_span.py @@ -0,0 +1,142 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Tests for LLM span (grade_file_async - Rubric Judge).""" + +from unittest.mock import MagicMock + +import pytest + +from opentelemetry.trace import SpanKind, StatusCode + + +@pytest.mark.asyncio +class TestLLMSpan: + """Verify that grade_file_async produces an LLM span.""" + + async def test_llm_span_created(self, span_exporter, instrument): + """grade_file_async should create an LLM span.""" + import slop_code.metrics.rubric.router as mod + + provider = MagicMock() + provider.value = "openrouter" + + grades, resp = await mod.grade_file_async( + "prompt_prefix", + "criteria_text", + "test.py", + "anthropic/claude-3.5-sonnet", + provider, + 0.7, + ) + + spans = span_exporter.get_finished_spans() + llm_spans = [ + s for s in spans + if s.attributes.get("gen_ai.span.kind") == "LLM" + ] + assert len(llm_spans) == 1 + + span = llm_spans[0] + assert span.name == "chat anthropic/claude-3.5-sonnet" + assert span.attributes["gen_ai.system"] == "openrouter" + assert span.attributes["gen_ai.operation.name"] == "chat" + assert span.attributes["gen_ai.request.model"] == "anthropic/claude-3.5-sonnet" + assert span.attributes["gen_ai.request.temperature"] == 0.7 + assert span.kind == SpanKind.CLIENT + assert span.status.status_code == StatusCode.OK + + async def test_llm_span_captures_usage(self, span_exporter, instrument): + """LLM span should capture token usage from response.""" + import slop_code.metrics.rubric.router as mod + + provider = MagicMock() + provider.value = "openrouter" + + await mod.grade_file_async( + "prefix", "criteria", "file.py", + "anthropic/claude-3.5-sonnet", provider, 0.5, + ) + + spans = span_exporter.get_finished_spans() + llm_spans = [ + s for s in spans + if s.attributes.get("gen_ai.span.kind") == "LLM" + ] + assert len(llm_spans) == 1 + span = llm_spans[0] + + assert span.attributes["gen_ai.usage.input_tokens"] == 500 + assert span.attributes["gen_ai.usage.output_tokens"] == 200 + assert span.attributes["gen_ai.usage.cache_read.input_tokens"] == 100 + assert span.attributes["gen_ai.usage.cache_creation.input_tokens"] == 50 + assert span.attributes["gen_ai.response.id"] == "resp-123" + + async def test_llm_span_error(self, span_exporter, tracer_provider): + """Exception in grade_file_async should produce an error LLM span.""" + import slop_code.metrics.rubric.router as mod + + from opentelemetry.instrumentation.slop_code import SlopCodeInstrumentor + + original = mod.grade_file_async + + async def failing_grade(*args, **kwargs): + raise ConnectionError("API unreachable") + + mod.grade_file_async = failing_grade + + instrumentor = SlopCodeInstrumentor() + instrumentor.instrument(tracer_provider=tracer_provider, skip_dep_check=True) + + provider = MagicMock() + provider.value = "bedrock" + + try: + with pytest.raises(ConnectionError, match="API unreachable"): + await mod.grade_file_async( + "prefix", "criteria", "file.py", + "us.anthropic.claude-3-5-sonnet-20241022-v2:0", provider, 0.3, + ) + + spans = span_exporter.get_finished_spans() + llm_spans = [ + s for s in spans + if s.attributes.get("gen_ai.span.kind") == "LLM" + ] + assert len(llm_spans) == 1 + assert llm_spans[0].status.status_code == StatusCode.ERROR + assert llm_spans[0].attributes["gen_ai.system"] == "bedrock" + finally: + instrumentor.uninstrument() + mod.grade_file_async = original + + async def test_llm_span_bedrock_provider(self, span_exporter, instrument): + """LLM span with bedrock provider should use 'bedrock' as system.""" + import slop_code.metrics.rubric.router as mod + + provider = MagicMock() + provider.value = "bedrock" + + await mod.grade_file_async( + "prefix", "criteria", "file.py", + "us.anthropic.claude-3-5-sonnet-20241022-v2:0", provider, 0.5, + ) + + spans = span_exporter.get_finished_spans() + llm_spans = [ + s for s in spans + if s.attributes.get("gen_ai.span.kind") == "LLM" + ] + assert len(llm_spans) == 1 + assert llm_spans[0].attributes["gen_ai.system"] == "bedrock" diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_step_span.py b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_step_span.py new file mode 100644 index 000000000..70e221da2 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_step_span.py @@ -0,0 +1,133 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Tests for STEP span (MiniSWEAgent.agent_step).""" + +from unittest.mock import MagicMock + +import pytest + +from opentelemetry.trace import StatusCode + + +class TestStepSpan: + """Verify that MiniSWEAgent.agent_step produces a STEP span.""" + + def test_step_span_created(self, span_exporter, instrument): + """agent_step should create a STEP span with token attributes.""" + import slop_code.agent_runner.agents.miniswe as mod + + agent = mod.MiniSWEAgent(problem_name="test_prob") + result = agent.agent_step() + + spans = span_exporter.get_finished_spans() + step_spans = [ + s for s in spans + if s.attributes.get("gen_ai.span.kind") == "STEP" + ] + assert len(step_spans) == 1 + + span = step_spans[0] + assert span.name == "react.step.1" + assert span.attributes["gen_ai.system"] == "slop-code" + assert span.attributes["gen_ai.operation.name"] == "react" + assert span.attributes["gen_ai.react.round"] == 1 + assert span.status.status_code == StatusCode.OK + + def test_step_span_has_token_usage(self, span_exporter, instrument): + """STEP span should capture token usage from result.""" + import slop_code.agent_runner.agents.miniswe as mod + + agent = mod.MiniSWEAgent(problem_name="test_prob") + agent.agent_step() + + spans = span_exporter.get_finished_spans() + step_spans = [ + s for s in spans + if s.attributes.get("gen_ai.span.kind") == "STEP" + ] + assert len(step_spans) == 1 + span = step_spans[0] + + assert span.attributes["gen_ai.usage.input_tokens"] == 200 + assert span.attributes["gen_ai.usage.output_tokens"] == 80 + assert span.attributes["gen_ai.usage.cache_read.input_tokens"] == 50 + assert span.attributes["gen_ai.usage.cache_creation.input_tokens"] == 10 + + def test_step_span_increments_round(self, span_exporter, instrument): + """Multiple agent_step calls should increment the round number.""" + import slop_code.agent_runner.agents.miniswe as mod + + agent = mod.MiniSWEAgent(problem_name="test_prob") + # Simulate steps=2 already completed + agent.usage.steps = 2 + agent.agent_step() + + spans = span_exporter.get_finished_spans() + step_spans = [ + s for s in spans + if s.attributes.get("gen_ai.span.kind") == "STEP" + ] + assert len(step_spans) == 1 + assert step_spans[0].name == "react.step.3" + assert step_spans[0].attributes["gen_ai.react.round"] == 3 + + def test_step_span_error(self, span_exporter, tracer_provider): + """Exception in agent_step should produce an error STEP span.""" + import slop_code.agent_runner.agents.miniswe as mod + + from opentelemetry.instrumentation.slop_code import SlopCodeInstrumentor + + class FailingMiniSWE(mod.MiniSWEAgent): + def agent_step(self): + raise RuntimeError("LimitsExceeded") + + OriginalClass = mod.MiniSWEAgent + mod.MiniSWEAgent = FailingMiniSWE + + instrumentor = SlopCodeInstrumentor() + instrumentor.instrument(tracer_provider=tracer_provider, skip_dep_check=True) + + try: + agent = mod.MiniSWEAgent(problem_name="test_prob") + + with pytest.raises(RuntimeError, match="LimitsExceeded"): + agent.agent_step() + + spans = span_exporter.get_finished_spans() + step_spans = [ + s for s in spans + if s.attributes.get("gen_ai.span.kind") == "STEP" + ] + assert len(step_spans) == 1 + span = step_spans[0] + assert span.status.status_code == StatusCode.ERROR + assert span.attributes["gen_ai.react.finish_reason"] == "error" + finally: + instrumentor.uninstrument() + mod.MiniSWEAgent = OriginalClass + + def test_step_span_finish_reason_stop(self, span_exporter, instrument): + """Successful step should have finish_reason='stop'.""" + import slop_code.agent_runner.agents.miniswe as mod + + agent = mod.MiniSWEAgent(problem_name="test_prob") + agent.agent_step() + + spans = span_exporter.get_finished_spans() + step_spans = [ + s for s in spans + if s.attributes.get("gen_ai.span.kind") == "STEP" + ] + assert step_spans[0].attributes["gen_ai.react.finish_reason"] == "stop" diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_task_span.py b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_task_span.py new file mode 100644 index 000000000..de3e16a95 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_task_span.py @@ -0,0 +1,110 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Tests for TASK span (AgentRunner._run_checkpoint).""" + +from unittest.mock import MagicMock + +import pytest + +from opentelemetry.trace import StatusCode + + +class TestTaskSpan: + """Verify that AgentRunner._run_checkpoint produces a TASK span.""" + + def test_task_span_created(self, span_exporter, instrument): + """_run_checkpoint should create a task span.""" + import slop_code.agent_runner.runner as mod + + runner = mod.AgentRunner() + + checkpoint = MagicMock() + checkpoint.name = "checkpoint_1" + checkpoint.order = 1 + + result = runner._run_checkpoint(checkpoint, "/tmp/save", True) + + spans = span_exporter.get_finished_spans() + task_spans = [ + s for s in spans + if s.attributes.get("gen_ai.operation.name") == "run_task" + ] + assert len(task_spans) == 1 + + span = task_spans[0] + assert span.name == "task.checkpoint_1" + assert span.attributes["gen_ai.system"] == "slop-code" + assert span.attributes["gen_ai.span.kind"] == "TASK" + assert span.attributes["slop_code.checkpoint.name"] == "checkpoint_1" + assert span.attributes["slop_code.checkpoint.order"] == 1 + assert span.attributes["slop_code.is_first_checkpoint"] is True + assert span.status.status_code == StatusCode.OK + + def test_task_span_error(self, span_exporter, tracer_provider): + """Exception in _run_checkpoint should produce an error task span.""" + import slop_code.agent_runner.runner as mod + + from opentelemetry.instrumentation.slop_code import SlopCodeInstrumentor + + class FailingRunner(mod.AgentRunner): + def _run_checkpoint(self, checkpoint, checkpoint_save_dir, is_first_checkpoint=False): + raise RuntimeError("Checkpoint failed") + + # Replace class temporarily + OriginalRunner = mod.AgentRunner + mod.AgentRunner = FailingRunner + + instrumentor = SlopCodeInstrumentor() + instrumentor.instrument(tracer_provider=tracer_provider, skip_dep_check=True) + + try: + runner = mod.AgentRunner() + checkpoint = MagicMock() + checkpoint.name = "bad_checkpoint" + checkpoint.order = 2 + + with pytest.raises(RuntimeError, match="Checkpoint failed"): + runner._run_checkpoint(checkpoint, "/tmp/save", False) + + spans = span_exporter.get_finished_spans() + task_spans = [ + s for s in spans + if s.attributes.get("gen_ai.operation.name") == "run_task" + ] + assert len(task_spans) == 1 + assert task_spans[0].status.status_code == StatusCode.ERROR + finally: + instrumentor.uninstrument() + mod.AgentRunner = OriginalRunner + + def test_task_span_not_first_checkpoint(self, span_exporter, instrument): + """Subsequent checkpoint should have is_first_checkpoint=False.""" + import slop_code.agent_runner.runner as mod + + runner = mod.AgentRunner() + + checkpoint = MagicMock() + checkpoint.name = "checkpoint_2" + checkpoint.order = 2 + + runner._run_checkpoint(checkpoint, "/tmp/save", False) + + spans = span_exporter.get_finished_spans() + task_spans = [ + s for s in spans + if s.attributes.get("gen_ai.operation.name") == "run_task" + ] + assert len(task_spans) == 1 + assert task_spans[0].attributes["slop_code.is_first_checkpoint"] is False diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_workflow_span.py b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_workflow_span.py new file mode 100644 index 000000000..6d0a79ddc --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-slop-code/tests/test_workflow_span.py @@ -0,0 +1,117 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Tests for CHAIN/workflow span (run_agent_on_problem).""" + +from unittest.mock import MagicMock + +import pytest + +from opentelemetry.trace import StatusCode + + +class TestWorkflowSpan: + """Verify that run_agent_on_problem produces a workflow span.""" + + def test_workflow_span_created(self, span_exporter, instrument): + """run_agent_on_problem should create a workflow span.""" + import slop_code.entrypoints.problem_runner.worker as mod + + config = MagicMock() + config.model_def = MagicMock() + config.model_def.name = "anthropic/claude-3.5-sonnet" + config.agent_config = MagicMock() + config.agent_config.type = "claude_code" + config.pass_policy = MagicMock() + config.pass_policy.value = "any" + + result = mod.run_agent_on_problem( + MagicMock(), # problem_config + "file_backup", # problem_name + config, # config + MagicMock(), # progress_queue + "/tmp/output", # output_path + ) + + spans = span_exporter.get_finished_spans() + workflow_spans = [ + s for s in spans + if s.attributes.get("gen_ai.operation.name") == "workflow" + ] + assert len(workflow_spans) == 1 + + span = workflow_spans[0] + assert span.name == "workflow.file_backup" + assert span.attributes["gen_ai.system"] == "slop-code" + assert span.attributes["gen_ai.span.kind"] == "CHAIN" + assert span.attributes["slop_code.problem.name"] == "file_backup" + assert span.attributes["gen_ai.request.model"] == "anthropic/claude-3.5-sonnet" + assert span.attributes["slop_code.agent.type"] == "claude_code" + assert span.status.status_code == StatusCode.OK + + def test_workflow_span_error(self, span_exporter, tracer_provider): + """Exception in run_agent_on_problem should produce error workflow span.""" + import slop_code.entrypoints.problem_runner.worker as mod + + from opentelemetry.instrumentation.slop_code import SlopCodeInstrumentor + + original = mod.run_agent_on_problem + + def failing_worker(*args, **kwargs): + raise ValueError("Problem not found") + + mod.run_agent_on_problem = failing_worker + + instrumentor = SlopCodeInstrumentor() + instrumentor.instrument(tracer_provider=tracer_provider, skip_dep_check=True) + + try: + with pytest.raises(ValueError, match="Problem not found"): + mod.run_agent_on_problem( + MagicMock(), "missing_problem", MagicMock(), MagicMock(), "/tmp" + ) + + spans = span_exporter.get_finished_spans() + workflow_spans = [ + s for s in spans + if s.attributes.get("gen_ai.operation.name") == "workflow" + ] + assert len(workflow_spans) == 1 + assert workflow_spans[0].status.status_code == StatusCode.ERROR + finally: + instrumentor.uninstrument() + mod.run_agent_on_problem = original + + def test_workflow_span_with_none_config_fields(self, span_exporter, instrument): + """Workflow span should handle None config fields gracefully.""" + import slop_code.entrypoints.problem_runner.worker as mod + + config = MagicMock() + config.model_def = None + config.agent_config = None + config.pass_policy = None + + mod.run_agent_on_problem( + MagicMock(), "test_problem", config, MagicMock(), "/tmp" + ) + + spans = span_exporter.get_finished_spans() + workflow_spans = [ + s for s in spans + if s.attributes.get("gen_ai.operation.name") == "workflow" + ] + assert len(workflow_spans) == 1 + span = workflow_spans[0] + assert span.attributes["slop_code.problem.name"] == "test_problem" + assert "gen_ai.request.model" not in span.attributes From de596314b0c5bc6f5f8cf489e6c2ce889905b16a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=89=A7=E6=80=9D?= Date: Thu, 7 May 2026 09:32:45 +0800 Subject: [PATCH 7/8] feat: support wild-tool Change-Id: I0da98161cbdbe6a51b963bcc19f45a3d2d977968 (cherry picked from commit b7e7a4b0bcfc507c94858c4f17fc717eee6437ea) Co-authored-by: 123liuziming <32130965+123liuziming@users.noreply.github.com> --- .../README.md | 55 ++ .../pyproject.toml | 66 ++ .../instrumentation/wildtool/__init__.py | 161 +++++ .../instrumentation/wildtool/_wrappers.py | 644 ++++++++++++++++++ .../instrumentation/wildtool/package.py | 2 + .../instrumentation/wildtool/utils.py | 17 + .../instrumentation/wildtool/version.py | 1 + .../tests/__init__.py | 0 .../tests/conftest.py | 182 +++++ .../tests/test_agent_span.py | 108 +++ .../tests/test_chain_step_tool_spans.py | 283 ++++++++ .../tests/test_entry_span.py | 115 ++++ .../tests/test_error_scenarios.py | 135 ++++ .../tests/test_instrumentor.py | 20 + .../tests/test_round2_fixes.py | 441 ++++++++++++ 15 files changed, 2230 insertions(+) create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-wildtool/README.md create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-wildtool/pyproject.toml create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-wildtool/src/opentelemetry/instrumentation/wildtool/__init__.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-wildtool/src/opentelemetry/instrumentation/wildtool/_wrappers.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-wildtool/src/opentelemetry/instrumentation/wildtool/package.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-wildtool/src/opentelemetry/instrumentation/wildtool/utils.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-wildtool/src/opentelemetry/instrumentation/wildtool/version.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/__init__.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/conftest.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/test_agent_span.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/test_chain_step_tool_spans.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/test_entry_span.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/test_error_scenarios.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/test_instrumentor.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/test_round2_fixes.py diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/README.md b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/README.md new file mode 100644 index 000000000..1b0499fa4 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/README.md @@ -0,0 +1,55 @@ +# LoongSuite WildToolBench Instrumentation + +OpenTelemetry instrumentation for the [WildToolBench](https://github.com/yupeijei1997/WildToolBench) benchmark framework. + +## Installation + +WildToolBench is not available on PyPI. Install it from source: + +```bash +pip install -e /path/to/WildToolBench/wild-tool-bench +pip install loongsuite-instrumentation-wildtool +``` + +## Requirements + +- **OpenAI provider instrumentation**: To produce LLM spans, you must also enable an OpenAI provider instrumentation (e.g., `opentelemetry-instrumentation-openai` or LoongSuite's equivalent). This plugin creates ENTRY/AGENT/CHAIN/STEP/TOOL spans but does **not** create LLM spans itself. + +## Usage + +```python +from opentelemetry.instrumentation.wildtool import WildToolInstrumentor + +WildToolInstrumentor().instrument() + +# Run WildToolBench as usual — spans are automatically generated. +``` + +## Span Topology + +``` +ENTRY (enter_ai_application_system) +└── AGENT (invoke_agent wildtool) + └── CHAIN (workflow task_{idx}) + └── STEP (react step) + ├── [LLM span — provider instrumentation] + └── TOOL (execute_tool {tool_name}) +``` + +## Patch Points + +| # | Target | Span Type | +|---|--------|-----------| +| P1 | `multi_threaded_inference` | ENTRY | +| P2 | `BaseHandler.inference_multi_turn` | AGENT | +| P3 | `BaseHandler.inference_and_eval_multi_step` | CHAIN + TOOL | +| P4 | `BaseHandler._request_tool_call` | STEP | +| P5 | `BaseHandler._parse_api_response` | (token extraction) | + +## Round 2 fixes (see `llm-dev/execute.md` § "修订记录 (Round 2 fix)") + +- **H1**: TOOL span is now parented on STEP, not CHAIN. Strategy A enhanced — the chain wrapper holds a `round → STEP span` map and uses `trace.set_span_in_context(step_span)` to anchor each post-hoc TOOL span on the matching STEP. STEP `SpanContext`s remain valid parents even after `end()`. +- **H2 (provider-name fallback)**: `opentelemetry-instrumentation-openai-v2 == 0.62b1` only emits the legacy `gen_ai.system` attribute on its LLM span; the new `gen_ai.provider.name` attribute is missing. As a *pure fallback* the wildtool plugin writes both `gen_ai.system="openai"` and `gen_ai.provider.name="openai"` on the **STEP** span (not on the LLM span — that is owned by the OpenAI v2 instrumentation and we do **not** patch it). Once the OpenAI v2 instrumentation upstream emits `gen_ai.provider.name` natively this fallback can be removed. +- **M1**: CHAIN span now carries `input.value` (last user message in `inference_data["messages"]`, truncated to 4096 chars) and `output.value` (JSON of `action_name_label`/`task_idx`/`is_optimal`). +- **M2**: STEP span now carries `gen_ai.react.finish_reason` on error paths. Mapping table is in `execute.md` § "M2: gen_ai.react.finish_reason 取值映射". +- **M3**: TOOL span explicitly writes `gen_ai.tool.call.arguments` / `gen_ai.tool.call.result` / `gen_ai.tool.description`, bypassing `OTEL_INSTRUMENTATION_GENAI_CAPTURE_*` gating in `opentelemetry-util-genai`. The custom `wildtool.tool.execution_mode = "ground_truth_replay"` is preserved. diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/pyproject.toml b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/pyproject.toml new file mode 100644 index 000000000..b8f9f44d0 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/pyproject.toml @@ -0,0 +1,66 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "loongsuite-instrumentation-wildtool" +dynamic = ["version"] +description = "LoongSuite WildToolBench Instrumentation" +readme = "README.md" +license = "Apache-2.0" +requires-python = ">=3.9" +authors = [ + { name = "LoongSuite Python Agent Authors", email = "caishipeng.csp@alibaba-inc.com" }, + { name = "OpenTelemetry Authors", email = "cncf-opentelemetry-contributors@lists.cncf.io" }, +] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Developers", + "License :: OSI Approved :: Apache Software License", + "Programming Language :: Python", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", +] +dependencies = [ + "opentelemetry-api ~= 1.37", + "opentelemetry-instrumentation >= 0.58b0", + "opentelemetry-semantic-conventions >= 0.58b0", + "opentelemetry-util-genai", + "wrapt >= 1.17.3, < 3.0.0", +] + +[project.optional-dependencies] +instruments = [ + "openai >= 1.0.0", +] + +test = [ + "pytest ~= 8.0", + "pytest-cov ~= 4.1.0", + "pytest-forked >= 1.6.0", + "opentelemetry-sdk >= 1.37", + "openai >= 1.0.0", +] + +[project.entry-points.opentelemetry_instrumentor] +wildtool = "opentelemetry.instrumentation.wildtool:WildToolInstrumentor" + +[project.urls] +Homepage = "https://github.com/alibaba/loongsuite-python-agent/tree/main/instrumentation-loongsuite/loongsuite-instrumentation-wildtool" +Repository = "https://github.com/alibaba/loongsuite-python-agent" + +[tool.hatch.version] +path = "src/opentelemetry/instrumentation/wildtool/version.py" + +[tool.hatch.build.targets.sdist] +include = [ + "/src", + "/tests", +] + +[tool.hatch.build.targets.wheel] +packages = ["src/opentelemetry"] diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/src/opentelemetry/instrumentation/wildtool/__init__.py b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/src/opentelemetry/instrumentation/wildtool/__init__.py new file mode 100644 index 000000000..dad772500 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/src/opentelemetry/instrumentation/wildtool/__init__.py @@ -0,0 +1,161 @@ +"""OpenTelemetry WildToolBench Instrumentation""" + +import logging +from typing import Any, Collection + +from wrapt import wrap_function_wrapper + +from opentelemetry.instrumentation.instrumentor import BaseInstrumentor +from opentelemetry.instrumentation.utils import unwrap +from opentelemetry.instrumentation.wildtool.package import _instruments +from opentelemetry.instrumentation.wildtool.version import __version__ +from opentelemetry.instrumentation.wildtool._wrappers import ( + WildToolAgentWrapper, + WildToolChainWrapper, + WildToolEntryWrapper, + WildToolParseWrapper, + WildToolRequestWrapper, +) +from opentelemetry.util.genai.extended_handler import ExtendedTelemetryHandler + +logger = logging.getLogger(__name__) + +_LLM_RESPONSE_GEN_MODULE = "wtb._llm_response_generation" +_BASE_HANDLER_MODULE = "wtb.model_handler.base_handler" + +__all__ = ["WildToolInstrumentor", "__version__"] + + +class WildToolInstrumentor(BaseInstrumentor): + """OpenTelemetry instrumentor for WildToolBench framework.""" + + def __init__(self): + super().__init__() + self._handler = None + # Track concrete handler subclasses whose abstract _request_tool_call / + # _parse_api_response we have already wrapped, so we can unwrap on + # uninstrument and avoid double-wrapping. + self._patched_handler_classes: set = set() + self._request_wrapper = None + self._parse_wrapper = None + + def instrumentation_dependencies(self) -> Collection[str]: + return _instruments + + def _instrument(self, **kwargs: Any) -> None: + tracer_provider = kwargs.get("tracer_provider") + meter_provider = kwargs.get("meter_provider") + logger_provider = kwargs.get("logger_provider") + + self._handler = ExtendedTelemetryHandler( + tracer_provider=tracer_provider, + meter_provider=meter_provider, + logger_provider=logger_provider, + ) + self._request_wrapper = WildToolRequestWrapper(self._handler) + self._parse_wrapper = WildToolParseWrapper(self._handler) + + # P1: ENTRY span + try: + wrap_function_wrapper( + _LLM_RESPONSE_GEN_MODULE, + "multi_threaded_inference", + WildToolEntryWrapper(self._handler), + ) + except Exception as e: + logger.warning("Failed to instrument multi_threaded_inference: %s", e) + + # P2: AGENT span + try: + wrap_function_wrapper( + _BASE_HANDLER_MODULE, + "BaseHandler.inference_multi_turn", + WildToolAgentWrapper(self._handler), + ) + except Exception as e: + logger.warning("Failed to instrument inference_multi_turn: %s", e) + + # P3: CHAIN span (+ STEP + TOOL management). + # The chain wrapper also lazily patches the concrete subclass' + # `_request_tool_call` / `_parse_api_response` on first use, so that + # subclasses overriding the abstract base methods are still + # intercepted (P4 / P5). + try: + wrap_function_wrapper( + _BASE_HANDLER_MODULE, + "BaseHandler.inference_and_eval_multi_step", + WildToolChainWrapper(self._handler, self), + ) + except Exception as e: + logger.warning( + "Failed to instrument inference_and_eval_multi_step: %s", e + ) + + def ensure_handler_class_patched(self, handler_cls) -> None: + """Lazily wrap the concrete handler subclass' P4/P5 methods. + + WildToolBench declares ``_request_tool_call`` and ``_parse_api_response`` + as abstract on ``BaseHandler``, but real handlers (and tests) override + them. Python method resolution dispatches directly to the override and + therefore never reaches a wrapper installed on the base class. We + instead wrap the override on first invocation per subclass. + """ + if handler_cls in self._patched_handler_classes: + return + self._patched_handler_classes.add(handler_cls) + + module_name = handler_cls.__module__ + cls_name = handler_cls.__name__ + for method, wrapper in ( + ("_request_tool_call", self._request_wrapper), + ("_parse_api_response", self._parse_wrapper), + ): + if method not in handler_cls.__dict__: + continue + try: + wrap_function_wrapper( + module_name, + f"{cls_name}.{method}", + wrapper, + ) + except Exception as e: + logger.debug( + "Failed to wrap %s.%s.%s: %s", + module_name, + cls_name, + method, + e, + ) + + def _uninstrument(self, **kwargs: Any) -> None: + try: + import wtb._llm_response_generation as llm_gen + + unwrap(llm_gen, "multi_threaded_inference") + except Exception as e: + logger.debug("Failed to uninstrument multi_threaded_inference: %s", e) + + try: + import wtb.model_handler.base_handler as bh + + unwrap(bh.BaseHandler, "inference_multi_turn") + unwrap(bh.BaseHandler, "inference_and_eval_multi_step") + except Exception as e: + logger.debug("Failed to uninstrument BaseHandler methods: %s", e) + + for cls in list(self._patched_handler_classes): + for method in ("_request_tool_call", "_parse_api_response"): + if method in cls.__dict__: + try: + unwrap(cls, method) + except Exception as e: + logger.debug( + "Failed to unwrap %s.%s: %s", + cls.__name__, + method, + e, + ) + self._patched_handler_classes.clear() + self._request_wrapper = None + self._parse_wrapper = None + self._handler = None diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/src/opentelemetry/instrumentation/wildtool/_wrappers.py b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/src/opentelemetry/instrumentation/wildtool/_wrappers.py new file mode 100644 index 000000000..612a332ab --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/src/opentelemetry/instrumentation/wildtool/_wrappers.py @@ -0,0 +1,644 @@ +"""Wrapper classes for WildToolBench instrumentation. + +Each wrapper corresponds to one patch point and manages the lifecycle +of one or more span types. + +Round 2 fix highlights (see ``llm-dev/execute.md`` § "修订记录 (Round 2 fix)"): + +H1 + TOOL span parent is now STEP rather than CHAIN. Each STEP invocation is + appended to a per-chain list in :data:`_chain_step_invocations`; when the + chain wrapper post-processes ``inference_log`` it looks up the matching + STEP span by ``round`` and uses + :func:`opentelemetry.trace.set_span_in_context` so ``start_execute_tool`` + parents the TOOL span on the STEP context (even if STEP is already + closed — its :class:`SpanContext` remains a valid parent reference). + +H2 + The OpenAI v2 provider instrumentation (0.62b1) writes only the legacy + ``gen_ai.system`` attribute to its LLM span. The wildtool plugin now + writes both ``gen_ai.system`` and ``gen_ai.provider.name`` on the STEP + span as a fallback so the new semantic-conventions attribute is present + in the trace tree even before the upstream OpenAI v2 instrumentation + catches up. We do **not** patch the OpenAI v2 instrumentation itself. + +M1 + ``input.value`` (last user message in the chain's ``messages``, truncated + to 4096 chars) and ``output.value`` (a JSON of action label, task index + and is_optimal) are written on the CHAIN span. + +M2 + ``gen_ai.react.finish_reason`` is derived from ``inference_log`` on the + *last* (currently active) STEP. Mappings: + + ``"parse_tool_calls_failed"`` + ``error_reason`` contains "parse tool_calls failed". + ``"action_name_mismatch"`` + ``error_reason`` contains "action name not in candidate". + ``"empty_response"`` + ``error_reason`` contains "tool_calls and content are None". + ``"error"`` + request raised an exception (handled in + :class:`WildToolRequestWrapper`). + +M3 + ``gen_ai.tool.call.arguments``, ``gen_ai.tool.call.result`` and + ``gen_ai.tool.description`` are written explicitly on TOOL spans + *before* close as a fallback. ``opentelemetry-util-genai`` gates these + sensitive attributes behind ``OTEL_INSTRUMENTATION_GENAI_CAPTURE_*`` env + vars; the wildtool plugin always writes them since wtb data is + benchmark-synthetic and never PII. +""" + +import json +import logging +from contextvars import ContextVar +from typing import List, Optional + +from opentelemetry.trace import StatusCode, set_span_in_context +from opentelemetry.util.genai.extended_handler import ExtendedTelemetryHandler +from opentelemetry.util.genai.extended_types import ( + EntryInvocation, + ExecuteToolInvocation, + InvokeAgentInvocation, + ReactStepInvocation, +) +from opentelemetry.util.genai.types import Error + +logger = logging.getLogger(__name__) + +# ─────────────────────────── ContextVars ─────────────────────────────── +# The CHAIN wrapper opens a new logical "chain" by flipping ``_in_chain`` +# and resetting the counter. The REQUEST wrapper reads these to decide +# whether to create a STEP span and what round number to assign. +_in_chain: ContextVar[bool] = ContextVar("_wt_in_chain", default=False) + +# Currently open STEP invocation. Used by the parse wrapper to attach +# token attributes to the right span. +_step_invocation: ContextVar[Optional[ReactStepInvocation]] = ContextVar( + "_wt_step_inv", default=None +) +_step_counter: ContextVar[int] = ContextVar("_wt_step_ctr", default=0) + +# Per-chain list of every STEP invocation created in the current chain +# (in `round` order). The chain wrapper allocates this list on entry and +# uses it after ``wrapped`` returns to re-parent TOOL spans onto the +# matching STEP. Even if a STEP span is already ``end()``-ed, its +# :class:`SpanContext` stays valid as a parent reference for new spans. +_chain_step_invocations: ContextVar[Optional[List[ReactStepInvocation]]] = ( + ContextVar("_wt_chain_step_invs", default=None) +) + +_PROVIDER_FALLBACK_NAME = "openai" +_INPUT_VALUE_MAX_CHARS = 4096 + + +def _close_active_step(handler: ExtendedTelemetryHandler) -> None: + """Close the currently active STEP span, if any.""" + prev = _step_invocation.get() + if prev is not None: + try: + handler.stop_react_step(prev) + except Exception as e: # noqa: BLE001 + logger.debug("Failed to close step: %s", e) + _step_invocation.set(None) + + +def _truncate(text: str, max_chars: int) -> str: + if len(text) <= max_chars: + return text + return text[:max_chars] + "...(truncated)" + + +def _stringify(value) -> str: + if isinstance(value, str): + return value + try: + return json.dumps(value, ensure_ascii=False) + except (TypeError, ValueError): + return str(value) + + +class WildToolEntryWrapper: + """P1: Wraps multi_threaded_inference → ENTRY span.""" + + def __init__(self, handler: ExtendedTelemetryHandler): + self._handler = handler + + def __call__(self, wrapped, instance, args, kwargs): + # Signature: multi_threaded_inference(handler, model_name, test_case). + # We only need model_name and test_case for ENTRY attributes; the + # handler instance flows through as args[0] untouched. + model_name = args[1] if len(args) > 1 else kwargs.get("model_name", "") + test_case = args[2] if len(args) > 2 else kwargs.get("test_case", {}) + + invocation = EntryInvocation( + session_id=test_case.get("id"), + attributes={ + "gen_ai.framework": "wildtool", + "gen_ai.request.model": model_name, + "wildtool.turn_count": len(test_case.get("english_tasks", [])), + }, + ) + self._handler.start_entry(invocation) + try: + result = wrapped(*args, **kwargs) + self._handler.stop_entry(invocation) + return result + except Exception as e: + self._handler.fail_entry( + invocation, Error(message=str(e), type=type(e)) + ) + raise + + +class WildToolAgentWrapper: + """P2: Wraps BaseHandler.inference_multi_turn → AGENT span.""" + + def __init__(self, handler: ExtendedTelemetryHandler): + self._handler = handler + + def __call__(self, wrapped, instance, args, kwargs): + test_entry = args[0] if args else kwargs.get("test_entry", {}) + + invocation = InvokeAgentInvocation( + provider=None, + agent_name=type(instance).__name__, + conversation_id=test_entry.get("id"), + request_model=getattr(instance, "model_name", None), + attributes={ + "gen_ai.framework": "wildtool", + "wildtool.turn_count": len( + test_entry.get("english_answer_list", []) + ), + }, + ) + self._handler.start_invoke_agent(invocation) + try: + result = wrapped(*args, **kwargs) + total_input = 0 + total_output = 0 + for task_result in (result or []): + if isinstance(task_result, dict): + total_input += sum( + task_result.get("input_token_count", []) + ) + total_output += sum( + task_result.get("output_token_count", []) + ) + if total_input: + invocation.input_tokens = total_input + if total_output: + invocation.output_tokens = total_output + self._handler.stop_invoke_agent(invocation) + return result + except Exception as e: + self._handler.fail_invoke_agent( + invocation, Error(message=str(e), type=type(e)) + ) + raise + + +class WildToolChainWrapper: + """P3: Wraps BaseHandler.inference_and_eval_multi_step → CHAIN span. + + Also manages the lifecycle of the final STEP span and creates TOOL spans + from the returned ``inference_log`` after the original function completes. + Round 2 fixes (H1/M1/M2/M3) are implemented here. + """ + + def __init__(self, handler: ExtendedTelemetryHandler, instrumentor=None): + self._handler = handler + self._instrumentor = instrumentor + + def __call__(self, wrapped, instance, args, kwargs): + if self._instrumentor is not None and instance is not None: + try: + self._instrumentor.ensure_handler_class_patched(type(instance)) + except Exception as e: # noqa: BLE001 + logger.debug("Failed to ensure subclass patched: %s", e) + + inference_data = args[0] if args else kwargs.get("inference_data", {}) + if not isinstance(inference_data, dict): + inference_data = {} + task_idx = inference_data.get("task_idx", 0) + test_entry_id = inference_data.get("test_entry_id", "") + + span_name = f"workflow task_{task_idx}" + tracer = self._handler._tracer + + chain_token = _in_chain.set(True) + counter_token = _step_counter.set(0) + step_token = _step_invocation.set(None) + chain_steps: List[ReactStepInvocation] = [] + chain_steps_token = _chain_step_invocations.set(chain_steps) + + chain_attributes = { + "gen_ai.span.kind": "CHAIN", + "gen_ai.operation.name": "workflow", + "gen_ai.framework": "wildtool", + "wildtool.task_idx": task_idx, + "wildtool.test_entry_id": test_entry_id, + } + + # M1: Capture last user message as ``input.value`` BEFORE running the + # wrapped function (the wtb function mutates ``messages`` in place). + input_value = self._extract_input_value(inference_data) + if input_value is not None: + chain_attributes["input.value"] = input_value + + with tracer.start_as_current_span( + name=span_name, attributes=chain_attributes + ) as span: + try: + result = wrapped(*args, **kwargs) + + # M2: Set finish_reason on the currently active (last) STEP + # BEFORE we close it. Only the terminal step ever carries an + # error finish_reason (every wtb error path triggers `break`). + if isinstance(result, dict): + self._apply_last_step_finish_reason( + result.get("inference_log", {}) + ) + + _close_active_step(self._handler) + + if isinstance(result, dict): + label = result.get("action_name_label", "") + is_optimal = bool(result.get("is_optimal", False)) + span.set_attribute("wildtool.action_name_label", label) + span.set_attribute("wildtool.is_optimal", is_optimal) + + # M1: ``output.value`` summarising chain outcome. + try: + span.set_attribute( + "output.value", + json.dumps( + { + "action_name_label": label, + "task_idx": task_idx, + "is_optimal": is_optimal, + }, + ensure_ascii=False, + ), + ) + except Exception as e: # noqa: BLE001 + logger.debug("Failed to set output.value: %s", e) + + # H1 + M3: re-parent TOOL spans on STEP and force-write + # tool call sensitive attributes. + self._create_tool_spans_from_log( + result.get("inference_log", {}), + inference_data, + chain_steps, + ) + + span.set_status(StatusCode.OK) + return result + except Exception as e: + _close_active_step(self._handler) + span.record_exception(e) + span.set_status(StatusCode.ERROR) + raise + finally: + _chain_step_invocations.reset(chain_steps_token) + _step_counter.reset(counter_token) + _step_invocation.reset(step_token) + _in_chain.reset(chain_token) + + # -- M1 --------------------------------------------------------------- + + @staticmethod + def _extract_input_value(inference_data) -> Optional[str]: + msgs = inference_data.get("messages") if isinstance( + inference_data, dict + ) else None + if not isinstance(msgs, list): + return None + for m in reversed(msgs): + if not isinstance(m, dict) or m.get("role") != "user": + continue + content = m.get("content") + if content is None: + continue + text = _stringify(content) + return _truncate(text, _INPUT_VALUE_MAX_CHARS) + return None + + # -- M2 --------------------------------------------------------------- + + def _apply_last_step_finish_reason(self, inference_log) -> None: + if not isinstance(inference_log, dict): + return + current_step = _step_invocation.get() + if current_step is None or current_step.round is None: + return + step_key = f"step_{current_step.round - 1}" + step_data = inference_log.get(step_key) + if not isinstance(step_data, dict): + return + output = step_data.get("inference_output") or {} + if not isinstance(output, dict): + return + label = output.get("current_action_name_label") + error_reason = output.get("error_reason") or "" + reason = self._derive_step_finish_reason(label, error_reason) + if reason is None: + return + # Setting `invocation.finish_reason` is enough — the util-genai + # `_apply_react_step_finish_attributes` writes + # ``gen_ai.react.finish_reason`` from this field on stop. + current_step.finish_reason = reason + + @staticmethod + def _derive_step_finish_reason( + label, error_reason: str + ) -> Optional[str]: + """Map wtb inference_log error_reason → gen_ai.react.finish_reason.""" + if label != "error": + return None + if "parse tool_calls failed" in error_reason: + return "parse_tool_calls_failed" + if "action name not in candidate" in error_reason: + return "action_name_mismatch" + if "tool_calls and content are None" in error_reason: + return "empty_response" + return "error" + + # -- H1 + M3 ---------------------------------------------------------- + + def _create_tool_spans_from_log( + self, + inference_log, + inference_data, + chain_steps: List[ReactStepInvocation], + ) -> None: + """Post-hoc TOOL span creation from inference_log. + + Uses the per-chain STEP invocation list to parent each TOOL span on + the matching STEP span (H1). Sensitive tool-call attributes are + written explicitly on the span (M3) so they appear regardless of + ``OTEL_INSTRUMENTATION_GENAI_CAPTURE_*`` settings. + """ + if not isinstance(inference_log, dict): + return + + # round → SpanContext-bearing OTel context for parenting + step_ctx_by_round = {} + for step_inv in chain_steps: + if step_inv.round is None or step_inv.span is None: + continue + try: + step_ctx_by_round[step_inv.round] = set_span_in_context( + step_inv.span + ) + except Exception as e: # noqa: BLE001 + logger.debug("Failed to compute step parent context: %s", e) + + # tool name → description (for gen_ai.tool.description) + tool_desc_map = {} + tools = inference_data.get("tools") if isinstance( + inference_data, dict + ) else None + if isinstance(tools, list): + for tool in tools: + if not isinstance(tool, dict): + continue + func = tool.get("function") or tool + if not isinstance(func, dict): + continue + name = func.get("name") + desc = func.get("description") + if name: + tool_desc_map[name] = desc + + # Extract tool observations from final messages keyed by tool_call_id; + # wtb only embeds them in messages (not in inference_answer) for the + # tool_call branch. + observation_by_call_id = {} + messages = inference_data.get("messages") if isinstance( + inference_data, dict + ) else None + if isinstance(messages, list): + for msg in messages: + if not isinstance(msg, dict) or msg.get("role") != "tool": + continue + tid = msg.get("tool_call_id") + if tid is None: + continue + content = msg.get("content") + if content is None: + continue + observation_by_call_id[tid] = ( + content if isinstance(content, str) else _stringify(content) + ) + + for key in sorted(k for k in inference_log if k.startswith("step_")): + try: + step_idx = int(key[len("step_"):]) + except ValueError: + continue + round_num = step_idx + 1 + + step_data = inference_log[key] + if not isinstance(step_data, dict): + continue + output = step_data.get("inference_output") or {} + if not isinstance(output, dict): + continue + tool_calls = output.get("tool_calls") + label = output.get("current_action_name_label") + if not tool_calls or label != "correct": + continue + + answer_data = step_data.get("inference_answer") or {} + candidate = ( + answer_data.get("candidate_0_answer_function_list") + if isinstance(answer_data, dict) + else None + ) or {} + candidate_observation = ( + candidate.get("observation") + if isinstance(candidate, dict) + else None + ) + + parent_ctx = step_ctx_by_round.get(round_num) + + for tc in tool_calls: + if not isinstance(tc, dict): + continue + func = tc.get("function") or {} + if not isinstance(func, dict): + func = {} + tool_name = func.get("name", "unknown") + tool_id = tc.get("id") + tool_args_raw = func.get("arguments", "") + tool_args_str = ( + tool_args_raw + if isinstance(tool_args_raw, str) + else _stringify(tool_args_raw) + ) + + observation_str: Optional[str] = None + if tool_id is not None and tool_id in observation_by_call_id: + observation_str = observation_by_call_id[tool_id] + elif candidate_observation is not None: + observation_str = ( + candidate_observation + if isinstance(candidate_observation, str) + else _stringify(candidate_observation) + ) + + description = tool_desc_map.get(tool_name) + + invocation = ExecuteToolInvocation( + tool_name=tool_name, + tool_call_id=tool_id, + tool_call_arguments=tool_args_str, + tool_call_result=observation_str, + tool_type="function", + tool_description=description, + attributes={ + "wildtool.tool.execution_mode": "ground_truth_replay", + }, + ) + + try: + self._handler.start_execute_tool( + invocation, context=parent_ctx + ) + except Exception as e: # noqa: BLE001 + logger.debug("Failed to start_execute_tool: %s", e) + continue + + # M3: explicitly write tool_call sensitive attrs. The + # util-genai `_get_tool_call_data_attributes` helper guards + # these behind experimental-mode + content-capture-mode env + # vars which are not always set in real deployments. + tool_span = invocation.span + if tool_span is not None and tool_span.is_recording(): + try: + tool_span.set_attribute( + "gen_ai.tool.call.arguments", tool_args_str + ) + if observation_str is not None: + tool_span.set_attribute( + "gen_ai.tool.call.result", observation_str + ) + if description: + tool_span.set_attribute( + "gen_ai.tool.description", description + ) + except Exception as e: # noqa: BLE001 + logger.debug("Failed to set tool span attrs: %s", e) + + try: + self._handler.stop_execute_tool(invocation) + except Exception as e: # noqa: BLE001 + logger.debug("Failed to stop_execute_tool: %s", e) + + +class WildToolRequestWrapper: + """P4: Wraps BaseHandler._request_tool_call. + + Creates STEP span (ReactStepInvocation) before each LLM call. + Extracts latency from return value. Also writes the H2 provider-name + fallback attributes (``gen_ai.system`` + ``gen_ai.provider.name``) on + the STEP span so the new semconv attribute is present in the trace + even when the upstream OpenAI v2 instrumentation only emits the legacy + ``gen_ai.system``. + """ + + def __init__(self, handler: ExtendedTelemetryHandler): + self._handler = handler + + def __call__(self, wrapped, instance, args, kwargs): + if not _in_chain.get(): + return wrapped(*args, **kwargs) + + # Close the previous step (the natural end-of-step is when the next + # request fires). The STEP span's SpanContext stays valid as a + # parent for TOOL spans created later. + _close_active_step(self._handler) + + step_num = _step_counter.get() + 1 + _step_counter.set(step_num) + + step_inv = ReactStepInvocation(round=step_num) + try: + self._handler.start_react_step(step_inv) + except Exception as e: # noqa: BLE001 + logger.debug("Failed to start react step: %s", e) + return wrapped(*args, **kwargs) + + # H2: provider-name fallback attributes. Written on the STEP, not + # on the LLM span, because the LLM span is owned by the OpenAI v2 + # provider instrumentation and is created lazily inside the wtb + # request implementation. + if step_inv.span is not None and step_inv.span.is_recording(): + try: + step_inv.span.set_attribute( + "gen_ai.system", _PROVIDER_FALLBACK_NAME + ) + step_inv.span.set_attribute( + "gen_ai.provider.name", _PROVIDER_FALLBACK_NAME + ) + except Exception as e: # noqa: BLE001 + logger.debug("Failed to set provider fallback attrs: %s", e) + + # Track this step for H1 TOOL re-parenting. + chain_steps = _chain_step_invocations.get() + if chain_steps is not None: + chain_steps.append(step_inv) + _step_invocation.set(step_inv) + + try: + result = wrapped(*args, **kwargs) + if isinstance(result, tuple) and len(result) == 2: + _, latency = result + if step_inv.span and step_inv.span.is_recording(): + try: + step_inv.span.set_attribute( + "wildtool.latency", float(latency) + ) + except Exception as e: # noqa: BLE001 + logger.debug("Failed to set wildtool.latency: %s", e) + return result + except Exception as e: + step_inv.finish_reason = "error" + self._handler.fail_react_step( + step_inv, Error(message=str(e), type=type(e)) + ) + _step_invocation.set(None) + raise + + +class WildToolParseWrapper: + """P5: Wraps BaseHandler._parse_api_response. + + Extracts token counts from parsed response and sets them on the + current STEP span as attributes. + """ + + def __init__(self, handler: ExtendedTelemetryHandler): + self._handler = handler + + def __call__(self, wrapped, instance, args, kwargs): + result = wrapped(*args, **kwargs) + + step_inv = _step_invocation.get() + if step_inv and step_inv.span and step_inv.span.is_recording(): + if isinstance(result, dict): + input_t = result.get("input_token") + output_t = result.get("output_token") + if input_t is not None: + step_inv.span.set_attribute( + "gen_ai.usage.input_tokens", input_t + ) + if output_t is not None: + step_inv.span.set_attribute( + "gen_ai.usage.output_tokens", output_t + ) + + return result diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/src/opentelemetry/instrumentation/wildtool/package.py b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/src/opentelemetry/instrumentation/wildtool/package.py new file mode 100644 index 000000000..1ac5bcfee --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/src/opentelemetry/instrumentation/wildtool/package.py @@ -0,0 +1,2 @@ +_instruments = ("openai >= 1.0.0",) +_supports_metrics = False diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/src/opentelemetry/instrumentation/wildtool/utils.py b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/src/opentelemetry/instrumentation/wildtool/utils.py new file mode 100644 index 000000000..c26b7711d --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/src/opentelemetry/instrumentation/wildtool/utils.py @@ -0,0 +1,17 @@ +"""Utility functions for WildToolBench instrumentation.""" + +import json +from typing import Any, Optional + + +def safe_json_dumps(obj: Any, max_length: int = 10000) -> Optional[str]: + """Safely serialize object to JSON string with length limit.""" + if obj is None: + return None + try: + s = json.dumps(obj, ensure_ascii=False) + if len(s) > max_length: + return s[:max_length] + "...(truncated)" + return s + except (TypeError, ValueError): + return str(obj)[:max_length] diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/src/opentelemetry/instrumentation/wildtool/version.py b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/src/opentelemetry/instrumentation/wildtool/version.py new file mode 100644 index 000000000..3dc1f76bc --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/src/opentelemetry/instrumentation/wildtool/version.py @@ -0,0 +1 @@ +__version__ = "0.1.0" diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/__init__.py b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/conftest.py b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/conftest.py new file mode 100644 index 000000000..014186185 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/conftest.py @@ -0,0 +1,182 @@ +"""Test configuration for WildToolBench instrumentation tests.""" + +import json +import os + +import pytest + +os.environ.setdefault("OPENAI_API_KEY", "test_key_not_real") +os.environ.setdefault("OPENAI_BASE_URL", "http://localhost:9999/v1") + +from opentelemetry.instrumentation.wildtool import WildToolInstrumentor +from opentelemetry.sdk.trace import TracerProvider +from opentelemetry.sdk.trace.export import SimpleSpanProcessor +from opentelemetry.sdk.trace.export.in_memory_span_exporter import ( + InMemorySpanExporter, +) + + +def pytest_configure(config: pytest.Config): + os.environ["OTEL_SEMCONV_STABILITY_OPT_IN"] = "gen_ai_latest_experimental" + + +@pytest.fixture(scope="function", name="span_exporter") +def fixture_span_exporter(): + exporter = InMemorySpanExporter() + yield exporter + + +@pytest.fixture(scope="function", name="tracer_provider") +def fixture_tracer_provider(span_exporter): + provider = TracerProvider() + provider.add_span_processor(SimpleSpanProcessor(span_exporter)) + return provider + + +@pytest.fixture(scope="function") +def instrument(tracer_provider): + instrumentor = WildToolInstrumentor() + instrumentor.instrument( + tracer_provider=tracer_provider, + skip_dep_check=True, + ) + yield instrumentor + instrumentor.uninstrument() + + +# ==================== Minimal test data fixtures ==================== + + +def _make_chat_completion_response( + content=None, + tool_calls=None, + input_tokens=10, + output_tokens=5, + model="gpt-4o", +): + """Build a minimal ChatCompletion-like dict that can be JSON-serialized.""" + message = {"role": "assistant", "content": content or ""} + if tool_calls: + message["tool_calls"] = tool_calls + return { + "id": "chatcmpl-test", + "object": "chat.completion", + "model": model, + "choices": [{"index": 0, "message": message, "finish_reason": "stop"}], + "usage": { + "prompt_tokens": input_tokens, + "completion_tokens": output_tokens, + "total_tokens": input_tokens + output_tokens, + }, + } + + +class FakeChatCompletion: + """Mimics openai.types.chat.ChatCompletion enough for _parse_api_response.""" + + def __init__(self, data: dict): + self._data = data + + def json(self): + return json.dumps(self._data) + + def __getattr__(self, name): + return self._data[name] + + +@pytest.fixture() +def make_completion(): + """Factory fixture to build FakeChatCompletion objects.""" + + def _factory(**kwargs): + return FakeChatCompletion(_make_chat_completion_response(**kwargs)) + + return _factory + + +@pytest.fixture() +def simple_test_entry(): + """A minimal WildToolBench test_entry with 1 task, 1 step (prepare_to_answer).""" + return { + "id": "wild_tool_bench_test_001", + "english_env_info": "2025-01-01", + "english_tools": [ + { + "type": "function", + "function": { + "name": "get_weather", + "description": "Get weather for a city", + "parameters": { + "type": "object", + "properties": { + "city": {"type": "string"}, + }, + "required": ["city"], + }, + }, + } + ], + "english_tasks": ["What is the weather in Beijing?"], + "english_answer_list": [ + [ + { + "action": { + "name": "get_weather", + "arguments": {"city": "Beijing"}, + }, + "observation": "Sunny, 25°C", + "dependency_list": [], + }, + { + "action": { + "name": "prepare_to_answer", + "arguments": {}, + }, + "observation": "The weather in Beijing is Sunny, 25°C", + "dependency_list": [0], + }, + ] + ], + } + + +@pytest.fixture() +def tool_call_response_factory(): + """Factory to make tool_call ChatCompletion responses.""" + + def _factory(tool_name, arguments, tool_call_id="call_001"): + tc = [ + { + "id": tool_call_id, + "type": "function", + "function": { + "name": tool_name, + "arguments": ( + json.dumps(arguments) + if isinstance(arguments, dict) + else arguments + ), + }, + } + ] + return FakeChatCompletion( + _make_chat_completion_response(tool_calls=tc) + ) + + return _factory + + +@pytest.fixture() +def text_response_factory(): + """Factory to make text-only ChatCompletion responses.""" + + def _factory(content, input_tokens=10, output_tokens=5): + return FakeChatCompletion( + _make_chat_completion_response( + content=content, + input_tokens=input_tokens, + output_tokens=output_tokens, + ) + ) + + return _factory diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/test_agent_span.py b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/test_agent_span.py new file mode 100644 index 000000000..2929eeb33 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/test_agent_span.py @@ -0,0 +1,108 @@ +"""Tests for AGENT span (P2: inference_multi_turn).""" + +import json +from unittest.mock import patch + +import pytest +from opentelemetry.trace import StatusCode + +from wtb.model_handler.base_handler import BaseHandler + + +class _StubHandler(BaseHandler): + """Minimal handler subclass for testing AGENT span.""" + + def __init__(self): + super().__init__("test-model", 0.0) + self._step_responses = [] + self._step_idx = 0 + + def _request_tool_call(self, inference_data): + resp = self._step_responses[self._step_idx] + self._step_idx += 1 + return resp, 0.1 + + def _parse_api_response(self, api_response): + data = json.loads(api_response.json()) + choice = data["choices"][0] + message = choice["message"] + return { + "reasoning_content": None, + "content": message.get("content"), + "tool_calls": message.get("tool_calls"), + "input_token": data["usage"]["prompt_tokens"], + "output_token": data["usage"]["completion_tokens"], + } + + +class TestAgentSpan: + def test_agent_span_attributes( + self, span_exporter, instrument, simple_test_entry, make_completion, + tool_call_response_factory, text_response_factory, + ): + """AGENT span should exist with correct attributes and token aggregation.""" + handler = _StubHandler() + + # Step 0: model returns tool call for get_weather + resp0 = tool_call_response_factory( + "get_weather", {"city": "Beijing"}, "call_001" + ) + # Step 1: model returns text (prepare_to_answer match) + resp1 = text_response_factory( + "The weather in Beijing is Sunny, 25°C", + input_tokens=20, output_tokens=15, + ) + handler._step_responses = [resp0, resp1] + + result = handler.inference_multi_turn(simple_test_entry) + assert result is not None + + spans = span_exporter.get_finished_spans() + agent_spans = [s for s in spans if "invoke_agent" in s.name] + assert len(agent_spans) == 1 + + span = agent_spans[0] + assert span.name == "invoke_agent _StubHandler" + attrs = dict(span.attributes or {}) + assert attrs.get("gen_ai.span.kind") == "AGENT" + assert attrs.get("gen_ai.operation.name") == "invoke_agent" + assert attrs.get("gen_ai.framework") == "wildtool" + assert attrs.get("gen_ai.agent.name") == "_StubHandler" + assert attrs.get("gen_ai.conversation.id") == "wild_tool_bench_test_001" + assert attrs.get("gen_ai.request.model") == "test-model" + assert attrs.get("wildtool.turn_count") == 1 + + assert attrs.get("gen_ai.usage.input_tokens") == 30 + assert attrs.get("gen_ai.usage.output_tokens") == 20 + + def test_agent_parent_is_entry( + self, span_exporter, instrument, simple_test_entry, + tool_call_response_factory, text_response_factory, + ): + """When called via multi_threaded_inference, AGENT span should be child of ENTRY.""" + from wtb._llm_response_generation import multi_threaded_inference + + handler = _StubHandler() + resp0 = tool_call_response_factory( + "get_weather", {"city": "Beijing"}, "call_001" + ) + resp1 = text_response_factory("The weather in Beijing is Sunny, 25°C") + handler._step_responses = [resp0, resp1] + + test_case = simple_test_entry.copy() + multi_threaded_inference(handler, "test-model", test_case) + + spans = span_exporter.get_finished_spans() + entry_spans = [ + s for s in spans if s.name == "enter_ai_application_system" + ] + agent_spans = [s for s in spans if "invoke_agent" in s.name] + + assert len(entry_spans) == 1 + assert len(agent_spans) == 1 + + entry = entry_spans[0] + agent = agent_spans[0] + assert agent.context.trace_id == entry.context.trace_id + assert agent.parent is not None + assert agent.parent.span_id == entry.context.span_id diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/test_chain_step_tool_spans.py b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/test_chain_step_tool_spans.py new file mode 100644 index 000000000..d7dd7b4aa --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/test_chain_step_tool_spans.py @@ -0,0 +1,283 @@ +"""Tests for CHAIN / STEP / TOOL spans (P3, P4, P5).""" + +import json + +import pytest +from opentelemetry.trace import StatusCode + +from wtb.model_handler.base_handler import BaseHandler + + +class _StubHandler(BaseHandler): + """Minimal handler subclass with controllable responses.""" + + def __init__(self): + super().__init__("test-model", 0.0) + self._step_responses = [] + self._step_idx = 0 + + def _request_tool_call(self, inference_data): + resp = self._step_responses[self._step_idx] + self._step_idx += 1 + return resp, 0.05 + + def _parse_api_response(self, api_response): + data = json.loads(api_response.json()) + choice = data["choices"][0] + message = choice["message"] + return { + "reasoning_content": None, + "content": message.get("content"), + "tool_calls": message.get("tool_calls"), + "input_token": data["usage"]["prompt_tokens"], + "output_token": data["usage"]["completion_tokens"], + } + + +class TestChainSpan: + def test_chain_span_per_task( + self, span_exporter, instrument, simple_test_entry, + tool_call_response_factory, text_response_factory, + ): + """Each task should produce one CHAIN span with correct attributes.""" + handler = _StubHandler() + resp0 = tool_call_response_factory( + "get_weather", {"city": "Beijing"}, "call_001" + ) + resp1 = text_response_factory("The weather in Beijing is Sunny, 25°C") + handler._step_responses = [resp0, resp1] + + handler.inference_multi_turn(simple_test_entry) + + spans = span_exporter.get_finished_spans() + chain_spans = [s for s in spans if s.name.startswith("workflow")] + assert len(chain_spans) == 1 + + chain = chain_spans[0] + assert chain.name == "workflow task_0" + attrs = dict(chain.attributes or {}) + assert attrs.get("gen_ai.span.kind") == "CHAIN" + assert attrs.get("gen_ai.operation.name") == "workflow" + assert attrs.get("gen_ai.framework") == "wildtool" + assert attrs.get("wildtool.task_idx") == 0 + assert attrs.get("wildtool.test_entry_id") == "wild_tool_bench_test_001" + assert attrs.get("wildtool.action_name_label") == "correct" + assert attrs.get("wildtool.is_optimal") is True + + def test_chain_parent_is_agent( + self, span_exporter, instrument, simple_test_entry, + tool_call_response_factory, text_response_factory, + ): + """CHAIN span should be child of AGENT span.""" + handler = _StubHandler() + resp0 = tool_call_response_factory( + "get_weather", {"city": "Beijing"}, "call_001" + ) + resp1 = text_response_factory("The weather in Beijing is Sunny, 25°C") + handler._step_responses = [resp0, resp1] + + handler.inference_multi_turn(simple_test_entry) + + spans = span_exporter.get_finished_spans() + agent_spans = [s for s in spans if "invoke_agent" in s.name] + chain_spans = [s for s in spans if s.name.startswith("workflow")] + + assert len(agent_spans) == 1 + assert len(chain_spans) == 1 + + agent = agent_spans[0] + chain = chain_spans[0] + assert chain.context.trace_id == agent.context.trace_id + assert chain.parent is not None + assert chain.parent.span_id == agent.context.span_id + + +class TestStepSpans: + def test_step_spans_per_chain( + self, span_exporter, instrument, simple_test_entry, + tool_call_response_factory, text_response_factory, + ): + """Each _request_tool_call invocation should produce a STEP span.""" + handler = _StubHandler() + resp0 = tool_call_response_factory( + "get_weather", {"city": "Beijing"}, "call_001" + ) + resp1 = text_response_factory("The weather in Beijing is Sunny, 25°C") + handler._step_responses = [resp0, resp1] + + handler.inference_multi_turn(simple_test_entry) + + spans = span_exporter.get_finished_spans() + step_spans = [s for s in spans if s.name == "react step"] + assert len(step_spans) == 2 + + attrs0 = dict(step_spans[0].attributes or {}) + attrs1 = dict(step_spans[1].attributes or {}) + rounds = sorted([attrs0.get("gen_ai.react.round"), attrs1.get("gen_ai.react.round")]) + assert rounds == [1, 2] + + for ss in step_spans: + a = dict(ss.attributes or {}) + assert a.get("gen_ai.span.kind") == "STEP" + assert a.get("gen_ai.operation.name") == "react" + + def test_step_parent_is_chain( + self, span_exporter, instrument, simple_test_entry, + tool_call_response_factory, text_response_factory, + ): + """STEP spans should be children of CHAIN span.""" + handler = _StubHandler() + resp0 = tool_call_response_factory( + "get_weather", {"city": "Beijing"}, "call_001" + ) + resp1 = text_response_factory("The weather in Beijing is Sunny, 25°C") + handler._step_responses = [resp0, resp1] + + handler.inference_multi_turn(simple_test_entry) + + spans = span_exporter.get_finished_spans() + chain_spans = [s for s in spans if s.name.startswith("workflow")] + step_spans = [s for s in spans if s.name == "react step"] + + assert len(chain_spans) == 1 + chain = chain_spans[0] + + for ss in step_spans: + assert ss.context.trace_id == chain.context.trace_id + assert ss.parent is not None + assert ss.parent.span_id == chain.context.span_id + + def test_step_token_attributes( + self, span_exporter, instrument, simple_test_entry, + tool_call_response_factory, text_response_factory, + ): + """STEP span should have gen_ai.usage.input_tokens and output_tokens.""" + handler = _StubHandler() + resp0 = tool_call_response_factory( + "get_weather", {"city": "Beijing"}, "call_001" + ) + resp1 = text_response_factory( + "The weather in Beijing is Sunny, 25°C", + input_tokens=25, output_tokens=12, + ) + handler._step_responses = [resp0, resp1] + + handler.inference_multi_turn(simple_test_entry) + + spans = span_exporter.get_finished_spans() + step_spans = sorted( + [s for s in spans if s.name == "react step"], + key=lambda s: s.attributes.get("gen_ai.react.round", 0), + ) + assert len(step_spans) == 2 + + # First step: default 10 input, 5 output from make_completion defaults + a0 = dict(step_spans[0].attributes or {}) + assert a0.get("gen_ai.usage.input_tokens") == 10 + assert a0.get("gen_ai.usage.output_tokens") == 5 + + # Second step: 25 input, 12 output + a1 = dict(step_spans[1].attributes or {}) + assert a1.get("gen_ai.usage.input_tokens") == 25 + assert a1.get("gen_ai.usage.output_tokens") == 12 + + +class TestToolSpans: + def test_tool_span_attributes( + self, span_exporter, instrument, simple_test_entry, + tool_call_response_factory, text_response_factory, + ): + """TOOL span should have correct attributes including execution_mode.""" + handler = _StubHandler() + resp0 = tool_call_response_factory( + "get_weather", {"city": "Beijing"}, "call_001" + ) + resp1 = text_response_factory("The weather in Beijing is Sunny, 25°C") + handler._step_responses = [resp0, resp1] + + handler.inference_multi_turn(simple_test_entry) + + spans = span_exporter.get_finished_spans() + tool_spans = [s for s in spans if "execute_tool" in s.name] + assert len(tool_spans) == 1 + + tool = tool_spans[0] + assert tool.name == "execute_tool get_weather" + attrs = dict(tool.attributes or {}) + assert attrs.get("gen_ai.span.kind") == "TOOL" + assert attrs.get("gen_ai.operation.name") == "execute_tool" + assert attrs.get("gen_ai.tool.name") == "get_weather" + assert attrs.get("gen_ai.tool.type") == "function" + assert ( + attrs.get("wildtool.tool.execution_mode") == "ground_truth_replay" + ) + + def test_tool_span_parent_is_chain( + self, span_exporter, instrument, simple_test_entry, + tool_call_response_factory, text_response_factory, + ): + """TOOL spans share the CHAIN trace_id (parent is STEP after Round 2).""" + handler = _StubHandler() + resp0 = tool_call_response_factory( + "get_weather", {"city": "Beijing"}, "call_001" + ) + resp1 = text_response_factory("The weather in Beijing is Sunny, 25°C") + handler._step_responses = [resp0, resp1] + + handler.inference_multi_turn(simple_test_entry) + + spans = span_exporter.get_finished_spans() + chain_spans = [s for s in spans if s.name.startswith("workflow")] + tool_spans = [s for s in spans if "execute_tool" in s.name] + + assert len(chain_spans) == 1 + assert len(tool_spans) >= 1 + + chain = chain_spans[0] + for ts in tool_spans: + assert ts.context.trace_id == chain.context.trace_id + + +class TestSpanHierarchy: + def test_full_hierarchy( + self, span_exporter, instrument, simple_test_entry, + tool_call_response_factory, text_response_factory, + ): + """Verify ENTRY → AGENT → CHAIN → STEP hierarchy and consistent trace_id.""" + from wtb._llm_response_generation import multi_threaded_inference + + handler = _StubHandler() + resp0 = tool_call_response_factory( + "get_weather", {"city": "Beijing"}, "call_001" + ) + resp1 = text_response_factory("The weather in Beijing is Sunny, 25°C") + handler._step_responses = [resp0, resp1] + + test_case = simple_test_entry.copy() + multi_threaded_inference(handler, "test-model", test_case) + + spans = span_exporter.get_finished_spans() + + entry = [s for s in spans if s.name == "enter_ai_application_system"] + agent = [s for s in spans if "invoke_agent" in s.name] + chain = [s for s in spans if s.name.startswith("workflow")] + step = [s for s in spans if s.name == "react step"] + tool = [s for s in spans if "execute_tool" in s.name] + + assert len(entry) == 1 + assert len(agent) == 1 + assert len(chain) == 1 + assert len(step) == 2 + assert len(tool) >= 1 + + trace_id = entry[0].context.trace_id + for s in spans: + assert s.context.trace_id == trace_id + + # AGENT parent = ENTRY + assert agent[0].parent.span_id == entry[0].context.span_id + # CHAIN parent = AGENT + assert chain[0].parent.span_id == agent[0].context.span_id + # STEP parent = CHAIN + for s in step: + assert s.parent.span_id == chain[0].context.span_id diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/test_entry_span.py b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/test_entry_span.py new file mode 100644 index 000000000..834e7dd13 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/test_entry_span.py @@ -0,0 +1,115 @@ +"""Tests for ENTRY span (P1: multi_threaded_inference). + +Module-level imports of ``wtb._llm_response_generation.multi_threaded_inference`` +must be avoided: ``wrapt.wrap_function_wrapper`` patches the attribute on the +module, but a pre-imported local binding still references the original +unwrapped function. All tests therefore import the symbol lazily after the +``instrument`` fixture has run. +""" + +import pytest +from opentelemetry.trace import StatusCode + +from wtb.model_handler.base_handler import BaseHandler + + +class _StubHandler(BaseHandler): + """Minimal handler subclass for testing. + + Overrides ``inference`` so the multi_threaded_inference wrapper invokes a + deterministic, side-effect-free body that returns a fake result dict and + therefore exercises only the ENTRY span codepath. + """ + + def __init__(self): + super().__init__("test-model", 0.0) + + def _request_tool_call(self, inference_data): + raise NotImplementedError + + def _parse_api_response(self, api_response): + raise NotImplementedError + + def inference(self, test_entry): + return [ + { + "action_name_label": "correct", + "is_optimal": True, + "inference_log": {}, + "latency": [0.1], + "input_token_count": [10], + "output_token_count": [5], + } + ] + + +class TestEntrySpan: + def test_entry_span_created(self, span_exporter, instrument): + """ENTRY span should be created with correct attributes.""" + from wtb._llm_response_generation import multi_threaded_inference + + handler = _StubHandler() + test_case = { + "id": "wild_tool_bench_test_001", + "english_tasks": ["task1", "task2"], + } + + result = multi_threaded_inference(handler, "gpt-4o", test_case) + + assert result is not None + assert result["id"] == "wild_tool_bench_test_001" + + spans = span_exporter.get_finished_spans() + entry_spans = [ + s for s in spans if s.name == "enter_ai_application_system" + ] + assert len(entry_spans) == 1 + + span = entry_spans[0] + attrs = dict(span.attributes or {}) + assert attrs.get("gen_ai.span.kind") == "ENTRY" + assert attrs.get("gen_ai.operation.name") == "enter" + assert attrs.get("gen_ai.framework") == "wildtool" + assert attrs.get("gen_ai.session.id") == "wild_tool_bench_test_001" + assert attrs.get("gen_ai.request.model") == "gpt-4o" + assert attrs.get("wildtool.turn_count") == 2 + # ENTRY spans rely on default OTel status semantics: success leaves + # the span UNSET, failures explicitly mark it ERROR. + assert span.status.status_code != StatusCode.ERROR + + def test_entry_span_error_path(self, span_exporter, instrument): + """The ENTRY wrapper marks the span ERROR when the wrapped callable + raises an unhandled exception. + + ``multi_threaded_inference`` swallows non-rate-limit errors itself + (see test_error_scenarios.test_entry_span_captures_retry_error_path + for that path). To exercise the wrapper's failure branch directly we + invoke the underlying ``WildToolEntryWrapper`` with a callable that + deliberately raises, bypassing ``multi_threaded_inference``'s own + error handling. + """ + from opentelemetry.instrumentation.wildtool._wrappers import ( + WildToolEntryWrapper, + ) + + wrapper = WildToolEntryWrapper(instrument._handler) + + def _raising(handler, model_name, test_case): + raise RuntimeError("API connection failed") + + handler = _StubHandler() + test_case = { + "id": "wild_tool_bench_test_002", + "english_tasks": ["task1"], + } + + with pytest.raises(RuntimeError, match="API connection failed"): + wrapper(_raising, None, (handler, "gpt-4o", test_case), {}) + + spans = span_exporter.get_finished_spans() + entry_spans = [ + s for s in spans if s.name == "enter_ai_application_system" + ] + assert len(entry_spans) == 1 + span = entry_spans[0] + assert span.status.status_code == StatusCode.ERROR diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/test_error_scenarios.py b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/test_error_scenarios.py new file mode 100644 index 000000000..c14a3f40c --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/test_error_scenarios.py @@ -0,0 +1,135 @@ +"""Tests for error/edge-case scenarios.""" + +import json + +import pytest +from opentelemetry.trace import StatusCode + +from wtb.model_handler.base_handler import BaseHandler + + +class _StubHandler(BaseHandler): + """Handler with controllable step responses.""" + + def __init__(self): + super().__init__("test-model", 0.0) + self._step_responses = [] + self._step_idx = 0 + + def _request_tool_call(self, inference_data): + resp = self._step_responses[self._step_idx] + self._step_idx += 1 + if isinstance(resp, Exception): + raise resp + return resp, 0.05 + + def _parse_api_response(self, api_response): + data = json.loads(api_response.json()) + choice = data["choices"][0] + message = choice["message"] + return { + "reasoning_content": None, + "content": message.get("content"), + "tool_calls": message.get("tool_calls"), + "input_token": data["usage"]["prompt_tokens"], + "output_token": data["usage"]["completion_tokens"], + } + + +class TestErrorScenarios: + def test_action_name_mismatch( + self, span_exporter, instrument, simple_test_entry, + tool_call_response_factory, + ): + """When model calls wrong tool, CHAIN span should still be OK with error label.""" + handler = _StubHandler() + # Model calls wrong_tool instead of get_weather + resp0 = tool_call_response_factory( + "wrong_tool", {"x": 1}, "call_bad" + ) + handler._step_responses = [resp0] + + handler.inference_multi_turn(simple_test_entry) + + spans = span_exporter.get_finished_spans() + chain_spans = [s for s in spans if s.name.startswith("workflow")] + assert len(chain_spans) == 1 + + chain = chain_spans[0] + attrs = dict(chain.attributes or {}) + assert attrs.get("wildtool.action_name_label") == "error" + assert chain.status.status_code == StatusCode.OK + + def test_empty_response( + self, span_exporter, instrument, simple_test_entry, + make_completion, + ): + """When model returns no content and no tool_calls, process terminates gracefully.""" + from tests.conftest import FakeChatCompletion, _make_chat_completion_response + + handler = _StubHandler() + resp = FakeChatCompletion( + _make_chat_completion_response(content="", tool_calls=None) + ) + handler._step_responses = [resp] + + handler.inference_multi_turn(simple_test_entry) + + spans = span_exporter.get_finished_spans() + chain_spans = [s for s in spans if s.name.startswith("workflow")] + assert len(chain_spans) == 1 + attrs = dict(chain_spans[0].attributes or {}) + assert attrs.get("wildtool.action_name_label") == "error" + + def test_request_tool_call_exception_sets_error( + self, span_exporter, instrument, simple_test_entry, + ): + """Exception in _request_tool_call should produce ERROR on STEP span and propagate.""" + handler = _StubHandler() + handler._step_responses = [RuntimeError("Connection timeout")] + + with pytest.raises(RuntimeError, match="Connection timeout"): + handler.inference_multi_turn(simple_test_entry) + + spans = span_exporter.get_finished_spans() + step_spans = [s for s in spans if s.name == "react step"] + assert len(step_spans) == 1 + assert step_spans[0].status.status_code == StatusCode.ERROR + + chain_spans = [s for s in spans if s.name.startswith("workflow")] + assert len(chain_spans) == 1 + assert chain_spans[0].status.status_code == StatusCode.ERROR + + def test_entry_span_captures_retry_error_path( + self, span_exporter, instrument, + ): + """multi_threaded_inference catches non-rate-limit errors and returns error dict. + ENTRY span should still complete successfully (not raise).""" + from wtb._llm_response_generation import multi_threaded_inference + + handler = _StubHandler() + + def failing_inference(test_entry): + raise ValueError("Invalid JSON from model") + + handler.inference = failing_inference + + test_case = { + "id": "wild_tool_bench_err_001", + "english_tasks": ["task1"], + } + + # multi_threaded_inference catches non-rate-limit errors + result = multi_threaded_inference(handler, "test-model", test_case) + assert "Error during inference" in result["result"] + + spans = span_exporter.get_finished_spans() + entry_spans = [ + s for s in spans if s.name == "enter_ai_application_system" + ] + assert len(entry_spans) == 1 + # multi_threaded_inference's own try/except converts the error into a + # normal return, so the ENTRY wrapper observes a successful call and + # leaves the span at the default UNSET status (definitely not ERROR). + span = entry_spans[0] + assert span.status.status_code != StatusCode.ERROR diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/test_instrumentor.py b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/test_instrumentor.py new file mode 100644 index 000000000..a8be5b4da --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/test_instrumentor.py @@ -0,0 +1,20 @@ +"""Tests for WildToolInstrumentor lifecycle.""" + +from opentelemetry.instrumentation.wildtool import WildToolInstrumentor + + +class TestWildToolInstrumentor: + def test_instrument_and_uninstrument(self, tracer_provider): + instrumentor = WildToolInstrumentor() + instrumentor.instrument( + tracer_provider=tracer_provider, + skip_dep_check=True, + ) + assert instrumentor._handler is not None + instrumentor.uninstrument() + assert instrumentor._handler is None + + def test_instrumentation_dependencies(self): + instrumentor = WildToolInstrumentor() + deps = instrumentor.instrumentation_dependencies() + assert ("openai >= 1.0.0",) == deps diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/test_round2_fixes.py b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/test_round2_fixes.py new file mode 100644 index 000000000..9f4f4d895 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-wildtool/tests/test_round2_fixes.py @@ -0,0 +1,441 @@ +"""Round 2 regression tests covering the H1 / H2 / M1 / M2 / M3 fixes. + +See ``llm-dev/execute.md`` § "修订记录 (Round 2 fix)" and +``example-deploy/validation/SUMMARY.md`` for the original validation gaps +addressed by these tests. +""" + +import json + +import pytest +from opentelemetry.trace import StatusCode + +from wtb.model_handler.base_handler import BaseHandler + + +class _StubHandler(BaseHandler): + """Minimal handler with controllable LLM responses (no real network).""" + + def __init__(self): + super().__init__("test-model", 0.0) + self._step_responses = [] + self._step_idx = 0 + + def _request_tool_call(self, inference_data): + resp = self._step_responses[self._step_idx] + self._step_idx += 1 + if isinstance(resp, Exception): + raise resp + return resp, 0.05 + + def _parse_api_response(self, api_response): + data = json.loads(api_response.json()) + choice = data["choices"][0] + message = choice["message"] + return { + "reasoning_content": None, + "content": message.get("content"), + "tool_calls": message.get("tool_calls"), + "input_token": data["usage"]["prompt_tokens"], + "output_token": data["usage"]["completion_tokens"], + } + + +def _spans_by_kind(spans, kind): + return [s for s in spans if (s.attributes or {}).get("gen_ai.span.kind") == kind] + + +def _spans_named(spans, name): + return [s for s in spans if s.name == name] + + +def _step_for_round(spans, round_num): + for s in _spans_named(spans, "react step"): + attrs = s.attributes or {} + if attrs.get("gen_ai.react.round") == round_num: + return s + raise AssertionError(f"no STEP span found for round={round_num}") + + +# ============================================================================ +# H1: TOOL span parent_span_id == STEP span_id (was CHAIN before fix) +# ============================================================================ + + +class TestToolParentIsStep: + def test_single_tool_parent_is_step_round_one( + self, span_exporter, instrument, simple_test_entry, + tool_call_response_factory, text_response_factory, + ): + """The single TOOL span in simple_test_entry should be a child of the + first STEP span (round=1), not the CHAIN span.""" + handler = _StubHandler() + resp0 = tool_call_response_factory( + "get_weather", {"city": "Beijing"}, "call_001" + ) + resp1 = text_response_factory("The weather in Beijing is Sunny, 25°C") + handler._step_responses = [resp0, resp1] + + handler.inference_multi_turn(simple_test_entry) + + spans = span_exporter.get_finished_spans() + tool_spans = _spans_by_kind(spans, "TOOL") + assert len(tool_spans) == 1, [s.name for s in spans] + + tool = tool_spans[0] + step_round1 = _step_for_round(spans, 1) + chain = _spans_by_kind(spans, "CHAIN")[0] + + # H1 core assertion: parent is STEP, not CHAIN. + assert tool.parent is not None + assert tool.parent.span_id == step_round1.context.span_id, ( + "TOOL parent should be STEP round=1, got " + f"{tool.parent.span_id} (STEP={step_round1.context.span_id}, " + f"CHAIN={chain.context.span_id})" + ) + assert tool.parent.span_id != chain.context.span_id + + # And trace_id of course remains consistent. + assert tool.context.trace_id == step_round1.context.trace_id + + def test_multi_step_each_tool_parented_to_correct_step( + self, span_exporter, instrument, + tool_call_response_factory, text_response_factory, + ): + """multi-step scenario: 2 successful tool steps + 1 prepare_to_answer. + + Each TOOL span must be parented to the STEP span of its own round, + not to the CHAIN or to a different round's STEP. + """ + handler = _StubHandler() + # Test entry with 2 tool steps (search, lookup) then prepare_to_answer. + test_entry = { + "id": "wild_tool_bench_multi_001", + "english_env_info": "2025-01-01", + "english_tools": [ + { + "type": "function", + "function": { + "name": "search", + "description": "Search items", + "parameters": { + "type": "object", + "properties": {"q": {"type": "string"}}, + "required": ["q"], + }, + }, + }, + { + "type": "function", + "function": { + "name": "lookup", + "description": "Look up details", + "parameters": { + "type": "object", + "properties": {"id": {"type": "string"}}, + "required": ["id"], + }, + }, + }, + ], + "english_tasks": ["Find and summarize item X"], + "english_answer_list": [ + [ + { + "action": {"name": "search", "arguments": {"q": "X"}}, + "observation": "found:item_42", + "dependency_list": [], + }, + { + "action": {"name": "lookup", "arguments": {"id": "item_42"}}, + "observation": "details:hello", + "dependency_list": [0], + }, + { + "action": {"name": "prepare_to_answer", "arguments": {}}, + "observation": "Item X is hello.", + "dependency_list": [1], + }, + ] + ], + } + + resp_step1 = tool_call_response_factory( + "search", {"q": "X"}, "call_search_1" + ) + resp_step2 = tool_call_response_factory( + "lookup", {"id": "item_42"}, "call_lookup_1" + ) + resp_step3 = text_response_factory("Item X is hello.") + handler._step_responses = [resp_step1, resp_step2, resp_step3] + + handler.inference_multi_turn(test_entry) + + spans = span_exporter.get_finished_spans() + tool_spans = sorted( + _spans_by_kind(spans, "TOOL"), + key=lambda s: (s.attributes or {}).get("gen_ai.tool.name") or "", + ) + assert len(tool_spans) == 2, [s.name for s in spans] + + step_round1 = _step_for_round(spans, 1) + step_round2 = _step_for_round(spans, 2) + chain = _spans_by_kind(spans, "CHAIN")[0] + + lookup_tool = next( + t for t in tool_spans + if (t.attributes or {}).get("gen_ai.tool.name") == "lookup" + ) + search_tool = next( + t for t in tool_spans + if (t.attributes or {}).get("gen_ai.tool.name") == "search" + ) + + # search → STEP round=1, lookup → STEP round=2 + assert search_tool.parent.span_id == step_round1.context.span_id + assert lookup_tool.parent.span_id == step_round2.context.span_id + # Neither parented on CHAIN (the regression we are fixing) + for t in tool_spans: + assert t.parent.span_id != chain.context.span_id + assert t.context.trace_id == chain.context.trace_id + + +# ============================================================================ +# M1: CHAIN span carries input.value and output.value +# ============================================================================ + + +class TestChainInputOutputValue: + def test_chain_input_value_and_output_value( + self, span_exporter, instrument, simple_test_entry, + tool_call_response_factory, text_response_factory, + ): + handler = _StubHandler() + resp0 = tool_call_response_factory( + "get_weather", {"city": "Beijing"}, "call_001" + ) + resp1 = text_response_factory("The weather in Beijing is Sunny, 25°C") + handler._step_responses = [resp0, resp1] + + handler.inference_multi_turn(simple_test_entry) + + spans = span_exporter.get_finished_spans() + chain_spans = _spans_by_kind(spans, "CHAIN") + assert len(chain_spans) == 1 + attrs = dict(chain_spans[0].attributes or {}) + + # input.value: last user message of the chain (prepared by wtb's + # _pre_messages_processing which appends the current task as user). + assert "input.value" in attrs, attrs + assert attrs["input.value"] == "What is the weather in Beijing?" + + # output.value: JSON containing action_name_label, task_idx, is_optimal. + assert "output.value" in attrs, attrs + out = json.loads(attrs["output.value"]) + assert out["action_name_label"] == "correct" + assert out["task_idx"] == 0 + assert out["is_optimal"] is True + + def test_chain_input_value_truncated_when_long( + self, span_exporter, instrument, + tool_call_response_factory, text_response_factory, + ): + """Very long user content should be truncated to keep span attribute small.""" + handler = _StubHandler() + long_text = "x" * 5000 + test_entry = { + "id": "wild_tool_bench_long_001", + "english_env_info": "2025-01-01", + "english_tools": [ + { + "type": "function", + "function": { + "name": "noop", + "description": "noop", + "parameters": {"type": "object", "properties": {}}, + }, + } + ], + "english_tasks": [long_text], + "english_answer_list": [ + [ + { + "action": {"name": "prepare_to_answer", "arguments": {}}, + "observation": "ok", + "dependency_list": [], + } + ] + ], + } + handler._step_responses = [text_response_factory("ok")] + + handler.inference_multi_turn(test_entry) + + spans = span_exporter.get_finished_spans() + chain = _spans_by_kind(spans, "CHAIN")[0] + attrs = dict(chain.attributes or {}) + assert "input.value" in attrs + # Default cap is 4096; truncated form must be <= cap + suffix length. + assert len(attrs["input.value"]) <= 4096 + len("...(truncated)") + assert attrs["input.value"].startswith("xxx") + + +# ============================================================================ +# M2: STEP span carries gen_ai.react.finish_reason on error paths +# ============================================================================ + + +class TestStepFinishReason: + def test_finish_reason_action_name_mismatch( + self, span_exporter, instrument, simple_test_entry, + tool_call_response_factory, + ): + handler = _StubHandler() + # wrong tool name → wtb's "action name not in candidate" branch + handler._step_responses = [ + tool_call_response_factory("wrong_tool", {"x": 1}, "call_bad") + ] + + handler.inference_multi_turn(simple_test_entry) + + spans = span_exporter.get_finished_spans() + steps = _spans_named(spans, "react step") + assert len(steps) == 1 + attrs = dict(steps[0].attributes or {}) + assert attrs.get("gen_ai.react.finish_reason") == "action_name_mismatch" + + def test_finish_reason_empty_response( + self, span_exporter, instrument, simple_test_entry, make_completion, + ): + """Empty content + no tool_calls → STEP gets finish_reason=empty_response.""" + from tests.conftest import ( + FakeChatCompletion, + _make_chat_completion_response, + ) + + handler = _StubHandler() + handler._step_responses = [ + FakeChatCompletion( + _make_chat_completion_response(content="", tool_calls=None) + ) + ] + + handler.inference_multi_turn(simple_test_entry) + + spans = span_exporter.get_finished_spans() + steps = _spans_named(spans, "react step") + assert len(steps) == 1 + attrs = dict(steps[0].attributes or {}) + assert attrs.get("gen_ai.react.finish_reason") == "empty_response" + + def test_finish_reason_request_exception( + self, span_exporter, instrument, simple_test_entry, + ): + """Exception in _request_tool_call → STEP ERROR + finish_reason=error.""" + handler = _StubHandler() + handler._step_responses = [RuntimeError("Boom")] + + with pytest.raises(RuntimeError): + handler.inference_multi_turn(simple_test_entry) + + spans = span_exporter.get_finished_spans() + steps = _spans_named(spans, "react step") + assert len(steps) == 1 + attrs = dict(steps[0].attributes or {}) + assert steps[0].status.status_code == StatusCode.ERROR + assert attrs.get("gen_ai.react.finish_reason") == "error" + + def test_finish_reason_omitted_on_success( + self, span_exporter, instrument, simple_test_entry, + tool_call_response_factory, text_response_factory, + ): + """Successful steps should NOT have a finish_reason (per execute.md).""" + handler = _StubHandler() + handler._step_responses = [ + tool_call_response_factory( + "get_weather", {"city": "Beijing"}, "call_001" + ), + text_response_factory("OK"), + ] + handler.inference_multi_turn(simple_test_entry) + + spans = span_exporter.get_finished_spans() + for s in _spans_named(spans, "react step"): + attrs = dict(s.attributes or {}) + assert "gen_ai.react.finish_reason" not in attrs, ( + f"unexpected finish_reason on success step round=" + f"{attrs.get('gen_ai.react.round')}: {attrs.get('gen_ai.react.finish_reason')}" + ) + + +# ============================================================================ +# M3: TOOL span carries gen_ai.tool.call.arguments / result / description +# (and keeps wildtool.tool.execution_mode) +# ============================================================================ + + +class TestToolSensitiveAttributes: + def test_tool_args_result_description_and_execution_mode( + self, span_exporter, instrument, simple_test_entry, + tool_call_response_factory, text_response_factory, + ): + handler = _StubHandler() + resp0 = tool_call_response_factory( + "get_weather", {"city": "Beijing"}, "call_001" + ) + resp1 = text_response_factory("Sunny day") + handler._step_responses = [resp0, resp1] + + handler.inference_multi_turn(simple_test_entry) + + spans = span_exporter.get_finished_spans() + tool_spans = _spans_by_kind(spans, "TOOL") + assert len(tool_spans) == 1 + attrs = dict(tool_spans[0].attributes or {}) + + # M3 explicit attrs. + args_attr = attrs.get("gen_ai.tool.call.arguments") + assert args_attr is not None + assert json.loads(args_attr) == {"city": "Beijing"} + + # observation comes from the appended {"role": "tool", ...} message + # written by wtb after the call matches the answer; it's a string. + result_attr = attrs.get("gen_ai.tool.call.result") + assert result_attr == "Sunny, 25°C", attrs + + # description sourced from inference_data["tools"][i].function.description + assert attrs.get("gen_ai.tool.description") == "Get weather for a city" + + # Existing custom attribute must still be present. + assert ( + attrs.get("wildtool.tool.execution_mode") + == "ground_truth_replay" + ) + + +# ============================================================================ +# H2: STEP span carries gen_ai.system / gen_ai.provider.name fallback +# ============================================================================ + + +class TestStepProviderFallback: + def test_step_has_provider_name_fallback( + self, span_exporter, instrument, simple_test_entry, + tool_call_response_factory, text_response_factory, + ): + handler = _StubHandler() + handler._step_responses = [ + tool_call_response_factory( + "get_weather", {"city": "Beijing"}, "call_001" + ), + text_response_factory("OK"), + ] + handler.inference_multi_turn(simple_test_entry) + + spans = span_exporter.get_finished_spans() + steps = _spans_named(spans, "react step") + assert len(steps) == 2 + for s in steps: + attrs = dict(s.attributes or {}) + assert attrs.get("gen_ai.system") == "openai", attrs + assert attrs.get("gen_ai.provider.name") == "openai", attrs From 40b1b2ccfe10ace44bac8bcb63b2673653e9e82c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=89=A7=E6=80=9D?= Date: Thu, 7 May 2026 18:35:13 +0800 Subject: [PATCH 8/8] feat: add support for new tool integration This commit introduces support for a new tool, enhancing the existing functionality and maintaining compatibility with previous integrations. Change-Id: I674acb157591b4bee6f951defbbc8a57135ce036 Co-authored-by: 123liuziming <32130965+123liuziming@users.noreply.github.com> --- .../README.rst | 90 + .../pyproject.toml | 50 + .../instrumentation/openhands/__init__.py | 265 ++ .../instrumentation/openhands/config.py | 25 + .../openhands/internal/__init__.py | 1 + .../openhands/internal/constants.py | 12 + .../openhands/internal/session_context.py | 196 ++ .../openhands/internal/utils.py | 190 ++ .../openhands/internal/v0_wrappers.py | 2535 +++++++++++++++++ .../instrumentation/openhands/package.py | 1 + .../instrumentation/openhands/version.py | 1 + .../test-requirements.txt | 9 + .../tests/__init__.py | 0 .../tests/conftest.py | 244 ++ .../tests/test_v0_tool_attributes.py | 201 ++ .../tests/test_v0_trace_continuity.py | 246 ++ .../tests/test_v0_wrappers.py | 161 ++ .../LICENSE | 201 ++ .../pyproject.toml | 52 + .../instrumentation/terminus2/__init__.py | 802 ++++++ .../instrumentation/terminus2/package.py | 15 + .../instrumentation/terminus2/version.py | 15 + .../test-requirements.txt | 4 + .../tests/__init__.py | 0 packages.txt | 112 + 25 files changed, 5428 insertions(+) create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-openhands/README.rst create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-openhands/pyproject.toml create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/__init__.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/config.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/internal/__init__.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/internal/constants.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/internal/session_context.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/internal/utils.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/internal/v0_wrappers.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/package.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/version.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-openhands/test-requirements.txt create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-openhands/tests/__init__.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-openhands/tests/conftest.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-openhands/tests/test_v0_tool_attributes.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-openhands/tests/test_v0_trace_continuity.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-openhands/tests/test_v0_wrappers.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-terminus2/LICENSE create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-terminus2/pyproject.toml create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-terminus2/src/opentelemetry/instrumentation/terminus2/__init__.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-terminus2/src/opentelemetry/instrumentation/terminus2/package.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-terminus2/src/opentelemetry/instrumentation/terminus2/version.py create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-terminus2/test-requirements.txt create mode 100644 instrumentation-loongsuite/loongsuite-instrumentation-terminus2/tests/__init__.py create mode 100644 packages.txt diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-openhands/README.rst b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/README.rst new file mode 100644 index 000000000..8f4dc9a8b --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/README.rst @@ -0,0 +1,90 @@ +OpenTelemetry OpenHands Instrumentation +======================================== + +Automatic OpenTelemetry instrumentation for the legacy OpenHands V0 / +CodeAct runtime. + +What is covered +--------------- + +This package wraps the V0 ``python -m openhands.core.main`` execution path: + +* ``openhands.core.main.run_controller`` for the ENTRY span. +* ``openhands.core.loop.run_agent_until_done`` for the AGENT span fallback. +* ``AgentController.__init__`` / ``AgentController.close`` for lifecycle-bound + ENTRY and AGENT spans that survive ``python -m`` from-import binding. +* ``AgentController._step`` for ReAct STEP spans. +* ``Runtime.run_action`` for TOOL spans. +* ``LLM.__init__`` to bridge the current OpenHands context into LiteLLM calls. + +Span tree +--------- + +:: + + ENTRY enter openhands + `-- AGENT invoke_agent codeact + |-- STEP react step [xN] + | |-- LLM chat {model} + | `-- TOOL execute_tool {tool_name} + `-- STEP react step [...] + +``python -m`` and from-import binding +------------------------------------- + +When OpenHands V0 is launched via ``python -m openhands.core.main``, Python +executes ``main.py`` as ``__main__``. Symbols imported with ``from ... import`` +can be bound before module-level wrappers are installed, so patching +``openhands.core.main.run_controller`` is not enough by itself. + +To keep ENTRY and AGENT spans reliable, this instrumentation primarily opens +them from ``AgentController.__init__`` and closes them from +``AgentController.close``. The module-level wrappers remain as a fallback for +programmatic invocations. + +Cross-thread context bridge +--------------------------- + +OpenHands V0 may execute controller steps and runtime tool calls in worker +threads with fresh asyncio loops. The instrumentation stores the active OTel +context by session id and re-attaches it in STEP, TOOL, and LLM bridge wrappers +so the trace remains: + +``ENTRY -> AGENT -> STEP -> (LLM / TOOL)``. + +Semantic-convention I/O capture +------------------------------- + +ENTRY, AGENT, STEP, and TOOL spans emit ``input.value`` / ``output.value`` and +GenAI semantic attributes where applicable. + +* **ENTRY** emits ``gen_ai.input.messages`` and ``gen_ai.output.messages`` using + the ARMS parts-based message schema. +* **AGENT** emits ``gen_ai.input.messages``, ``gen_ai.output.messages``, + ``gen_ai.system_instructions`` / ``gen_ai.system_instruction``, and + ``gen_ai.tool.definitions``. +* **STEP** emits recent input history and the pending assistant/tool-call + output for the ReAct round. +* **TOOL** emits ``gen_ai.tool.name``, ``gen_ai.tool.type``, + ``gen_ai.tool.call.id``, ``gen_ai.tool.description``, + ``gen_ai.tool.call.arguments``, and ``gen_ai.tool.call.result``. + +Usage +----- + +.. code:: python + + from opentelemetry.instrumentation.openhands import OpenHandsInstrumentor + + OpenHandsInstrumentor().instrument() + +Configuration +------------- + +Environment variables: + +* ``OTEL_INSTRUMENTATION_OPENHANDS_ENABLED`` (default ``true``) +* ``OTEL_INSTRUMENTATION_OPENHANDS_OUTER_SPANS`` (default ``true``) +* ``OTEL_INSTRUMENTATION_OPENHANDS_AUTO_INSTRUMENT_LITELLM`` (default ``true``) + +I/O capture is always on and content is emitted in full. diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-openhands/pyproject.toml b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/pyproject.toml new file mode 100644 index 000000000..b9f0ae7f4 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/pyproject.toml @@ -0,0 +1,50 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "loongsuite-instrumentation-openhands" +dynamic = ["version"] +description = "LoongSuite OpenHands Instrumentation" +readme = "README.rst" +license = "Apache-2.0" +requires-python = ">=3.10" +authors = [ + { name = "OpenTelemetry Authors", email = "cncf-opentelemetry-contributors@lists.cncf.io" }, +] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Developers", + "License :: OSI Approved :: Apache Software License", + "Programming Language :: Python", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", +] +dependencies = [ + "wrapt >= 1.0.0, < 2.0.0", +] + +[project.optional-dependencies] +instruments = [] + +[project.entry-points.opentelemetry_instrumentor] +openhands = "opentelemetry.instrumentation.openhands:OpenHandsInstrumentor" + +[project.urls] +Homepage = "https://github.com/alibaba/loongsuite-python-agent/tree/main/instrumentation-loongsuite/loongsuite-instrumentation-openhands" +Repository = "https://github.com/alibaba/loongsuite-python-agent" + +[tool.hatch.version] +path = "src/opentelemetry/instrumentation/openhands/version.py" + +[tool.hatch.build.targets.sdist] +include = [ + "/src", + "/tests", +] + +[tool.hatch.build.targets.wheel] +packages = ["src/opentelemetry"] diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/__init__.py b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/__init__.py new file mode 100644 index 000000000..a02a7d3b3 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/__init__.py @@ -0,0 +1,265 @@ +"""OpenTelemetry OpenHands Instrumentation. + +Wraps the legacy V0 (CodeAct + AgentController + Runtime) path: + +* V0 — ``python -m openhands.core.main``. We add + ``ENTRY → AGENT → STEP → TOOL`` directly on top of the controller / runtime + call chain. LLM spans come from the bundled LiteLLM instrumentor. + +Usage +----- + +.. code:: python + + from opentelemetry.instrumentation.openhands import OpenHandsInstrumentor + + OpenHandsInstrumentor().instrument() +""" + +from __future__ import annotations + +import importlib +import logging +from typing import Any, Collection + +from opentelemetry import trace as trace_api +from opentelemetry.instrumentation.instrumentor import BaseInstrumentor +from wrapt import wrap_function_wrapper + +from opentelemetry.instrumentation.openhands.config import ( + OTEL_INSTRUMENTATION_OPENHANDS_AUTO_INSTRUMENT_LITELLM, + OTEL_INSTRUMENTATION_OPENHANDS_ENABLED, + OTEL_INSTRUMENTATION_OPENHANDS_OUTER_SPANS, +) +from opentelemetry.instrumentation.openhands.package import _instruments +from opentelemetry.instrumentation.openhands.version import __version__ + +logger = logging.getLogger(__name__) + +__all__ = ["OpenHandsInstrumentor"] + + +# --------------------------------------------------------------------------- +# Wrap-point registry — single source of truth shared with _uninstrument. +# Entries: (module, qualified_name) +# --------------------------------------------------------------------------- + +_PATCH_TARGETS: list[tuple[str, str]] = [ + ("openhands.core.main", "run_controller"), + ("openhands.core.loop", "run_agent_until_done"), + # AgentController.__init__ / .close are the *primary* ENTRY+AGENT + # span source for V0 — they're class methods, so they're patchable + # regardless of the from-import binding problem in main.py + # (see v0_wrappers.AgentControllerInitWrapper docstring). + ( + "openhands.controller.agent_controller", + "AgentController.__init__", + ), + ( + "openhands.controller.agent_controller", + "AgentController.close", + ), + ( + "openhands.controller.agent_controller", + "AgentController._step", + ), + ("openhands.runtime.base", "Runtime.run_action"), + # LLM context bridge — re-attaches the current sid-stashed context + # (STEP while a step is open) onto every ``LLM.completion`` invocation + # so the downstream LiteLLM / Aliyun GenAI auto-instrumentation emits + # the LLM span as a child of STEP and shares its ``trace_id``. + ("openhands.llm.llm", "LLM.__init__"), +] + + +def _module_importable(module: str) -> bool: + try: + importlib.import_module(module) + return True + except ModuleNotFoundError: + return False + except Exception: + # Other import errors should still let the wrap attempt surface a + # warning. + return True + + +def _safe_wrap(module: str, name: str, wrapper: Any) -> bool: + """Patch ``module.name`` with ``wrapper``; classify failures sensibly.""" + if not _module_importable(module): + # OpenHands versions can move modules around. Missing V0 modules + # should not prevent applications from starting. + logger.debug( + "OpenHands instrumentation: module %s not importable, skipping %s", + module, + name, + ) + return False + try: + wrap_function_wrapper(module=module, name=name, wrapper=wrapper) + logger.debug("OpenHands instrumentation: wrapped %s.%s", module, name) + return True + except (AttributeError, ImportError) as exc: + # Attribute missing inside the module — usually a version-skew issue. + logger.warning( + "OpenHands instrumentation: could not wrap %s.%s: %s", + module, + name, + exc, + ) + return False + except Exception as exc: # pragma: no cover - defensive + logger.warning( + "OpenHands instrumentation: unexpected error wrapping %s.%s: %s", + module, + name, + exc, + ) + return False + + +def _safe_unwrap(module: str, qualname: str) -> None: + """Unwrap a previously ``wrapt``-patched function or method.""" + try: + mod = importlib.import_module(module) + except Exception: + return + parts = qualname.split(".") + obj: Any = mod + parents: list[Any] = [mod] + try: + for p in parts: + obj = getattr(obj, p) + parents.append(obj) + except Exception: + return + if not hasattr(obj, "__wrapped__"): + return + parent = parents[-2] + try: + setattr(parent, parts[-1], obj.__wrapped__) + except Exception: + pass + + +class OpenHandsInstrumentor(BaseInstrumentor): + """Instrumentation entry point for OpenHands V0.""" + + def instrumentation_dependencies(self) -> Collection[str]: + return _instruments + + def _instrument(self, **kwargs: Any) -> None: + if not OTEL_INSTRUMENTATION_OPENHANDS_ENABLED: + logger.info("OpenHands instrumentation disabled via env var") + return + + tracer_provider = kwargs.get("tracer_provider") + tracer = trace_api.get_tracer( + __name__, __version__, tracer_provider=tracer_provider + ) + + from opentelemetry.instrumentation.openhands.internal.v0_wrappers import ( + AgentControllerCloseWrapper, + AgentControllerInitWrapper, + AgentControllerStepWrapper, + LLMInitWrapper, + RunAgentUntilDoneWrapper, + RunControllerWrapper, + RuntimeRunActionWrapper, + ) + + if OTEL_INSTRUMENTATION_OPENHANDS_OUTER_SPANS: + self._install_v0_patches(tracer, { + "run_controller": RunControllerWrapper, + "run_agent_until_done": RunAgentUntilDoneWrapper, + "agent_init": AgentControllerInitWrapper, + "agent_close": AgentControllerCloseWrapper, + "agent_step": AgentControllerStepWrapper, + "runtime_run_action": RuntimeRunActionWrapper, + "llm_init": LLMInitWrapper, + }) + + # Auto-enable bundled LiteLLM instrumentation so SDK / V0 LLM + # ``litellm.completion()`` calls become LLM spans. + if OTEL_INSTRUMENTATION_OPENHANDS_AUTO_INSTRUMENT_LITELLM: + self._maybe_enable_litellm(**kwargs) + + def _install_v0_patches(self, tracer, factories) -> None: + RunControllerWrapper = factories["run_controller"] + RunAgentUntilDoneWrapper = factories["run_agent_until_done"] + AgentControllerInitWrapper = factories["agent_init"] + AgentControllerCloseWrapper = factories["agent_close"] + AgentControllerStepWrapper = factories["agent_step"] + RuntimeRunActionWrapper = factories["runtime_run_action"] + LLMInitWrapper = factories["llm_init"] + + # `run_controller` and `run_agent_until_done` patches are best-effort: + # they only fire when run_controller is called via the proper module + # path (programmatic / test). When OpenHands is launched via + # ``python -m openhands.core.main``, the from-import binding in + # main.py bypasses these patches — the AgentController.__init__ / + # .close patches below take over and produce ENTRY+AGENT spans + # reliably (class methods are immune to from-import binding). + _safe_wrap( + "openhands.core.main", + "run_controller", + RunControllerWrapper(tracer), + ) + _safe_wrap( + "openhands.core.loop", + "run_agent_until_done", + RunAgentUntilDoneWrapper(tracer), + ) + _safe_wrap( + "openhands.controller.agent_controller", + "AgentController.__init__", + AgentControllerInitWrapper(tracer), + ) + _safe_wrap( + "openhands.controller.agent_controller", + "AgentController.close", + AgentControllerCloseWrapper(tracer), + ) + _safe_wrap( + "openhands.controller.agent_controller", + "AgentController._step", + AgentControllerStepWrapper(tracer), + ) + _safe_wrap( + "openhands.runtime.base", + "Runtime.run_action", + RuntimeRunActionWrapper(tracer), + ) + # LLM context bridge — patches ``LLM.__init__`` so every instance's + # ``self._completion`` re-attaches the latest sid-stashed context. + # See ``LLMInitWrapper`` for why we need this even though the LLM + # call is synchronous: in real OpenHands deployments LiteLLM ends + # up creating its span in a thread / context that ``contextvars`` + # didn't propagate STEP into, so we re-attach explicitly. + _safe_wrap( + "openhands.llm.llm", + "LLM.__init__", + LLMInitWrapper(tracer), + ) + + def _maybe_enable_litellm(self, **kwargs: Any) -> None: + try: + from opentelemetry.instrumentation.litellm import ( + LiteLLMInstrumentor, + ) + except Exception as exc: + logger.debug( + "LiteLLM instrumentation not available, skipping: %s", exc + ) + return + try: + instr = LiteLLMInstrumentor() + already = getattr(instr, "_is_instrumented_by_opentelemetry", False) + if not already: + instr.instrument(**kwargs) + except Exception as exc: + logger.debug("Could not auto-enable LiteLLM instrumentation: %s", exc) + + def _uninstrument(self, **kwargs: Any) -> None: + for module, qualname in _PATCH_TARGETS: + _safe_unwrap(module, qualname) diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/config.py b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/config.py new file mode 100644 index 000000000..4f5ad38db --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/config.py @@ -0,0 +1,25 @@ +"""Environment-variable driven configuration for the OpenHands instrumentation.""" + +from __future__ import annotations + +import os + + +def _bool_env(name: str, default: bool) -> bool: + val = os.getenv(name) + if val is None: + return default + return val.strip().lower() in {"true", "1", "yes", "on"} + + +OTEL_INSTRUMENTATION_OPENHANDS_ENABLED = _bool_env( + "OTEL_INSTRUMENTATION_OPENHANDS_ENABLED", True +) + +OTEL_INSTRUMENTATION_OPENHANDS_OUTER_SPANS = _bool_env( + "OTEL_INSTRUMENTATION_OPENHANDS_OUTER_SPANS", True +) + +OTEL_INSTRUMENTATION_OPENHANDS_AUTO_INSTRUMENT_LITELLM = _bool_env( + "OTEL_INSTRUMENTATION_OPENHANDS_AUTO_INSTRUMENT_LITELLM", True +) diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/internal/__init__.py b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/internal/__init__.py new file mode 100644 index 000000000..7b2c8b6a1 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/internal/__init__.py @@ -0,0 +1 @@ +"""Internal helpers for OpenHands instrumentation.""" diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/internal/constants.py b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/internal/constants.py new file mode 100644 index 000000000..6d99a6820 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/internal/constants.py @@ -0,0 +1,12 @@ +"""Constant attribute keys & framework identity used across wrappers.""" + +from __future__ import annotations + +GEN_AI_FRAMEWORK = "gen_ai.framework" +GEN_AI_SPAN_KIND = "gen_ai.span.kind" + +FRAMEWORK_NAME = "openhands" + +# OpenHands-specific span attributes (namespaced to avoid clashing with the +# generic GenAI semconv attributes already provided by upstream). +OH_INITIAL_MESSAGE_PREVIEW = "openhands.initial_message.preview" diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/internal/session_context.py b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/internal/session_context.py new file mode 100644 index 000000000..534d3e611 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/internal/session_context.py @@ -0,0 +1,196 @@ +"""Cross-thread / cross-loop OTel context bridge keyed by OpenHands session id. + +Why this exists +--------------- + +OpenHands V0's ``EventStream`` delivers events to subscribers via a +``ThreadPoolExecutor``. The ``AgentController.on_event`` callback then runs + +.. code:: python + + asyncio.get_event_loop().run_until_complete(self._on_event(event)) + +inside a *worker thread*, which spins up a brand-new asyncio loop with a +fresh ``contextvars.Context``. This means none of the OTel context (tracer +spans / baggage) attached on the main coroutine in ``run_controller`` is +visible inside ``AgentController._step`` or ``Runtime.run_action`` — every +STEP / TOOL span starts at the **trace root**, fragmenting the trace into +many disconnected pieces. + +This module bridges that gap. We snapshot the OTel context at entry-time +(``run_controller`` / ``run_agent_until_done``) under the controller's +session id, and the STEP / TOOL wrappers re-attach the snapshot before +opening their spans so every span shares a single ``trace_id`` rooted at +the ENTRY span. + +The store is keyed by **session id (sid)** so concurrent benchmark +sessions stay isolated. +""" + +from __future__ import annotations + +import threading +from typing import Optional + +from opentelemetry import context as otel_context + +_lock = threading.Lock() +# Map session id -> OTel Context object. The Context contains the active +# Span (and any baggage / suppression flags). Re-attaching it makes the +# stored span the *current* span for whatever thread/loop attaches it. +_session_contexts: dict[str, otel_context.Context] = {} + +# Map session id -> { tool_name: tool_definition_dict }. Captured at +# AGENT span open from ``controller.agent.tools`` and consumed by the +# TOOL wrapper to populate ``gen_ai.tool.description`` and friends — the +# Runtime instance does not have direct access to the agent's tool list. +_session_tool_registry: dict[str, dict[str, dict]] = {} + +# Tracks the most-recent sid we stored a context for. Used as a fallback +# when a hook point (typically ``Runtime.run_action``) cannot locate the +# session id from its arguments — in single-session CLI runs this is +# always the right answer. +_last_sid: Optional[str] = None + + +def store_context(sid: Optional[str], ctx: otel_context.Context) -> None: + """Stash ``ctx`` under ``sid``. Updates ``_last_sid``.""" + if not sid: + return + global _last_sid + with _lock: + _session_contexts[sid] = ctx + _last_sid = sid + + +def get_context(sid: Optional[str]) -> Optional[otel_context.Context]: + """Return the stashed context for ``sid``, falling back to the last sid.""" + with _lock: + if sid and sid in _session_contexts: + return _session_contexts[sid] + if _last_sid and _last_sid in _session_contexts: + return _session_contexts[_last_sid] + return None + + +def clear_context(sid: Optional[str]) -> None: + if not sid: + return + global _last_sid + with _lock: + _session_contexts.pop(sid, None) + _session_tool_registry.pop(sid, None) + if _last_sid == sid: + _last_sid = None + + +def clear_all() -> None: + """Drop everything (only used by tests).""" + global _last_sid + with _lock: + _session_contexts.clear() + _session_tool_registry.clear() + _last_sid = None + + +# --------------------------------------------------------------------------- +# Tool registry (per-sid) +# --------------------------------------------------------------------------- + + +def store_tool_registry(sid: Optional[str], tools: object) -> None: + """Index ``tools`` by name and stash under ``sid``. + + ``tools`` is whatever ``controller.agent.tools`` exposes — typically a + list of LiteLLM ``ChatCompletionToolParam`` dicts of the form + ``{"type": "function", "function": {"name": ..., "description": ..., ...}}``. + Anything that doesn't fit that shape is best-effort skipped. + """ + if not sid or not tools: + return + registry: dict[str, dict] = {} + try: + for t in tools: # type: ignore[union-attr] + try: + if isinstance(t, dict): + fn = t.get("function") or {} + name = fn.get("name") if isinstance(fn, dict) else None + else: + fn = getattr(t, "function", None) + name = getattr(fn, "name", None) if fn is not None else None + # Normalize to a dict so the consumer doesn't need type-knowledge. + if name and not isinstance(t, dict): + t = { + "type": getattr(t, "type", "function"), + "function": { + "name": name, + "description": getattr(fn, "description", "") or "", + "parameters": getattr(fn, "parameters", None) or {}, + }, + } + if name: + registry[str(name)] = t + except Exception: + continue + except TypeError: + return + if not registry: + return + with _lock: + _session_tool_registry[sid] = registry + + +def get_tool_definition(sid: Optional[str], name: Optional[str]) -> Optional[dict]: + """Look up a single tool's definition (dict) by name, sid-scoped.""" + if not name: + return None + with _lock: + if sid and sid in _session_tool_registry: + return _session_tool_registry[sid].get(name) + # Fallback to the most-recent session — single-CLI-run case. + if _last_sid and _last_sid in _session_tool_registry: + return _session_tool_registry[_last_sid].get(name) + return None + + +def get_tool_registry(sid: Optional[str]) -> Optional[dict[str, dict]]: + """Return the full ``{name: definition}`` registry for ``sid``.""" + with _lock: + if sid and sid in _session_tool_registry: + return dict(_session_tool_registry[sid]) + if _last_sid and _last_sid in _session_tool_registry: + return dict(_session_tool_registry[_last_sid]) + return None + + +class AttachedSession: + """Context manager that attaches the stashed context for ``sid``. + + Usage:: + + with AttachedSession(sid): + span = tracer.start_span(...) + # span is parented under whatever the stashed context contains + + No-op when no stash exists for the given sid. + """ + + __slots__ = ("_sid", "_token") + + def __init__(self, sid: Optional[str]): + self._sid = sid + self._token = None + + def __enter__(self) -> "AttachedSession": + ctx = get_context(self._sid) + if ctx is not None: + self._token = otel_context.attach(ctx) + return self + + def __exit__(self, exc_type, exc_val, exc_tb) -> None: + if self._token is not None: + try: + otel_context.detach(self._token) + except Exception: + pass + self._token = None diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/internal/utils.py b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/internal/utils.py new file mode 100644 index 000000000..7354bb8b2 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/internal/utils.py @@ -0,0 +1,190 @@ +"""Small attribute / argument extraction helpers shared by the wrappers.""" + +from __future__ import annotations + +import json +from typing import Any + + +def safe_str(value: Any) -> str: + """Best-effort string conversion that never raises.""" + if value is None: + return "" + try: + return str(value) + except Exception: + return "" + + +def preview(text: Any, max_len: int | None = None) -> str: + """Return a string preview of *text* (kept for API compatibility). + + Truncation is no longer applied — captured content is emitted in + full so dashboards never lose information. ``max_len`` is accepted + but ignored. + """ + return safe_str(text) + + +def maybe_preview(text: Any) -> str: + """Alias for :func:`preview` — kept for API compatibility.""" + return preview(text) + + +def safe_get_attr(obj: Any, *names: str, default: Any = None) -> Any: + """Return the first non-None attribute among *names* on *obj*.""" + for name in names: + if obj is None: + return default + try: + v = getattr(obj, name, None) + except Exception: + v = None + if v is not None: + return v + return default + + +def serialize_message(message: Any) -> str: + """Best-effort serialize an OpenHands message-like object to text.""" + if message is None: + return "" + if isinstance(message, str): + return message + text_parts: list[str] = [] + for attr in ("text", "content", "value"): + v = safe_get_attr(message, attr) + if isinstance(v, str) and v: + return v + if isinstance(v, list): + for item in v: + t = safe_get_attr(item, "text", "content") + if isinstance(t, str) and t: + text_parts.append(t) + if text_parts: + return "\n".join(text_parts) + return safe_str(message) + + +def extract_uuid_str(value: Any) -> str: + """Convert a UUID-like value to its hex/string form, returning ''.""" + if value is None: + return "" + hex_attr = getattr(value, "hex", None) + if isinstance(hex_attr, str) and hex_attr: + return hex_attr + return safe_str(value) + + +# --------------------------------------------------------------------------- +# Semconv I/O serialization (input.value / output.value) +# --------------------------------------------------------------------------- + + +def _to_jsonable(obj: Any, depth: int = 0, max_depth: int = 3) -> Any: + """Best-effort convert ``obj`` into something json.dumps can serialize.""" + if obj is None or isinstance(obj, (bool, int, float, str)): + return obj + if depth >= max_depth: + return safe_str(obj) + if isinstance(obj, dict): + out: dict[str, Any] = {} + for k, v in obj.items(): + try: + out[safe_str(k)] = _to_jsonable(v, depth + 1, max_depth) + except Exception: + out[safe_str(k)] = safe_str(v) + return out + if isinstance(obj, (list, tuple, set)): + return [_to_jsonable(v, depth + 1, max_depth) for v in obj] + # Pydantic v2 + if hasattr(obj, "model_dump"): + try: + return _to_jsonable(obj.model_dump(), depth + 1, max_depth) + except Exception: + pass + # Dataclass / generic object + if hasattr(obj, "__dict__"): + try: + d = { + k: v + for k, v in vars(obj).items() + if not k.startswith("_") + and not callable(v) + } + if d: + return _to_jsonable(d, depth + 1, max_depth) + except Exception: + pass + return safe_str(obj) + + +def to_json_str(obj: Any, max_len: int | None = None) -> str: + """Convert ``obj`` to a JSON string. Empty string on failure. + + No truncation is applied — captured content is emitted in full. + ``max_len`` is accepted but ignored (kept for API compatibility). + """ + try: + jsonable = _to_jsonable(obj) + s = json.dumps(jsonable, ensure_ascii=False, default=safe_str) + except Exception: + s = safe_str(obj) + return s or "" + + +def maybe_to_json_str(obj: Any, max_len: int | None = None) -> str: + """Alias for :func:`to_json_str` — kept for API compatibility.""" + return to_json_str(obj, max_len) + + +def messages_to_genai_input(messages: Any) -> str: + """Serialize a chat-style ``messages`` list for ``gen_ai.input.messages``. + + Each item is normalized into ``{"role": ..., "content": ...}``. Keeps + ``tool_calls`` when present. + """ + if not isinstance(messages, list): + return "" + norm: list[dict[str, Any]] = [] + for m in messages: + role = safe_get_attr(m, "role") + content = safe_get_attr(m, "content") + if role is None and content is None and isinstance(m, dict): + role = m.get("role") + content = m.get("content") + if isinstance(content, list): + content = "".join( + safe_str(safe_get_attr(c, "text") or safe_get_attr(c, "content") or c) + for c in content + ) + item: dict[str, Any] = {"role": safe_str(role) or "user", "content": safe_str(content)} + tool_calls = safe_get_attr(m, "tool_calls") + if tool_calls: + item["tool_calls"] = _to_jsonable(tool_calls) + norm.append(item) + return to_json_str(norm) + + +def action_to_genai_output(action: Any) -> str: + """Serialize an OpenHands V0 ``Action`` into a GenAI-style assistant message.""" + if action is None: + return "" + action_type = safe_str(safe_get_attr(action, "action") or "") + thought = safe_str(safe_get_attr(action, "thought") or "") + item: dict[str, Any] = {"role": "assistant"} + if thought: + item["content"] = thought + args: dict[str, Any] = {} + for key in ("command", "code", "path", "url", "content", "task_list", "name", "arguments"): + v = safe_get_attr(action, key) + if v not in (None, "", []): + args[key] = _to_jsonable(v) + if action_type or args: + item["tool_calls"] = [ + { + "type": "function", + "function": {"name": action_type or "agent.action", "arguments": args}, + } + ] + return to_json_str([item]) diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/internal/v0_wrappers.py b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/internal/v0_wrappers.py new file mode 100644 index 000000000..614672ed5 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/internal/v0_wrappers.py @@ -0,0 +1,2535 @@ +"""Wrappers for the OpenHands **V0** (Legacy CodeAct) architecture. + +Trace tree +---------- + +:: + + ENTRY enter openhands (openhands.core.main.run_controller) + `-- AGENT invoke_agent codeact (openhands.core.loop.run_agent_until_done) + |-- STEP react step [×N] (openhands.controller.agent_controller.AgentController._step) + | `-- LLM chat {model} (litellm — covered by litellm instrumentor) + `-- TOOL execute_tool {tool_name} (openhands.runtime.base.Runtime.run_action) + +Context propagation across threads +---------------------------------- + +OpenHands V0's ``EventStream`` delivers events via ``ThreadPoolExecutor``, +and ``AgentController.on_event`` then runs the actual handler with a +*brand-new* asyncio loop in a worker thread: + +.. code:: python + + asyncio.get_event_loop().run_until_complete(self._on_event(event)) + +Python ``contextvars`` do NOT propagate from the main coroutine into these +worker threads, so ``AgentController._step`` and ``Runtime.run_action`` +would otherwise start *root* spans with fresh ``trace_id``s, fragmenting +the trace into many disconnected pieces. + +To fix that, we use :mod:`session_context` as a process-wide bridge: the +ENTRY wrapper stashes the OTel context (carrying the ENTRY+AGENT span +chain) keyed by session id, and STEP / TOOL wrappers re-attach it before +opening their span. The result is one trace per session id with the +correct parent-child links. + +I/O capture +----------- + +ENTRY / AGENT / STEP / TOOL spans all set: + +* ``input.value`` and ``output.value`` (OpenInference convention) +* ``input.mime_type`` / ``output.mime_type`` +* ``gen_ai.input.messages`` / ``gen_ai.output.messages`` where the GenAI + semconv applies (LLM-style messages + assistant tool calls) + +Capture is always on and content is emitted untruncated. +""" + +from __future__ import annotations + +import logging +from typing import Any + +from opentelemetry import context as otel_context +from opentelemetry import trace as trace_api +from opentelemetry.semconv._incubating.attributes import ( + gen_ai_attributes as GenAI, +) +from opentelemetry.trace import ( + SpanKind, + Status, + StatusCode, + Tracer, + set_span_in_context, +) + +from opentelemetry.instrumentation.openhands.config import ( + OTEL_INSTRUMENTATION_OPENHANDS_OUTER_SPANS, +) +from opentelemetry.instrumentation.openhands.internal.constants import ( + FRAMEWORK_NAME, + GEN_AI_FRAMEWORK, + GEN_AI_SPAN_KIND, + OH_INITIAL_MESSAGE_PREVIEW, +) +from opentelemetry.instrumentation.openhands.internal.session_context import ( + AttachedSession, + clear_context, + get_context, + get_tool_definition, + store_context, + store_tool_registry, +) +from opentelemetry.instrumentation.openhands.internal.utils import ( + action_to_genai_output, + maybe_preview, + maybe_to_json_str, + messages_to_genai_input, + safe_get_attr, + safe_str, + serialize_message, + to_json_str, +) + +logger = logging.getLogger(__name__) + + +# Constants ----------------------------------------------------------------- + +OH_AGENT_NAME = "openhands.agent.name" +OH_REACT_ROUND = "gen_ai.react.round" +OH_AGENT_STATE = "openhands.agent.state" +OH_RUNTIME_NAME = "openhands.runtime.name" +OH_ACTION_TYPE = "openhands.action.type" +OH_OBSERVATION_TYPE = "openhands.observation.type" +OH_HISTORY_LENGTH = "openhands.history.length" + +# OpenInference / GenAI common I/O attribute keys +INPUT_VALUE = "input.value" +INPUT_MIME = "input.mime_type" +OUTPUT_VALUE = "output.value" +OUTPUT_MIME = "output.mime_type" +GEN_AI_INPUT_MESSAGES = "gen_ai.input.messages" +GEN_AI_OUTPUT_MESSAGES = "gen_ai.output.messages" +GEN_AI_SYSTEM = "gen_ai.system" +GEN_AI_AGENT_ID = "gen_ai.agent.id" +GEN_AI_CONVERSATION_ID = "gen_ai.conversation.id" +GEN_AI_SESSION_ID = "gen_ai.session.id" +GEN_AI_REQUEST_MODEL = "gen_ai.request.model" +GEN_AI_SYSTEM_INSTRUCTIONS = "gen_ai.system_instructions" +GEN_AI_SYSTEM_INSTRUCTION = "gen_ai.system_instruction" + +# Tool span attributes per ARMS GenAI semconv (gen-ai.md §Tool). +GEN_AI_TOOL_CALL_ID = "gen_ai.tool.call.id" +GEN_AI_TOOL_CALL_ARGUMENTS = "gen_ai.tool.call.arguments" +GEN_AI_TOOL_CALL_RESULT = "gen_ai.tool.call.result" +GEN_AI_TOOL_DESCRIPTION = "gen_ai.tool.description" +GEN_AI_TOOL_DEFINITIONS = "gen_ai.tool.definitions" + +# Stash slots on AgentController instances (set by AgentControllerInitWrapper). +_OWNS_FLAG = "_otel_oh_owns_lifecycle" +_ENTRY_SPAN_ATTR = "_otel_oh_entry_span" +_AGENT_SPAN_ATTR = "_otel_oh_agent_span" +_ENTRY_TOKEN_ATTR = "_otel_oh_entry_token" +_AGENT_TOKEN_ATTR = "_otel_oh_agent_token" +# STEP persistence — keeps the *most-recent* STEP span alive across the +# return of ``_step`` so that ``Runtime.run_action`` (which fires *later* +# in a thread-pool executor via ``call_sync_from_async``) can re-attach +# the STEP context and become its child rather than a sibling. +# +# IMPORTANT: we deliberately do **not** stash an OTel attach-token across +# the return of ``_step``. ``otel_context.attach()`` returns a Token that +# is bound to the ``contextvars.Context`` it was created in; calling +# ``detach(token)`` from a *different* context raises ``ValueError`` (and +# in production the Aliyun OTel SDK floods the log with +# "Token was created in a different Context" errors). Attach/detach +# always happen as a balanced pair *inside the same async task*; cross- +# task / cross-thread propagation goes through the ``Context`` *object* +# stashed in :mod:`session_context` and re-attached on the consumer side. +_STEP_SPAN_ATTR = "_otel_oh_step_span" +_AGENT_CTX_ATTR = "_otel_oh_agent_ctx" # restore target when STEP closes + + +def _set_common(span: trace_api.Span, kind: str) -> None: + span.set_attribute(GEN_AI_SPAN_KIND, kind) + span.set_attribute(GEN_AI_FRAMEWORK, FRAMEWORK_NAME) + span.set_attribute(GEN_AI_SYSTEM, FRAMEWORK_NAME) + + +def _set_io( + span: trace_api.Span, + *, + input_value: str = "", + output_value: str = "", + input_messages: str = "", + output_messages: str = "", + mime: str = "application/json", +) -> None: + if input_value: + span.set_attribute(INPUT_VALUE, input_value) + span.set_attribute(INPUT_MIME, mime) + if output_value: + span.set_attribute(OUTPUT_VALUE, output_value) + span.set_attribute(OUTPUT_MIME, mime) + if input_messages: + span.set_attribute(GEN_AI_INPUT_MESSAGES, input_messages) + if output_messages: + span.set_attribute(GEN_AI_OUTPUT_MESSAGES, output_messages) + + +def _extract_model_from_config(config: Any) -> str: + if config is None: + return "" + try: + llms = safe_get_attr(config, "llms") + if isinstance(llms, dict) and llms: + llm = next(iter(llms.values())) + model = safe_get_attr(llm, "model") + if model: + return safe_str(model) + except Exception: + pass + try: + llm = safe_get_attr(config, "llm") + model = safe_get_attr(llm, "model") + if model: + return safe_str(model) + except Exception: + pass + return "" + + +def _extract_input_message_text(initial_user_action: Any) -> str: + """Pull human-readable text out of an ``initial_user_action`` argument.""" + return serialize_message(initial_user_action) + + +def _state_to_input_messages(state: Any, max_messages: int = 10) -> str: + """Best-effort extract a chat-style messages list from a controller State. + + The actual messages sent to the LLM are built inside ``CodeActAgent.step`` + and not stored on the controller, so this is a coarse summary derived + from ``state.history`` which is reliably available. + """ + history = safe_get_attr(state, "history") or [] + if not isinstance(history, list): + return "" + items: list[dict[str, str]] = [] + # Keep the most recent ``max_messages`` events for size budget. + for ev in history[-max_messages:]: + cls_name = type(ev).__name__ + # Map common event types to roles + if cls_name in ("MessageAction", "SystemMessageAction"): + role = "user" if str(safe_get_attr(ev, "source")) == "user" else "assistant" + content = safe_get_attr(ev, "content") or safe_get_attr(ev, "message") or "" + elif cls_name.endswith("Action"): + role = "assistant" + content = ( + safe_get_attr(ev, "thought") + or safe_get_attr(ev, "command") + or safe_get_attr(ev, "code") + or safe_str(ev) + ) + elif cls_name.endswith("Observation"): + role = "tool" + content = safe_get_attr(ev, "content") or safe_str(ev) + else: + role = "system" + content = safe_str(ev) + items.append({"role": role, "content": safe_str(content), "event": cls_name}) + return to_json_str(items) + + +def _final_state_to_output(state: Any) -> str: + """Serialize the controller's final state for output.value.""" + if state is None: + return "" + payload: dict[str, Any] = {} + agent_state = safe_get_attr(state, "agent_state") + if agent_state is not None: + payload["agent_state"] = ( + safe_get_attr(agent_state, "value") or safe_str(agent_state) + ) + last_error = safe_get_attr(state, "last_error") + if last_error: + payload["last_error"] = safe_str(last_error) + iteration = safe_get_attr(state, "iteration") + if iteration is not None: + payload["iteration"] = safe_str(iteration) + history = safe_get_attr(state, "history") or [] + if isinstance(history, list) and history: + payload["history_length"] = len(history) + # Find the last AgentFinishAction or last assistant content for a final answer summary. + for ev in reversed(history): + if type(ev).__name__ == "AgentFinishAction": + payload["final_thought"] = safe_str( + safe_get_attr(ev, "final_thought") + or safe_get_attr(ev, "thought") + or "" + ) + payload["outputs"] = safe_str(safe_get_attr(ev, "outputs") or {}) + break + return to_json_str(payload) + + +def _entry_input_messages_from_initial(initial_user_action: Any) -> str: + """Return ARMS gen_ai.input.messages for the ENTRY span.""" + text = _extract_input_message_text(initial_user_action) + if not text: + return "" + return to_json_str( + [{"role": "user", "parts": [{"type": "text", "content": text}]}] + ) + + +def _entry_io_from_state(state: Any) -> tuple[str, str]: + """Return (input_messages, output_messages) for ENTRY from final state.""" + history = safe_get_attr(state, "history") or [] + input_messages = "" + output_messages = "" + if isinstance(history, list) and history: + input_payload = _history_to_input_messages_schema(history) + if input_payload: + input_messages = to_json_str(input_payload) + output_payload = _history_to_output_messages_schema(history) + if output_payload: + output_messages = to_json_str(output_payload) + if not output_messages: + final_state = _final_state_to_output(state) + if final_state: + output_messages = to_json_str( + [ + { + "role": "assistant", + "parts": [{"type": "text", "content": final_state}], + "finish_reason": "stop", + } + ] + ) + return input_messages, output_messages + + +# --------------------------------------------------------------------------- +# ARMS GenAI semconv message-schema converters. +# +# Per gen-ai.md §LLM/§Agent, gen_ai.input.messages / gen_ai.output.messages +# / gen_ai.system_instructions follow a "parts"-based structure: +# +# [{"role": "user|assistant|tool|system", +# "parts": [{"type": "text|tool_call|tool_call_response|...", +# "content": "...", "name": "...", "id": "...", +# "arguments": {...}, "result": "..."}], +# "finish_reason": "stop|...", # output only +# }] +# +# The system instructions schema is a flat list of parts: +# +# [{"type": "text", "content": "..."}] +# --------------------------------------------------------------------------- + + +def _action_event_to_parts(ev: Any) -> list[dict[str, Any]]: + """Convert an Action event into a list of ``parts`` for AGENT messages. + + Captures both the model's "thought" text and any ``tool_call`` part + derived from ``tool_call_metadata``. + """ + parts: list[dict[str, Any]] = [] + thought = safe_get_attr(ev, "thought") + if thought: + parts.append({"type": "text", "content": safe_str(thought)}) + tcm = safe_get_attr(ev, "tool_call_metadata") + if tcm is not None: + fn_name = safe_str(safe_get_attr(tcm, "function_name") or "") + tcid = safe_str(safe_get_attr(tcm, "tool_call_id") or "") + # Best-effort harvest the original LLM-emitted JSON arguments. + args: Any = {} + try: + mr = safe_get_attr(tcm, "model_response") + choices = ( + getattr(mr, "choices", None) + if mr is not None + else None + ) or [] + for choice in choices: + msg = getattr(choice, "message", None) or ( + choice.get("message") if isinstance(choice, dict) else None + ) + tool_calls = ( + getattr(msg, "tool_calls", None) + if msg is not None + else None + ) or (msg.get("tool_calls") if isinstance(msg, dict) else None) + if not tool_calls: + continue + for tc in tool_calls: + tc_id = ( + getattr(tc, "id", None) + if not isinstance(tc, dict) + else tc.get("id") + ) + if tcid and safe_str(tc_id) != tcid: + continue + fn = ( + getattr(tc, "function", None) + if not isinstance(tc, dict) + else tc.get("function") + ) + raw = ( + getattr(fn, "arguments", None) + if not isinstance(fn, dict) + else fn.get("arguments") + ) + if isinstance(raw, str): + try: + import json as _json + + args = _json.loads(raw) + except Exception: + args = {"raw": raw} + elif isinstance(raw, dict): + args = raw + except Exception: + args = {} + if not args: + for key in ( + "command", + "code", + "path", + "url", + "content", + "task_list", + "old_str", + "new_str", + "file_text", + ): + v = safe_get_attr(ev, key) + if v not in (None, "", [], {}): + args[key] = v + if fn_name or tcid or args: + parts.append( + { + "type": "tool_call", + "id": tcid, + "name": fn_name or safe_str(safe_get_attr(ev, "action") or ""), + "arguments": args, + } + ) + if not parts: + # Minimal fallback when nothing else could be extracted. + action_type = safe_str(safe_get_attr(ev, "action") or "") + if action_type: + parts.append({"type": "tool_call", "name": action_type, "arguments": {}}) + return parts + + +def _observation_event_to_parts(ev: Any) -> list[dict[str, Any]]: + """Convert an Observation event into ``parts`` for tool-response messages.""" + tcm = safe_get_attr(ev, "tool_call_metadata") + tcid = safe_str(safe_get_attr(tcm, "tool_call_id") or "") if tcm else "" + result_payload: dict[str, Any] = {} + for key in ("content", "exit_code", "error", "stdout", "stderr", "url"): + v = safe_get_attr(ev, key) + if v not in (None, "", [], {}): + result_payload[key] = v + return [ + { + "type": "tool_call_response", + "id": tcid, + "result": result_payload or safe_str(ev), + } + ] + + +def _history_to_input_messages_schema(history: list, max_events: int = 200) -> list[dict[str, Any]]: + """Convert ``state.history`` into the ARMS gen_ai.input.messages schema. + + Folds adjacent same-role events into a single message with multiple + ``parts``, mirroring how the messages were assembled when sent to + the LLM. + """ + if not history: + return [] + items = history[-max_events:] + messages: list[dict[str, Any]] = [] + for ev in items: + cls = type(ev).__name__ + # Determine role + parts for this event. + if cls == "SystemMessageAction": + # System is reported separately under gen_ai.system_instructions. + continue + if cls == "MessageAction": + src = str(safe_get_attr(ev, "source") or "").lower() + role = "user" if src == "user" else "assistant" + content = safe_str(safe_get_attr(ev, "content") or "") + parts = [{"type": "text", "content": content}] + elif cls.endswith("Observation"): + role = "tool" + parts = _observation_event_to_parts(ev) + elif cls.endswith("Action"): + role = "assistant" + parts = _action_event_to_parts(ev) + else: + role = "system" + parts = [{"type": "text", "content": safe_str(ev)}] + # Fold consecutive same-role messages. + if messages and messages[-1]["role"] == role: + messages[-1]["parts"].extend(parts) + else: + messages.append({"role": role, "parts": parts}) + return messages + + +def _history_to_output_messages_schema(history: list) -> list[dict[str, Any]]: + """Pull the *final* assistant turn from history per ARMS gen_ai.output.messages. + + Walks back from the end of history and collects assistant-side events + (Actions) up to the previous user/tool boundary. Includes a + ``finish_reason`` derived from the last AgentFinishAction / state. + """ + if not history: + return [] + finish_reason = "stop" + tail_actions: list[Any] = [] + for ev in reversed(history): + cls = type(ev).__name__ + if cls == "AgentFinishAction": + finish_reason = safe_str( + safe_get_attr(ev, "final_thought") and "stop" or "stop" + ) + tail_actions.insert(0, ev) + continue + if cls.endswith("Observation") or cls == "MessageAction": + # Stop once we cross back into user-input or tool-result territory. + if cls == "MessageAction" and str( + safe_get_attr(ev, "source") or "" + ).lower() == "user": + break + if cls.endswith("Observation"): + break + if cls.endswith("Action") or ( + cls == "MessageAction" + and str(safe_get_attr(ev, "source") or "").lower() != "user" + ): + tail_actions.insert(0, ev) + if not tail_actions: + # Fallback: at least include the very last event as the assistant turn. + tail_actions = [history[-1]] + parts: list[dict[str, Any]] = [] + for ev in tail_actions: + cls = type(ev).__name__ + if cls == "MessageAction": + content = safe_str(safe_get_attr(ev, "content") or "") + if content: + parts.append({"type": "text", "content": content}) + elif cls == "AgentFinishAction": + ft = safe_str(safe_get_attr(ev, "final_thought") or "") + if ft: + parts.append({"type": "text", "content": ft}) + outputs = safe_get_attr(ev, "outputs") + if outputs: + parts.append({"type": "text", "content": safe_str(outputs)}) + else: + parts.extend(_action_event_to_parts(ev)) + if not parts: + parts = [{"type": "text", "content": ""}] + return [{"role": "assistant", "parts": parts, "finish_reason": finish_reason}] + + +def _agent_to_system_instructions(agent: Any, state: Any) -> list[dict[str, Any]]: + """Return ARMS gen_ai.system_instructions for the controller's agent. + + Tries the explicit ``agent.get_system_message()`` API first (most + accurate), then falls back to scanning ``state.history`` for a + ``SystemMessageAction``. + """ + content = "" + try: + gsm = safe_get_attr(agent, "get_system_message") + if callable(gsm): + sm = gsm() + content = safe_str(safe_get_attr(sm, "content") or "") + except Exception: + content = "" + if not content: + history = safe_get_attr(state, "history") or [] + if isinstance(history, list): + for ev in history: + if type(ev).__name__ == "SystemMessageAction": + content = safe_str(safe_get_attr(ev, "content") or "") + if content: + break + if not content: + return [] + return [{"type": "text", "content": content}] + + +# --------------------------------------------------------------------------- +# ENTRY: openhands.core.main.run_controller +# --------------------------------------------------------------------------- + + +class RunControllerWrapper: + """ENTRY span around the V0 CLI/headless ``run_controller`` coroutine. + + Stashes the active OTel Context (with the ENTRY span attached) keyed + by ``sid`` so STEP / TOOL spans firing in worker threads can re-attach + it and remain in the same trace. + """ + + __slots__ = ("_tracer",) + + def __init__(self, tracer: Tracer): + self._tracer = tracer + + def __call__(self, wrapped, instance, args, kwargs): + return self._impl(wrapped, instance, args, kwargs) + + async def _impl(self, wrapped, instance, args, kwargs): + if not OTEL_INSTRUMENTATION_OPENHANDS_OUTER_SPANS: + return await wrapped(*args, **kwargs) + + config = kwargs.get("config") + if config is None and args: + config = args[0] + initial_user_action = kwargs.get("initial_user_action") + if initial_user_action is None and len(args) >= 2: + initial_user_action = args[1] + sid = kwargs.get("sid") + if sid is None and len(args) >= 3: + sid = args[2] + # When sid wasn't passed, we don't yet know the auto-generated one; + # the controller will publish ``controller.id`` later. We update + # the stash again from inside the AGENT wrapper. + + span = self._tracer.start_span("enter openhands", kind=SpanKind.INTERNAL) + _set_common(span, "ENTRY") + span.set_attribute(GenAI.GEN_AI_OPERATION_NAME, "enter") + if sid: + span.set_attribute(GEN_AI_SESSION_ID, safe_str(sid)) + span.set_attribute(GEN_AI_CONVERSATION_ID, safe_str(sid)) + model = _extract_model_from_config(config) + if model: + span.set_attribute(GEN_AI_REQUEST_MODEL, model) + + input_text = _extract_input_message_text(initial_user_action) + preview = maybe_preview(input_text) + if preview: + span.set_attribute(OH_INITIAL_MESSAGE_PREVIEW, preview) + captured_input = ( + maybe_to_json_str({"role": "user", "content": input_text}) + if input_text + else "" + ) + if captured_input: + entry_input_messages = _entry_input_messages_from_initial( + initial_user_action + ) + _set_io( + span, + input_value=captured_input, + input_messages=entry_input_messages, + ) + + ctx = set_span_in_context(span) + token = otel_context.attach(ctx) + if sid: + store_context(sid, ctx) + try: + try: + result = await wrapped(*args, **kwargs) + except BaseException as exc: + span.record_exception(exc) + span.set_status(Status(StatusCode.ERROR, type(exc).__qualname__)) + raise + try: + final_state_repr = _final_state_to_output(result) + entry_input_messages, entry_output_messages = _entry_io_from_state( + result + ) + if final_state_repr: + _set_io( + span, + output_value=final_state_repr, + input_messages=entry_input_messages, + output_messages=entry_output_messages, + ) + agent_state = safe_get_attr(result, "agent_state") + if agent_state is not None: + span.set_attribute( + OH_AGENT_STATE, + safe_get_attr(agent_state, "value") or safe_str(agent_state), + ) + elif entry_input_messages or entry_output_messages: + _set_io( + span, + input_messages=entry_input_messages, + output_messages=entry_output_messages, + ) + except Exception: + pass + return result + finally: + try: + otel_context.detach(token) + except Exception: + pass + if sid: + clear_context(sid) + span.end() + + +# --------------------------------------------------------------------------- +# AGENT: openhands.core.loop.run_agent_until_done +# --------------------------------------------------------------------------- + + +class RunAgentUntilDoneWrapper: + """AGENT span around the V0 polling loop. + + Re-attaches the ENTRY context (in case asyncio task creation didn't + propagate it for some reason) and re-stashes a fresh context that now + also includes the AGENT span — that's what STEP / TOOL re-attach. + """ + + __slots__ = ("_tracer",) + + def __init__(self, tracer: Tracer): + self._tracer = tracer + + def __call__(self, wrapped, instance, args, kwargs): + return self._impl(wrapped, instance, args, kwargs) + + async def _impl(self, wrapped, instance, args, kwargs): + if not OTEL_INSTRUMENTATION_OPENHANDS_OUTER_SPANS: + return await wrapped(*args, **kwargs) + + controller = kwargs.get("controller") + if controller is None and args: + controller = args[0] + agent = safe_get_attr(controller, "agent") + agent_name = safe_get_attr(agent, "name") or "codeact" + agent_class = ( + f"{type(agent).__module__}.{type(agent).__name__}" if agent else "" + ) + sid = safe_str(safe_get_attr(controller, "id") or "") + llm = safe_get_attr(agent, "llm") + llm_config = safe_get_attr(llm, "config") + model = safe_get_attr(llm_config, "model") or safe_get_attr(llm, "model") + + # If AgentController.__init__ already opened lifecycle-bound ENTRY+AGENT + # spans, do not create a second AGENT here. Just run the loop with the + # existing AGENT context current so STEP/LLM/TOOL remain descendants. + lifecycle_agent_span = getattr(controller, _AGENT_SPAN_ATTR, None) + lifecycle_agent_ctx = getattr(controller, _AGENT_CTX_ATTR, None) + if lifecycle_agent_span is not None and lifecycle_agent_ctx is not None: + try: + _capture_agent_io_attributes( + lifecycle_agent_span, + controller, + agent, + safe_get_attr(controller, "state"), + ) + except Exception: + pass + lifecycle_token = otel_context.attach(lifecycle_agent_ctx) + try: + return await wrapped(*args, **kwargs) + except BaseException as exc: + try: + lifecycle_agent_span.record_exception(exc) + lifecycle_agent_span.set_status( + Status(StatusCode.ERROR, type(exc).__qualname__) + ) + except Exception: + pass + raise + finally: + try: + state = safe_get_attr(controller, "state") + _capture_agent_io_attributes( + lifecycle_agent_span, controller, agent, state + ) + history = safe_get_attr(state, "history") or [] + if isinstance(history, list): + lifecycle_agent_span.set_attribute(OH_HISTORY_LENGTH, len(history)) + except Exception: + pass + try: + otel_context.detach(lifecycle_token) + except Exception: + pass + + # Bridge: re-attach whatever the ENTRY wrapper stashed (works even + # if asyncio.create_task somehow lost the context, and is the only + # way for the worker-thread STEP / TOOL spans to find us). + attach_ctx = get_context(sid) + fallback_entry_span: trace_api.Span | None = None + if attach_ctx is None: + fallback_entry_span = self._tracer.start_span( + "enter openhands", kind=SpanKind.INTERNAL + ) + _set_common(fallback_entry_span, "ENTRY") + fallback_entry_span.set_attribute(GenAI.GEN_AI_OPERATION_NAME, "enter") + if sid: + fallback_entry_span.set_attribute(GEN_AI_SESSION_ID, sid) + fallback_entry_span.set_attribute(GEN_AI_CONVERSATION_ID, sid) + if agent_class: + fallback_entry_span.set_attribute(OH_AGENT_NAME, agent_class) + if model: + fallback_entry_span.set_attribute(GEN_AI_REQUEST_MODEL, safe_str(model)) + try: + state = safe_get_attr(controller, "state") + entry_input_messages, _ = _entry_io_from_state(state) + if entry_input_messages: + _set_io( + fallback_entry_span, + input_value=entry_input_messages, + input_messages=entry_input_messages, + ) + except Exception: + pass + attach_ctx = set_span_in_context(fallback_entry_span) + if sid: + store_context(sid, attach_ctx) + if attach_ctx is not None: + attach_token = otel_context.attach(attach_ctx) + else: + attach_token = None + + try: + span = self._tracer.start_span( + f"invoke_agent {agent_name}", + kind=SpanKind.INTERNAL, + context=attach_ctx, + ) + _set_common(span, "AGENT") + span.set_attribute( + GenAI.GEN_AI_OPERATION_NAME, + GenAI.GenAiOperationNameValues.INVOKE_AGENT.value, + ) + span.set_attribute(GenAI.GEN_AI_AGENT_NAME, safe_str(agent_name)) + if agent_class: + span.set_attribute(OH_AGENT_NAME, agent_class) + if sid: + span.set_attribute(GEN_AI_SESSION_ID, sid) + span.set_attribute(GEN_AI_CONVERSATION_ID, sid) + span.set_attribute(GEN_AI_AGENT_ID, sid) + if model: + span.set_attribute(GEN_AI_REQUEST_MODEL, safe_str(model)) + + # Capture the agent's tool registry so the TOOL wrapper (which + # only sees a Runtime instance) can resolve tool descriptions + # and produce ``gen_ai.tool.description``. Also emit + # ``gen_ai.tool.definitions`` on this AGENT span itself per the + # ARMS GenAI semconv §Agent — minimal {type,name} entries by + # default; full definitions only when content capture is on. + try: + tools = safe_get_attr(agent, "tools") or [] + if sid: + store_tool_registry(sid, tools) + tool_defs_summary: list[dict[str, Any]] = [] + for t in tools: + if isinstance(t, dict): + kind = t.get("type") or "function" + fn = t.get("function") or {} + name = fn.get("name") if isinstance(fn, dict) else None + else: + kind = safe_get_attr(t, "type") or "function" + fn = safe_get_attr(t, "function") + name = safe_get_attr(fn, "name") + if not name: + continue + item: dict[str, Any] = {"type": safe_str(kind), "name": safe_str(name)} + if isinstance(fn, dict): + desc = fn.get("description") + params = fn.get("parameters") + else: + desc = safe_get_attr(fn, "description") + params = safe_get_attr(fn, "parameters") + if desc: + item["description"] = safe_str(desc) + if params: + item["parameters"] = params + tool_defs_summary.append(item) + if tool_defs_summary: + span.set_attribute( + GEN_AI_TOOL_DEFINITIONS, to_json_str(tool_defs_summary) + ) + except Exception: + pass + + # Capture initial user/system context for AGENT using the same + # ARMS message schema as the lifecycle-bound AGENT path. + try: + state = safe_get_attr(controller, "state") + _capture_agent_io_attributes(span, controller, agent, state) + except Exception: + pass + + # Stash the context that now includes the AGENT span so STEP / + # TOOL re-attach correctly even when running in worker threads. + ctx_with_agent = set_span_in_context(span) + if sid: + store_context(sid, ctx_with_agent) + # Mirror onto the controller too — STEP wrapper uses this when + # closing a STEP to restore the session stash to AGENT instead + # of leaving a dangling closed-STEP context behind. + if controller is not None: + try: + setattr(controller, _AGENT_CTX_ATTR, ctx_with_agent) + setattr(controller, _AGENT_SPAN_ATTR, span) + except Exception: + pass + if getattr(controller, _STEP_SPAN_ATTR, None) is None: + try: + warmup_step = self._tracer.start_span( + "react step", + kind=SpanKind.INTERNAL, + context=ctx_with_agent, + ) + _set_common(warmup_step, "STEP") + warmup_step.set_attribute(GenAI.GEN_AI_OPERATION_NAME, "react") + warmup_step.set_attribute(OH_REACT_ROUND, 1) + warmup_step.set_attribute( + GenAI.GEN_AI_AGENT_NAME, safe_str(agent_name) + ) + if sid: + warmup_step.set_attribute(GEN_AI_SESSION_ID, sid) + warmup_step.set_attribute(GEN_AI_CONVERSATION_ID, sid) + warmup_step.set_attribute(GEN_AI_AGENT_ID, sid) + setattr(controller, _STEP_SPAN_ATTR, warmup_step) + setattr(controller, "_otel_oh_round", 1) + setattr(controller, "_otel_oh_step_consumed", False) + if sid: + store_context(sid, set_span_in_context(warmup_step)) + except Exception: + pass + agent_token = otel_context.attach(ctx_with_agent) + try: + try: + result = await wrapped(*args, **kwargs) + except BaseException as exc: + span.record_exception(exc) + span.set_status( + Status(StatusCode.ERROR, type(exc).__qualname__) + ) + raise + # Capture final AGENT I/O using ARMS gen_ai.* message attrs. + try: + state = safe_get_attr(controller, "state") + _capture_agent_io_attributes(span, controller, agent, state) + output_repr = _final_state_to_output(state) + if output_repr: + _set_io(span, output_value=output_repr) + if state is not None: + agent_state = safe_get_attr(state, "agent_state") + if agent_state is not None: + span.set_attribute( + OH_AGENT_STATE, + safe_get_attr(agent_state, "value") + or safe_str(agent_state), + ) + history = safe_get_attr(state, "history") or [] + if isinstance(history, list): + span.set_attribute(OH_HISTORY_LENGTH, len(history)) + except Exception: + pass + return result + finally: + try: + otel_context.detach(agent_token) + except Exception: + pass + if controller is not None: + try: + if getattr(controller, _AGENT_SPAN_ATTR, None) is span: + setattr(controller, _AGENT_SPAN_ATTR, None) + except Exception: + pass + try: + _close_open_step(controller) + except Exception: + pass + span.end() + finally: + if attach_token is not None: + try: + otel_context.detach(attach_token) + except Exception: + pass + if fallback_entry_span is not None: + try: + state = safe_get_attr(controller, "state") + output_repr = _final_state_to_output(state) + entry_input_messages, entry_output_messages = _entry_io_from_state( + state + ) + if output_repr: + _set_io( + fallback_entry_span, + output_value=output_repr, + input_messages=entry_input_messages, + output_messages=entry_output_messages, + ) + elif entry_input_messages or entry_output_messages: + _set_io( + fallback_entry_span, + input_messages=entry_input_messages, + output_messages=entry_output_messages, + ) + history = safe_get_attr(state, "history") or [] + if isinstance(history, list): + fallback_entry_span.set_attribute(OH_HISTORY_LENGTH, len(history)) + except Exception: + pass + try: + fallback_entry_span.end() + except Exception: + pass + if sid: + try: + clear_context(sid) + except Exception: + pass + + +# --------------------------------------------------------------------------- +# STEP: AgentController._step +# --------------------------------------------------------------------------- + + +def _close_open_step(controller: Any) -> None: + """End the controller's currently-open STEP span, if any. + + Restores the session-context stash to the controller's AGENT context + (kept under ``_AGENT_CTX_ATTR``) so subsequent TOOL spans are still + parented correctly even after the last STEP closes. + + Crucially, this function only ends the *span* — it never touches an + attach-token. The STEP wrapper attaches/detaches the STEP context + in a balanced pair *inside* the ``_step`` coroutine; cross-task + propagation happens via the ``Context`` object stashed in + :mod:`session_context`, which can be re-attached safely from any + task / thread because every attach is paired with a detach inside + its creating context. + """ + span = getattr(controller, _STEP_SPAN_ATTR, None) + if span is None: + return + try: + span.end() + except Exception: + pass + try: + setattr(controller, _STEP_SPAN_ATTR, None) + except Exception: + pass + sid = safe_str(safe_get_attr(controller, "id") or "") + agent_ctx = getattr(controller, _AGENT_CTX_ATTR, None) + if sid and agent_ctx is not None: + store_context(sid, agent_ctx) + + +class AgentControllerStepWrapper: + """STEP span around one ReAct iteration of the V0 controller. + + The STEP span is intentionally **kept open across the return of + ``_step``**. Why: ``Runtime.run_action`` runs *later*, in a thread-pool + executor (``call_sync_from_async`` inside ``_handle_action``), so by + the time TOOL fires the STEP coroutine has already returned. Closing + STEP at end of ``_step`` would make every TOOL a sibling of STEP + (parented under AGENT) instead of a child. + + Lifecycle: + + 1. New ``_step`` invoked → close *previous* STEP if any → open new + STEP (child of AGENT) → stash STEP context under ``sid`` so that + TOOL / LLM spans firing on worker threads re-attach STEP. + 2. ``_step`` body runs to completion. We do **not** close STEP here. + 3. The next ``_step`` (or ``AgentController.close``) closes the + still-open STEP. + """ + + __slots__ = ("_tracer",) + + def __init__(self, tracer: Tracer): + self._tracer = tracer + + def __call__(self, wrapped, instance, args, kwargs): + return self._impl(wrapped, instance, args, kwargs) + + @staticmethod + def _will_step_be_noop(instance: Any) -> bool: + """Return True if this ``_step`` call will short-circuit without + producing real work (state != RUNNING, or a pending action is + already queued). We skip span emission for these so the round + counter stays sequential (1, 2, 3, ...) instead of inflating to + (1, 3, 5, ...) with empty 0.5ms STEP spans cluttering the trace. + + This mirrors the early-return checks at the top of + ``AgentController._step`` (state-check + ``_pending_action``). + We read ``_pending_action_info`` directly rather than going + through the ``_pending_action`` *property* — the property has + logging side effects (it can emit a "pending action active for + Xs" log line at warn-level) that we don't want to trigger from + an instrumentation hot path. + """ + try: + state = safe_get_attr(instance, "state") + agent_state = safe_get_attr(state, "agent_state") + # AgentState enum value is 'running' (case-insensitive). + agent_state_str = ( + safe_str(safe_get_attr(agent_state, "value") or agent_state).lower() + ) + if agent_state_str != "running": + return True + # Check the underlying tuple slot, not the property — the + # property's getter is non-trivial in OpenHands. + if getattr(instance, "_pending_action_info", None) is not None: + return True + except Exception: + return False + return False + + @staticmethod + def _snapshot_for_work_detection(instance: Any) -> tuple[int, Any]: + """Snapshot the bits we need to tell whether ``_step`` body did + anything. Returned tuple is (history_length, pending_action_id). + Used by ``_impl`` to detect "empty" STEP invocations that get + through ``_will_step_be_noop`` (e.g. ``state_tracker`` raised, + ``_is_stuck`` early-returned, ``agent.step`` returned ``None``) + and shouldn't show up in the trace as 0.3ms placeholder spans. + """ + try: + state = safe_get_attr(instance, "state") + history = safe_get_attr(state, "history") + history_len = len(history) if isinstance(history, list) else 0 + except Exception: + history_len = 0 + try: + info = getattr(instance, "_pending_action_info", None) + pending_id = id(info) if info is not None else None + except Exception: + pending_id = None + return history_len, pending_id + + async def _impl(self, wrapped, instance, args, kwargs): + if not OTEL_INSTRUMENTATION_OPENHANDS_OUTER_SPANS: + return await wrapped(*args, **kwargs) + + # Skip no-op _step invocations entirely so the trace shows only + # the rounds that actually do work (LLM call + tool dispatch). + if self._will_step_be_noop(instance): + return await wrapped(*args, **kwargs) + + sid = safe_str(safe_get_attr(instance, "id") or "") + agent = safe_get_attr(instance, "agent") + agent_name = safe_get_attr(agent, "name") or "codeact" + + # Snapshot the AGENT context if we don't already have one so + # ``_close_open_step`` can restore the session stash to AGENT + # after STEP ends. + if not hasattr(instance, _AGENT_CTX_ATTR) or getattr(instance, _AGENT_CTX_ATTR, None) is None: + try: + setattr(instance, _AGENT_CTX_ATTR, get_context(sid)) + except Exception: + pass + + # ----- Reuse warmup STEP if not yet consumed ----- + # The init wrapper opens a warmup STEP (round 1) so pre-step + # actions like RECALL parent under STEP 1. The first real + # ``_step`` reuses that STEP (without bumping the round) so the + # LLM call + first LLM-driven tool also nest under STEP 1. From + # the second real ``_step`` onward, we close the previous STEP + # and open a new one with round = previous + 1. + existing_step = getattr(instance, _STEP_SPAN_ATTR, None) + consumed = bool(getattr(instance, "_otel_oh_step_consumed", True)) + reused_warmup = False + is_new_span = False + if existing_step is not None and not consumed: + span = existing_step + round_num = int(getattr(instance, "_otel_oh_round", 1) or 1) + reused_warmup = True + try: + setattr(instance, "_otel_oh_step_consumed", True) + except Exception: + pass + else: + # Close any still-open consumed STEP from the previous round + # before opening a new one. + _close_open_step(instance) + # Tentative round number — only committed if body does work. + round_num = int(getattr(instance, "_otel_oh_round", 0) or 0) + 1 + + # Open the new STEP as a child of AGENT. Prefer the explicit + # AGENT context (more reliable than relying on contextvars + # propagation across asyncio task / thread boundaries). + agent_ctx = getattr(instance, _AGENT_CTX_ATTR, None) + if agent_ctx is None and sid: + agent_ctx = get_context(sid) + try: + span = self._tracer.start_span( + "react step", + kind=SpanKind.INTERNAL, + context=agent_ctx, + ) + except Exception: + # Fall back to current-context-based parenting if explicit + # context= isn't accepted (older OTel SDKs). + with AttachedSession(sid): + span = self._tracer.start_span( + "react step", kind=SpanKind.INTERNAL + ) + _set_common(span, "STEP") + span.set_attribute(GenAI.GEN_AI_OPERATION_NAME, "react") + span.set_attribute(OH_REACT_ROUND, round_num) + span.set_attribute(GenAI.GEN_AI_AGENT_NAME, safe_str(agent_name)) + if sid: + span.set_attribute(GEN_AI_SESSION_ID, sid) + span.set_attribute(GEN_AI_CONVERSATION_ID, sid) + span.set_attribute(GEN_AI_AGENT_ID, sid) + is_new_span = True + try: + setattr(instance, _STEP_SPAN_ATTR, span) + setattr(instance, "_otel_oh_step_consumed", True) + except Exception: + try: + span.end() + except Exception: + pass + return await wrapped(*args, **kwargs) + + # Capture INPUT: messages going into this step. + try: + state = safe_get_attr(instance, "state") + history = safe_get_attr(state, "history") or [] + if isinstance(history, list): + span.set_attribute(OH_HISTORY_LENGTH, len(history)) + input_messages = _state_to_input_messages(state) + if input_messages: + _set_io( + span, + input_value=input_messages, + input_messages=input_messages, + ) + except Exception: + pass + + # Build the STEP context object. Cross-thread propagation goes + # through this Context object stashed in session_context (TOOL / + # LLM wrappers re-attach it inside their own scopes with paired + # attach/detach so no token ever crosses a context boundary). + step_ctx = set_span_in_context(span) + if sid: + store_context(sid, step_ctx) + + # Snapshot pre-body state so we can detect "empty" body that + # got through ``_will_step_be_noop`` (e.g. ``state_tracker`` + # raised inside ``_step``, ``_is_stuck`` early-returned, or + # ``agent.step`` returned ``None`` / raised handled error). + pre_history_len, pre_pending_id = self._snapshot_for_work_detection( + instance + ) + + # Attach STEP for the *body's* contextvars propagation only. + # Both attach and the matching detach happen in this coroutine's + # own context, so the Aliyun SDK's strict token check is happy. + step_token = otel_context.attach(step_ctx) + body_error: BaseException | None = None + try: + result = await wrapped(*args, **kwargs) + except BaseException as exc: + body_error = exc + finally: + try: + otel_context.detach(step_token) + except Exception: + pass + + if body_error is not None: + try: + span.set_attribute( + "gen_ai.react.finish_reason", type(body_error).__qualname__ + ) + span.record_exception(body_error) + span.set_status( + Status(StatusCode.ERROR, type(body_error).__qualname__) + ) + except Exception: + pass + # On error, close STEP now so the failure surfaces cleanly + # rather than waiting for the next _step / controller close. + _close_open_step(instance) + # Make sure the round counter we *tentatively* assigned for + # this STEP gets committed so subsequent rounds renumber + # past it instead of overlapping. + if is_new_span: + try: + instance._otel_oh_round = round_num + except Exception: + pass + raise body_error + + # Detect post-body "empty" STEP — the wrapper passed the + # ``_will_step_be_noop`` pre-check but the body still produced + # zero observable work (no new history events, no new pending + # action). The user has explicitly asked us not to clutter the + # trace with sub-millisecond placeholder STEP spans, so: + # + # * If we *opened* a fresh span this round, end it immediately, + # mark it ``openhands.step.empty=true``, and DO NOT bump the + # committed round counter. Next real _step opens a fresh STEP + # with the same round number — the empty span still appears + # in the trace (we have no way to suppress export from inside + # a wrapper), but with a clear ``empty=true`` marker so it's + # trivially filterable in the dashboard. + # * If we *reused* a warmup / persisted STEP that was already + # meaningful (had earlier RECALL/TOOL children), keep it open + # and don't mark it empty — the children give it value. + post_history_len, post_pending_id = self._snapshot_for_work_detection( + instance + ) + did_work = ( + post_history_len > pre_history_len + or (post_pending_id is not None and post_pending_id != pre_pending_id) + ) + + if not did_work and is_new_span: + try: + span.set_attribute("openhands.step.empty", True) + span.set_attribute( + "gen_ai.react.finish_reason", "noop_step_body" + ) + span.end() + except Exception: + pass + # Forget this empty STEP so the next _step opens a fresh one + # without trying to close-or-reuse this one. + try: + if getattr(instance, _STEP_SPAN_ATTR, None) is span: + setattr(instance, _STEP_SPAN_ATTR, None) + except Exception: + pass + try: + # Roll back to the previous committed round (don't + # advance the counter for an empty STEP). + instance._otel_oh_round = round_num - 1 + instance._otel_oh_step_consumed = True + except Exception: + pass + # Restore session stash to AGENT so subsequent TOOLs land + # under AGENT (not under a now-ended STEP). + if sid: + agent_ctx = getattr(instance, _AGENT_CTX_ATTR, None) + if agent_ctx is not None: + try: + store_context(sid, agent_ctx) + except Exception: + pass + return result + + # Body did work — commit the round counter (we only update it + # *after* we're sure the STEP is meaningful). + if is_new_span: + try: + instance._otel_oh_round = round_num + except Exception: + pass + + # Capture OUTPUT: the freshly-decided pending action. + try: + pending = getattr(instance, "_pending_action", None) + state = safe_get_attr(instance, "state") + agent_state = safe_get_attr(state, "agent_state") + if agent_state is not None: + span.set_attribute( + OH_AGENT_STATE, + safe_get_attr(agent_state, "value") + or safe_str(agent_state), + ) + if pending is not None: + action_type = _action_type_value(pending) + if action_type: + span.set_attribute(OH_ACTION_TYPE, action_type) + out = action_to_genai_output(pending) + if out: + _set_io(span, output_value=out, output_messages=out) + except Exception: + pass + + # Mirror the latest history snapshot back up to the AGENT span + # so AGENT's input.value / gen_ai.input.messages stay current + # *during* the run (not just at close-time). The user wants to + # see the conversation accumulate on AGENT live, since the + # downstream dashboards may read AGENT before the controller + # actually closes. + try: + agent_span = getattr(instance, _AGENT_SPAN_ATTR, None) + if agent_span is not None: + _capture_agent_io_attributes( + agent_span, instance, agent, safe_get_attr(instance, "state") + ) + except Exception: + pass + + # Mark the warmup STEP (round 1) the moment we know it carries + # real work — it now contains LLM/TOOL children and matters. + if reused_warmup: + try: + span.set_attribute("openhands.step.warmup_consumed", True) + except Exception: + pass + + # STEP span stays open here — it lives until the next _step (or + # AgentController.close) ends it. Until then any TOOL fired by + # Runtime.run_action on a thread-pool worker will re-attach the + # STEP context object stashed above and become its child. + return result + + +# --------------------------------------------------------------------------- +# TOOL: Runtime.run_action +# --------------------------------------------------------------------------- + + +_TOOL_KIND_TO_NAME: dict[str, str] = { + "run": "bash", + "run_ipython": "ipython", + "browse_interactive": "browser", + "browse": "browser", + "edit": "str_replace_editor", + "read": "file_read", + "write": "file_write", + "delegate": "delegate", + "finish": "finish", + "think": "think", + "task_tracking": "task_tracker", + "mcp": "mcp", + "send_message": "send_message", + # ``recall`` is a real (non-LLM-initiated) tool: the controller posts + # a RecallAction and the memory subsystem runs it just like any other + # action via ``Runtime.run_action``. Worth a TOOL span. + "recall": "recall", +} + +# Action types that are *not* real tool calls — they're internal control +# events posted by the controller / event-stream itself (system prompt, +# user message, agent-state transition, no-ops). Emitting TOOL spans for +# these clutters the trace tree and confuses the GenAI semconv (these +# aren't things the LLM "called"). +_INTERNAL_ACTION_TYPES: frozenset[str] = frozenset( + { + "message", + "system", + "change_agent_state", + "agent_state_changed", + "null", + "noop", + } +) + + +def _action_type_value(action: Any) -> str: + """Best-effort extract the canonical action-type string for ``action``. + + OpenHands declares ``ActionType`` as ``class ActionType(str, Enum)`` + with members like ``MESSAGE = 'message'``. Each Action subclass sets + ``action: str = ActionType.MESSAGE``. ``str(ActionType.MESSAGE)`` + returns ``'ActionType.MESSAGE'`` (Python's default Enum.__str__), + *not* the value ``'message'`` we want for filtering / lookup. This + helper prefers ``.value`` when the attribute is enum-like, else the + raw string. + """ + raw = safe_get_attr(action, "action") + if raw is None: + return "" + val = safe_get_attr(raw, "value") + if val is not None: + return safe_str(val).lower() + text = safe_str(raw).lower() + # ``str(ActionType.MESSAGE)`` → "actiontype.message"; strip the prefix. + prefix = "actiontype." + if text.startswith(prefix): + return text[len(prefix):] + return text + + +def _is_real_tool_call(action: Any) -> bool: + """Return True iff ``action`` represents a meaningful tool execution. + + Filtering rules (in order): + + 1. **Internal action types are *always* dropped** even when the + action carries ``tool_call_metadata``. OpenHands lets the LLM + produce ``MessageAction`` (via the ``send_message`` "tool"), + ``SystemMessageAction``, ``ChangeAgentStateAction`` etc. — those + are coordination signals, not real tool executions, and they + clutter the trace with sub-millisecond noise spans that the user + has explicitly asked us to suppress. + 2. Otherwise, an action qualifies if it has ``tool_call_metadata`` + (i.e. it was produced from an LLM ``tool_calls`` response — e.g. + ``execute_bash``, ``str_replace_editor``), or + 3. Its action-type is in the executable-tool whitelist + (``_TOOL_KIND_TO_NAME``) — this catches synthesized actions like + ``RECALL`` that don't come from the LLM but are still worth + tracing as TOOL spans (memory retrieval, microagent loading, + etc.). + """ + action_type = _action_type_value(action) + # Always drop internal/system actions regardless of how they were + # produced — see rule 1 above. + if action_type and action_type in _INTERNAL_ACTION_TYPES: + return False + if safe_get_attr(action, "tool_call_metadata") is not None: + return True + if not action_type: + return False + return action_type in _TOOL_KIND_TO_NAME + + +def _extract_tool_name(action: Any) -> tuple[str, str]: + """Return (tool_name, action_type). + + Prefers the function name carried on ``action.tool_call_metadata`` + (set when the action came from an LLM tool call) — that's what the + LLM and our LLM-side instrumentation know it as. Falls back to the + canonical action-type string (``ActionType.RECALL`` → ``"recall"``) + mapped through ``_TOOL_KIND_TO_NAME``. + """ + action_type = _action_type_value(action) + tcm = safe_get_attr(action, "tool_call_metadata") + if tcm is not None: + fn = safe_get_attr(tcm, "function_name") + if fn: + return safe_str(fn), action_type + tool_name = _TOOL_KIND_TO_NAME.get(action_type, action_type or "agent.action") + return tool_name, action_type + + +def _extract_tool_call_id(action: Any) -> str: + tcm = safe_get_attr(action, "tool_call_metadata") + if tcm is None: + return "" + return safe_str(safe_get_attr(tcm, "tool_call_id") or "") + + +def _runtime_sid(instance: Any) -> str: + """Best-effort discover the session id from a Runtime instance.""" + sid = safe_get_attr(instance, "sid") + if sid: + return safe_str(sid) + es = safe_get_attr(instance, "event_stream") + es_sid = safe_get_attr(es, "sid") + if es_sid: + return safe_str(es_sid) + return "" + + +class RuntimeRunActionWrapper: + """TOOL span around ``Runtime.run_action``. + + Bridges the session context across worker threads, then opens a TOOL + span whose ``input.value`` describes the action and whose + ``output.value`` describes the resulting observation. + """ + + __slots__ = ("_tracer",) + + def __init__(self, tracer: Tracer): + self._tracer = tracer + + def __call__(self, wrapped, instance, args, kwargs): + if not OTEL_INSTRUMENTATION_OPENHANDS_OUTER_SPANS: + return wrapped(*args, **kwargs) + + action = args[0] if args else kwargs.get("action") + # Skip internal control events — system prompts, user messages, + # memory recalls, agent-state transitions etc. aren't tool calls + # and shouldn't appear as TOOL spans alongside the real ones. + if not _is_real_tool_call(action): + return wrapped(*args, **kwargs) + + tool_name, action_type = _extract_tool_name(action) + tool_call_id = _extract_tool_call_id(action) + runtime_class = ( + f"{type(instance).__module__}.{type(instance).__name__}" + if instance + else "" + ) + sid = _runtime_sid(instance) + + # Look up the session-stashed context (STEP if a step is open, + # AGENT otherwise) and use it as the *explicit* parent context + # for the TOOL span. Explicit context= is more robust than + # relying on contextvars propagation across worker threads — it + # always parents under the latest STEP/AGENT no matter what + # thread/loop the runtime is running on. + parent_ctx = get_context(sid) + try: + span = self._tracer.start_span( + f"execute_tool {tool_name}", + kind=SpanKind.INTERNAL, + context=parent_ctx, + ) + except Exception: + with AttachedSession(sid): + span = self._tracer.start_span( + f"execute_tool {tool_name}", kind=SpanKind.INTERNAL + ) + # The TOOL span itself is parented *explicitly* via context= + # above. We additionally attach the session context throughout + # the wrapped call so any nested spans created by the runtime + # (e.g. a retried LLM call) that go through the contextvars + # propagation path also inherit the right session — and the + # ``otel_context.attach(set_span_in_context(span))`` below makes + # the TOOL itself current so retry-spawned child spans nest + # under TOOL, not under its parent STEP. + with AttachedSession(sid): + # ARMS GenAI semconv (Tool): + # gen_ai.span.kind=TOOL, gen_ai.operation.name=execute_tool, + # gen_ai.tool.name, gen_ai.tool.type + # gen_ai.tool.call.id, gen_ai.tool.description [recommended] + # gen_ai.tool.call.arguments, gen_ai.tool.call.result + # [optional, gated on capture-message-content] + _set_common(span, "TOOL") + span.set_attribute( + GenAI.GEN_AI_OPERATION_NAME, + GenAI.GenAiOperationNameValues.EXECUTE_TOOL.value, + ) + span.set_attribute(GenAI.GEN_AI_TOOL_NAME, tool_name) + span.set_attribute(GenAI.GEN_AI_TOOL_TYPE, "function") + if tool_call_id: + span.set_attribute(GEN_AI_TOOL_CALL_ID, tool_call_id) + if action_type: + # ``action_type`` from ``_extract_tool_name`` is the + # canonical lowercased value (e.g. ``"recall"``), suitable + # for ``openhands.action.type``. + span.set_attribute(OH_ACTION_TYPE, action_type) + if runtime_class: + span.set_attribute(OH_RUNTIME_NAME, runtime_class) + if sid: + span.set_attribute(GEN_AI_SESSION_ID, sid) + span.set_attribute(GEN_AI_CONVERSATION_ID, sid) + + # gen_ai.tool.description — looked up via the per-sid registry + # populated by the AGENT wrapper from ``controller.agent.tools``. + try: + tool_def = get_tool_definition(sid, tool_name) + if tool_def is not None: + if isinstance(tool_def, dict): + fn = tool_def.get("function") or {} + desc = fn.get("description") if isinstance(fn, dict) else None + else: + fn = safe_get_attr(tool_def, "function") + desc = safe_get_attr(fn, "description") + if desc: + span.set_attribute(GEN_AI_TOOL_DESCRIPTION, safe_str(desc)) + except Exception: + pass + + # gen_ai.tool.call.arguments + input.value + arguments_dict = _tool_call_arguments(action) + try: + if arguments_dict: + args_json = to_json_str(arguments_dict) + if args_json: + span.set_attribute(GEN_AI_TOOL_CALL_ARGUMENTS, args_json) + # OpenInference compat — input.value mirrors the args. + _set_io(span, input_value=args_json) + # Convenience preview attribute on the action's primary + # input field (command / code / path / ...). + preview_field, preview_text = _first_preview_field(action) + if preview_text: + span.set_attribute( + f"openhands.action.{preview_field}", preview_text + ) + except Exception: + pass + + ctx = set_span_in_context(span) + token = otel_context.attach(ctx) + try: + try: + observation = wrapped(*args, **kwargs) + except BaseException as exc: + span.record_exception(exc) + span.set_status( + Status(StatusCode.ERROR, type(exc).__qualname__) + ) + raise + try: + _annotate_observation(span, observation) + except Exception: + pass + return observation + finally: + try: + otel_context.detach(token) + except Exception: + pass + span.end() + + +def _first_preview_field(action: Any) -> tuple[str, str]: + for attr in ("command", "code", "path", "url", "content"): + v = safe_get_attr(action, attr) + if v: + return attr, safe_str(v) + return "", "" + + +_TOOL_ARG_FIELDS: tuple[str, ...] = ( + "command", + "code", + "path", + "url", + "content", + "task_list", + "name", + "arguments", + "thought", + "is_input", + "blocking", + "keep_prompt", + "translated_ipython_code", + "browser_actions", + "agent_state", + "outputs", + "final_thought", + "old_str", + "new_str", + "view_range", + "file_text", + "insert_line", + "start_line", + "end_line", +) + + +def _tool_call_arguments(action: Any) -> dict[str, Any]: + """Return the bare arguments dict for ``gen_ai.tool.call.arguments``. + + Per ARMS GenAI semconv the value is a JSON string of *just* the call + arguments — e.g. ``{"location": "San Francisco", "date": "2025-10-01"}`` + — not the wrapping ``{"tool": ..., "arguments": ...}`` envelope. + """ + if action is None: + return {} + # When the action came from an LLM tool call, prefer the original + # JSON arguments the model emitted (most faithful to what the LLM + # actually requested). + tcm = safe_get_attr(action, "tool_call_metadata") + model_response = safe_get_attr(tcm, "model_response") if tcm else None + if model_response is not None: + try: + choices = ( + model_response.choices + if hasattr(model_response, "choices") + else None + ) or [] + for choice in choices: + msg = getattr(choice, "message", None) or ( + choice.get("message") if isinstance(choice, dict) else None + ) + tool_calls = ( + getattr(msg, "tool_calls", None) + if msg is not None + else None + ) or (msg.get("tool_calls") if isinstance(msg, dict) else None) + if not tool_calls: + continue + want_id = safe_str(safe_get_attr(tcm, "tool_call_id") or "") + for tc in tool_calls: + tc_id = ( + getattr(tc, "id", None) + if not isinstance(tc, dict) + else tc.get("id") + ) + if want_id and safe_str(tc_id) != want_id: + continue + fn = ( + getattr(tc, "function", None) + if not isinstance(tc, dict) + else tc.get("function") + ) + raw_args = ( + getattr(fn, "arguments", None) + if not isinstance(fn, dict) + else fn.get("arguments") + ) + if isinstance(raw_args, str): + try: + import json as _json + + return _json.loads(raw_args) + except Exception: + return {"raw": raw_args} + if isinstance(raw_args, dict): + return raw_args + except Exception: + pass + # Fallback: harvest known argument-bearing fields off the Action object. + args: dict[str, Any] = {} + for key in _TOOL_ARG_FIELDS: + v = safe_get_attr(action, key) + if v not in (None, "", [], {}): + args[key] = v + return args + + +def _observation_to_result(observation: Any) -> dict[str, Any]: + """Return a dict suitable for ``gen_ai.tool.call.result``.""" + if observation is None: + return {} + payload: dict[str, Any] = {} + for key in ( + "content", + "exit_code", + "error", + "interpreter_details", + "command", + "stdout", + "stderr", + "url", + "screenshot", + "outputs", + ): + v = safe_get_attr(observation, key) + if v not in (None, "", [], {}): + payload[key] = v + return payload + + +def _annotate_observation(span: trace_api.Span, observation: Any) -> None: + if observation is None: + return + obs_type = safe_str( + safe_get_attr(observation, "observation") or type(observation).__name__ + ) + if obs_type: + span.set_attribute(OH_OBSERVATION_TYPE, obs_type) + exit_code = safe_get_attr(observation, "exit_code") + if exit_code is not None: + try: + ec = int(exit_code) + span.set_attribute("openhands.action.exit_code", ec) + if ec != 0: + span.set_status(Status(StatusCode.ERROR, f"exit_code={ec}")) + except (TypeError, ValueError): + pass + error = safe_get_attr(observation, "error") + if error: + span.set_attribute("openhands.observation.error", safe_str(error)) + span.set_status(Status(StatusCode.ERROR, safe_str(error))) + # Emit gen_ai.tool.call.result + OpenInference output.value. + try: + result_payload = _observation_to_result(observation) + result_payload.setdefault("observation", obs_type) + out = to_json_str(result_payload) + if out: + span.set_attribute(GEN_AI_TOOL_CALL_RESULT, out) + span.set_attribute(OUTPUT_VALUE, out) + span.set_attribute(OUTPUT_MIME, "application/json") + except Exception: + pass + + +# --------------------------------------------------------------------------- +# ENTRY + AGENT (controller-lifecycle bound) +# +# Why this exists in addition to RunControllerWrapper / RunAgentUntilDoneWrapper: +# +# When OpenHands V0 is launched via ``python -m openhands.core.main``, Python +# executes ``main.py`` *as ``__main__``*. The ``from openhands.core.loop +# import run_agent_until_done`` (and other from-imports) at the top of +# ``main.py`` bind those symbols into ``__main__``'s namespace **before** +# our instrumentor patches ``openhands.core.main.run_controller`` / +# ``openhands.core.loop.run_agent_until_done``. The ``__main__`` block's +# ``asyncio.run(run_controller(...))`` call uses the *unpatched* local +# reference, so the wrappers above never fire — and the trace appears +# without an ENTRY span. +# +# STEP / TOOL spans work because ``_step`` and ``run_action`` are *class +# methods*: patching ``AgentController._step`` updates the class object +# that both ``__main__.AgentController`` and +# ``openhands.controller.agent_controller.AgentController`` reference, so +# every method lookup at call time finds the wrapped version. +# +# ENTRY+AGENT here exploit the same principle — they hook +# ``AgentController.__init__`` and ``AgentController.close``, both class +# methods, so the spans bracket the controller's lifecycle reliably no +# matter how ``run_controller`` was invoked. They no-op when a session +# context is already stashed for this sid (i.e. ``RunControllerWrapper`` +# fired successfully — the API/test-suite code path). +# --------------------------------------------------------------------------- + + +def _capture_agent_io_attributes( + span: trace_api.Span, controller: Any, agent: Any, state: Any +) -> None: + """Set gen_ai.system_instructions / input.messages / output.messages on + the AGENT span, following the ARMS GenAI semconv schema.""" + try: + sys_instr = _agent_to_system_instructions(agent, state) + if sys_instr: + payload = to_json_str(sys_instr) + if payload: + span.set_attribute(GEN_AI_SYSTEM_INSTRUCTIONS, payload) + # Some downstream ARMS views still look for the legacy singular key. + span.set_attribute(GEN_AI_SYSTEM_INSTRUCTION, payload) + except Exception: + pass + try: + history = safe_get_attr(state, "history") or [] + if isinstance(history, list) and history: + input_msgs = _history_to_input_messages_schema(history) + if input_msgs: + payload = to_json_str(input_msgs) + if payload: + span.set_attribute(GEN_AI_INPUT_MESSAGES, payload) + _set_io(span, input_value=payload) + output_msgs = _history_to_output_messages_schema(history) + if output_msgs: + payload = to_json_str(output_msgs) + if payload: + span.set_attribute(GEN_AI_OUTPUT_MESSAGES, payload) + _set_io(span, output_value=payload) + except Exception: + pass + + +def _open_entry_and_agent_for_controller( + tracer: Tracer, controller: Any +) -> None: + """Open ENTRY (parent) + AGENT (child) + warmup STEP for ``controller``. + + Opening a *warmup STEP* (round 1) right after AGENT means that any + pre-step actions like RECALL — which are dispatched to the runtime + *before* the first ``_step`` invocation — become children of STEP 1 + instead of dangling siblings under AGENT. The first real ``_step`` + call detects that the warmup STEP isn't yet "consumed" and reuses + it (without bumping the round counter) so the LLM call + first + LLM-driven tool also nest under STEP 1. + + All inner span creations use the explicit ``context=`` argument + (instead of relying on ``contextvars`` propagation through + ``otel_context.attach``) — this is the most deterministic way to + parent a child span and avoids the entire class of "Token was + created in a different Context" failures we used to chase across + asyncio-task / thread boundaries. + + Idempotent on ``_OWNS_FLAG`` — safe to call multiple times for the + same controller. Deliberately does **not** check whether a session + context is already stashed: under ``python -m openhands.core.main`` + the from-import binding bypasses ``RunControllerWrapper`` and + ``RunAgentUntilDoneWrapper``, so the init wrapper is the only + reliable source of ENTRY+AGENT and must always run. + """ + if not OTEL_INSTRUMENTATION_OPENHANDS_OUTER_SPANS: + return + if getattr(controller, _OWNS_FLAG, False): + # Already opened (e.g. RunControllerWrapper fired first) — log + # and bail. We don't want to double-emit ENTRY/AGENT. + logger.debug( + "OpenHands instrumentation: ENTRY+AGENT already open on " + "controller %s — skipping init-wrapper open", + id(controller), + ) + return + + sid = safe_str(safe_get_attr(controller, "id") or "") + agent = safe_get_attr(controller, "agent") + agent_name = safe_get_attr(agent, "name") or "codeact" + agent_class = ( + f"{type(agent).__module__}.{type(agent).__name__}" if agent else "" + ) + llm = safe_get_attr(agent, "llm") + llm_config = safe_get_attr(llm, "config") + model = safe_get_attr(llm_config, "model") or safe_get_attr(llm, "model") + + # ----- ENTRY ----- + # If RunControllerWrapper already stashed an ENTRY context, parent AGENT + # directly under it. Otherwise create the lifecycle-owned ENTRY here. + entry: trace_api.Span | None = None + entry_ctx = get_context(sid) + if entry_ctx is None: + try: + entry = tracer.start_span("enter openhands", kind=SpanKind.INTERNAL) + except Exception as exc: + logger.error( + "OpenHands instrumentation: failed to start ENTRY span for " + "sid=%r: %s", + sid, + exc, + exc_info=True, + ) + return + + try: + _set_common(entry, "ENTRY") + entry.set_attribute(GenAI.GEN_AI_OPERATION_NAME, "enter") + if sid: + entry.set_attribute(GEN_AI_SESSION_ID, sid) + entry.set_attribute(GEN_AI_CONVERSATION_ID, sid) + if agent_class: + entry.set_attribute(OH_AGENT_NAME, agent_class) + if model: + entry.set_attribute(GEN_AI_REQUEST_MODEL, safe_str(model)) + state = safe_get_attr(controller, "state") + entry_input_messages, _ = _entry_io_from_state(state) + if entry_input_messages: + _set_io( + entry, + input_value=entry_input_messages, + input_messages=entry_input_messages, + ) + except Exception as exc: + logger.debug("OpenHands instrumentation: ENTRY attr setup: %s", exc) + + entry_ctx = set_span_in_context(entry) + + # ----- AGENT (child of ENTRY) ----- + # Pass ``context=entry_ctx`` *explicitly* so AGENT inherits ENTRY + # as parent regardless of what the surrounding contextvars look + # like (some 3rd-party SDKs reset contextvars between calls). + try: + agent_span = tracer.start_span( + f"invoke_agent {agent_name}", + kind=SpanKind.INTERNAL, + context=entry_ctx, + ) + except Exception as exc: + logger.error( + "OpenHands instrumentation: failed to start AGENT span for " + "sid=%r: %s", + sid, + exc, + exc_info=True, + ) + if entry is not None: + try: + entry.end() + except Exception: + pass + return + + try: + _set_common(agent_span, "AGENT") + agent_span.set_attribute( + GenAI.GEN_AI_OPERATION_NAME, + GenAI.GenAiOperationNameValues.INVOKE_AGENT.value, + ) + agent_span.set_attribute(GenAI.GEN_AI_AGENT_NAME, safe_str(agent_name)) + if agent_class: + agent_span.set_attribute(OH_AGENT_NAME, agent_class) + if sid: + agent_span.set_attribute(GEN_AI_SESSION_ID, sid) + agent_span.set_attribute(GEN_AI_CONVERSATION_ID, sid) + agent_span.set_attribute(GEN_AI_AGENT_ID, sid) + if model: + agent_span.set_attribute(GEN_AI_REQUEST_MODEL, safe_str(model)) + except Exception as exc: + logger.debug("OpenHands instrumentation: AGENT attr setup: %s", exc) + + # Tool registry + gen_ai.tool.definitions — same logic as + # RunAgentUntilDoneWrapper, since this path also needs the + # registry for downstream TOOL spans. + try: + tools = safe_get_attr(agent, "tools") or [] + if sid: + store_tool_registry(sid, tools) + defs_summary: list[dict[str, Any]] = [] + for t in tools: + if isinstance(t, dict): + kind = t.get("type") or "function" + fn = t.get("function") or {} + name = fn.get("name") if isinstance(fn, dict) else None + else: + kind = safe_get_attr(t, "type") or "function" + fn = safe_get_attr(t, "function") + name = safe_get_attr(fn, "name") + if not name: + continue + item: dict[str, Any] = {"type": safe_str(kind), "name": safe_str(name)} + if isinstance(fn, dict): + desc = fn.get("description") + params = fn.get("parameters") + else: + desc = safe_get_attr(fn, "description") + params = safe_get_attr(fn, "parameters") + if desc: + item["description"] = safe_str(desc) + if params: + item["parameters"] = params + defs_summary.append(item) + if defs_summary: + agent_span.set_attribute( + GEN_AI_TOOL_DEFINITIONS, to_json_str(defs_summary) + ) + except Exception: + pass + + # Best-effort INPUT + system_instructions capture on AGENT at open + # time. ``_capture_agent_io_attributes`` will run again at close to + # overwrite these with the *final* state, but having them now means + # an in-flight read of the AGENT span (e.g. live dashboards) sees + # at least the system prompt + initial user message. + try: + state = safe_get_attr(controller, "state") + _capture_agent_io_attributes(agent_span, controller, agent, state) + except Exception as exc: + logger.debug( + "OpenHands instrumentation: AGENT initial I/O capture: %s", exc + ) + + agent_ctx = set_span_in_context(agent_span) + if sid: + # Stash ctx-with-AGENT so STEP / TOOL re-attach correctly even + # when fired from worker threads with brand-new asyncio loops. + # The downstream consumers (STEP / TOOL / LLM bridge) all do + # their own paired attach/detach, so it's safe to share this + # ``Context`` object across asyncio tasks and threads. + store_context(sid, agent_ctx) + + # ----- WARMUP STEP (round 1) ----- + # Open right after AGENT so any pre-_step actions (RECALL, etc.) that + # the controller dispatches to the runtime become children of STEP 1 + # rather than dangling siblings under AGENT. The first real ``_step`` + # call detects this open STEP isn't yet "consumed" and reuses it + # (preserving the round number) so the LLM call + first LLM-driven + # tool also nest under STEP 1 — giving the trace tree: + # + # ENTRY > AGENT > STEP 1 > [RECALL, LLM, execute_bash] + # STEP 2 > [LLM, finish] + # ... + warmup_step_ctx: object | None = None + warmup_step_span: trace_api.Span | None = None + try: + warmup_step_span = tracer.start_span( + "react step", + kind=SpanKind.INTERNAL, + context=agent_ctx, + ) + _set_common(warmup_step_span, "STEP") + warmup_step_span.set_attribute(GenAI.GEN_AI_OPERATION_NAME, "react") + warmup_step_span.set_attribute(OH_REACT_ROUND, 1) + warmup_step_span.set_attribute( + GenAI.GEN_AI_AGENT_NAME, safe_str(agent_name) + ) + if sid: + warmup_step_span.set_attribute(GEN_AI_SESSION_ID, sid) + warmup_step_span.set_attribute(GEN_AI_CONVERSATION_ID, sid) + warmup_step_span.set_attribute(GEN_AI_AGENT_ID, sid) + warmup_step_ctx = set_span_in_context(warmup_step_span) + if sid and warmup_step_ctx is not None: + store_context(sid, warmup_step_ctx) + except Exception as exc: + logger.debug("Failed to open warmup STEP span: %s", exc) + warmup_step_span = None + + # Stash everything we need to tear down in close(). + try: + setattr(controller, _OWNS_FLAG, True) + setattr(controller, _ENTRY_SPAN_ATTR, entry) + setattr(controller, _AGENT_SPAN_ATTR, agent_span) + # Save the AGENT context so the STEP wrapper can restore the + # session stash to AGENT every time it closes a STEP — that way + # any TOOL fired between rounds re-attaches AGENT (not a closed + # STEP). + setattr(controller, _AGENT_CTX_ATTR, agent_ctx) + # Stash warmup STEP so the first real ``_step`` reuses it. + setattr(controller, _STEP_SPAN_ATTR, warmup_step_span) + setattr(controller, "_otel_oh_round", 1 if warmup_step_span is not None else 0) + setattr(controller, "_otel_oh_step_consumed", False) + except Exception: + # If we can't attach to the instance (slots, etc.), close the + # spans down so we don't leak them. + if warmup_step_span is not None: + try: + warmup_step_span.end() + except Exception: + pass + try: + agent_span.end() + except Exception: + pass + if entry is not None: + try: + entry.end() + except Exception: + pass + return + + # Log at INFO so the user can verify in their app logs that the + # ENTRY+AGENT spans were actually opened (and which trace/span IDs + # they got). When a user reports "no ENTRY span" in their backend, + # the first thing to check is whether this log line appeared. + try: + entry_sc = entry.get_span_context() if entry is not None else None + agent_sc = agent_span.get_span_context() + warmup_sc = ( + warmup_step_span.get_span_context() + if warmup_step_span is not None + else None + ) + logger.info( + "OpenHands instrumentation: opened ENTRY+AGENT for sid=%r " + "(trace_id=%032x entry_span=%016x agent_span=%016x " + "warmup_step=%s agent_name=%s model=%s)", + sid, + entry_sc.trace_id if entry_sc is not None else agent_sc.trace_id, + entry_sc.span_id if entry_sc is not None else 0, + agent_sc.span_id, + f"{warmup_sc.span_id:016x}" if warmup_sc is not None else "none", + agent_name, + model or "", + ) + except Exception: + pass + + +def _close_entry_and_agent_for_controller( + controller: Any, *, error: BaseException | None = None +) -> None: + """Tear down the ENTRY+AGENT spans previously opened for ``controller``. + + Also closes any STEP span left open from the last ``_step`` invocation + (STEP spans are intentionally persisted across the return of ``_step`` + so that thread-pooled TOOL / LLM calls fire as their children). + """ + if not getattr(controller, _OWNS_FLAG, False): + logger.debug( + "OpenHands instrumentation: close called on controller %s " + "without an open ENTRY/AGENT — nothing to do", + id(controller), + ) + return + sid = safe_str(safe_get_attr(controller, "id") or "") + agent = safe_get_attr(controller, "agent") + state = safe_get_attr(controller, "state") + entry_span: trace_api.Span | None = getattr(controller, _ENTRY_SPAN_ATTR, None) + agent_span: trace_api.Span | None = getattr(controller, _AGENT_SPAN_ATTR, None) + # Legacy slots — kept for back-compat with already-instrumented + # instances created before we stopped persisting attach-tokens. + # If they're set we simply ignore them (any detach attempt across + # asyncio task boundaries would raise ``ValueError`` in the Aliyun + # SDK; spans alone carry all the parentage info we need). + _ = getattr(controller, _AGENT_TOKEN_ATTR, None) + _ = getattr(controller, _ENTRY_TOKEN_ATTR, None) + + # Close any STEP span still hanging from the last round before tearing + # down AGENT/ENTRY. Restores the session stash to AGENT context so any + # in-flight TOOL re-attaches AGENT (not a closed STEP). + try: + _close_open_step(controller) + except Exception: + pass + + # Capture I/O attributes on the AGENT span before ending it. + if agent_span is not None: + try: + _capture_agent_io_attributes(agent_span, controller, agent, state) + except Exception: + pass + try: + history = safe_get_attr(state, "history") or [] + if isinstance(history, list): + agent_span.set_attribute(OH_HISTORY_LENGTH, len(history)) + agent_state = safe_get_attr(state, "agent_state") + if agent_state is not None: + agent_span.set_attribute( + OH_AGENT_STATE, + safe_get_attr(agent_state, "value") or safe_str(agent_state), + ) + except Exception: + pass + if error is not None: + try: + agent_span.record_exception(error) + agent_span.set_status( + Status(StatusCode.ERROR, type(error).__qualname__) + ) + except Exception: + pass + + # End AGENT (no detach — the token (if any) was attached in the + # ``__init__`` task's contextvars context and detaching here would + # cross a context boundary, raising ``ValueError`` in the Aliyun + # SDK. Legacy code may have set ``agent_token`` on older instances; + # we simply leave it alone — detaching is unnecessary because the + # span carries its own parentage and contextvars naturally unwind + # when the task that attached them exits). + if agent_span is not None: + try: + agent_span.end() + except Exception: + pass + + # Mirror the most-useful bits onto ENTRY before closing it. + if entry_span is not None: + try: + agent_state = safe_get_attr(state, "agent_state") + if agent_state is not None: + entry_span.set_attribute( + OH_AGENT_STATE, + safe_get_attr(agent_state, "value") or safe_str(agent_state), + ) + history = safe_get_attr(state, "history") or [] + if isinstance(history, list): + entry_span.set_attribute(OH_HISTORY_LENGTH, len(history)) + output_repr = _final_state_to_output(state) + entry_input_messages, entry_output_messages = _entry_io_from_state( + state + ) + if output_repr: + _set_io( + entry_span, + output_value=output_repr, + input_messages=entry_input_messages, + output_messages=entry_output_messages, + ) + elif entry_input_messages or entry_output_messages: + _set_io( + entry_span, + input_messages=entry_input_messages, + output_messages=entry_output_messages, + ) + except Exception: + pass + if error is not None: + try: + entry_span.record_exception(error) + entry_span.set_status( + Status(StatusCode.ERROR, type(error).__qualname__) + ) + except Exception: + pass + + # Same as AGENT: end the span; never touch a possibly-leftover token + # from an older instrumentation run. + if entry_span is not None: + try: + entry_span.end() + except Exception: + pass + + # Mirror the open-time INFO log so the user can confirm the spans + # actually closed and exported. + try: + agent_sc = ( + agent_span.get_span_context() if agent_span is not None else None + ) + entry_sc = ( + entry_span.get_span_context() if entry_span is not None else None + ) + logger.info( + "OpenHands instrumentation: closed ENTRY+AGENT for sid=%r " + "(entry_span=%s agent_span=%s rounds=%s error=%s)", + sid, + f"{entry_sc.span_id:016x}" if entry_sc is not None else "none", + f"{agent_sc.span_id:016x}" if agent_sc is not None else "none", + getattr(controller, "_otel_oh_round", 0), + type(error).__qualname__ if error is not None else "none", + ) + except Exception: + pass + + if sid: + try: + clear_context(sid) + except Exception: + pass + + # Wipe stash slots so a re-used controller instance doesn't double-emit. + for attr in ( + _OWNS_FLAG, + _ENTRY_SPAN_ATTR, + _AGENT_SPAN_ATTR, + _ENTRY_TOKEN_ATTR, + _AGENT_TOKEN_ATTR, + _STEP_SPAN_ATTR, + _AGENT_CTX_ATTR, + "_otel_oh_step_consumed", + "_otel_oh_round", + ): + try: + setattr(controller, attr, None) + except Exception: + pass + try: + setattr(controller, _OWNS_FLAG, False) + except Exception: + pass + + +class AgentControllerInitWrapper: + """Open ENTRY + AGENT spans at the end of ``AgentController.__init__``. + + Always reliable under ``python -m openhands.core.main`` because it + hooks a class method (immune to from-import binding). + """ + + __slots__ = ("_tracer",) + + def __init__(self, tracer: Tracer): + self._tracer = tracer + + def __call__(self, wrapped, instance, args, kwargs): + try: + result = wrapped(*args, **kwargs) + except BaseException: + raise + try: + # Skip delegate sub-controllers — they shouldn't open another + # ENTRY span; they live within the parent controller's trace. + is_delegate = bool(safe_get_attr(instance, "is_delegate")) + if is_delegate: + logger.debug( + "OpenHands instrumentation: skipping delegate " + "controller %s for ENTRY/AGENT", + id(instance), + ) + else: + _open_entry_and_agent_for_controller(self._tracer, instance) + except Exception as exc: + # Promote to ERROR — if this fails the user will see "no + # ENTRY span" in their backend and we want a loud signal in + # the app logs to point at the cause. + logger.error( + "OpenHands instrumentation: AgentController init wrapper " + "failed to open ENTRY/AGENT for controller %s: %s", + id(instance), + exc, + exc_info=True, + ) + return result + + +class AgentControllerCloseWrapper: + """End the ENTRY + AGENT spans previously opened in ``__init__``.""" + + __slots__ = () + + def __init__(self, _tracer: Tracer): + # Tracer arg unused (we only need the spans we previously opened) + # but kept for symmetry with the other factories. + pass + + def __call__(self, wrapped, instance, args, kwargs): + return self._impl(wrapped, instance, args, kwargs) + + async def _impl(self, wrapped, instance, args, kwargs): + err: BaseException | None = None + try: + return await wrapped(*args, **kwargs) + except BaseException as exc: + err = exc + raise + finally: + try: + _close_entry_and_agent_for_controller(instance, error=err) + except Exception as exc: + logger.error( + "OpenHands instrumentation: AgentController close " + "wrapper failed to end spans for controller %s: %s", + id(instance), + exc, + exc_info=True, + ) + + +# --------------------------------------------------------------------------- +# LLM context bridge: openhands.llm.llm.LLM.__init__ +# --------------------------------------------------------------------------- + + +# Sentinel used to mark already-bridged completion callables so we don't +# wrap them more than once if ``LLM.__init__`` runs again on the same +# completion partial (e.g. live config reload). +_LLM_BRIDGE_FLAG = "_otel_oh_ctx_bridged" + + +class LLMInitWrapper: + """Make sure ``LLM.completion`` runs with the current STEP context attached. + + Why this exists + --------------- + The LLM call inside ``AgentController._step`` is synchronous and *should* + inherit our STEP context via ``contextvars`` — but in real OpenHands + deployments LiteLLM ends up creating its span with a *different* + ``trace_id`` than the surrounding STEP/AGENT/ENTRY tree. Two known ways + that can happen: + + * a 3rd-party auto-instrumentation injected before ours stashes the + LLM call onto a thread-pool worker (no contextvars propagation); + * the call is made from outside any of our wrappers (e.g. a condenser + / summarizer worker) where no OTel context is current. + + The fix: at the end of ``LLM.__init__`` we monkey-patch ``self._completion`` + with a tiny shim that re-attaches the latest sid-stashed context (which, + while a STEP is open, is the STEP context — see ``AgentControllerStepWrapper``). + The downstream ``opentelemetry-instrumentation-litellm`` (or the Aliyun + GenAI auto-instrumentation) will then create the LLM span as a child + of STEP and the ``trace_id`` finally lines up. + """ + + __slots__ = ("_tracer",) + + def __init__(self, tracer: Tracer): + # Tracer arg unused — we only re-attach an existing OTel context + # so the *real* LLM instrumentor (litellm / aliyun) emits the + # span under it. We don't create our own LLM span here. + self._tracer = tracer + + def __call__(self, wrapped, instance, args, kwargs): + result = wrapped(*args, **kwargs) + try: + self._patch_completion(instance) + except Exception as exc: + logger.debug("LLM init wrapper failed to bridge completion: %s", exc) + return result + + @staticmethod + def _patch_completion(instance: Any) -> None: + completion = getattr(instance, "_completion", None) + if completion is None: + return + if getattr(completion, _LLM_BRIDGE_FLAG, False): + return + + def bridged(*a: Any, **kw: Any) -> Any: + # ``AttachedSession(None)`` re-attaches whatever context the + # most recent v0 wrapper stashed (STEP if a step is open, + # AGENT otherwise). When no OpenHands session is active the + # context manager is a no-op. + with AttachedSession(None): + return completion(*a, **kw) + + try: + setattr(bridged, _LLM_BRIDGE_FLAG, True) + except Exception: + pass + try: + instance._completion = bridged + except Exception: + return + # Mirror onto the unwrapped slot too — some OpenHands codepaths + # call ``_completion_unwrapped`` directly when retries are + # disabled, and we want them to inherit the same parent context. + unwrapped = getattr(instance, "_completion_unwrapped", None) + if unwrapped is not None and not getattr(unwrapped, _LLM_BRIDGE_FLAG, False): + + def bridged_unwrapped(*a: Any, **kw: Any) -> Any: + with AttachedSession(None): + return unwrapped(*a, **kw) + + try: + setattr(bridged_unwrapped, _LLM_BRIDGE_FLAG, True) + except Exception: + pass + try: + instance._completion_unwrapped = bridged_unwrapped + except Exception: + pass diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/package.py b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/package.py new file mode 100644 index 000000000..6e3b6b925 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/package.py @@ -0,0 +1 @@ +_instruments = ("openhands-ai >= 1.0.0",) diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/version.py b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/version.py new file mode 100644 index 000000000..3dc1f76bc --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/src/opentelemetry/instrumentation/openhands/version.py @@ -0,0 +1 @@ +__version__ = "0.1.0" diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-openhands/test-requirements.txt b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/test-requirements.txt new file mode 100644 index 000000000..b5c521bd2 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/test-requirements.txt @@ -0,0 +1,9 @@ +pytest>=7.0.0 +pytest-asyncio>=0.21.0 +wrapt>=1.0.0 +httpx>=0.24.0 + +-e ./instrumentation-loongsuite/loongsuite-instrumentation-openhands +-e ./opentelemetry-instrumentation +-e ./opentelemetry-sdk +-e ./opentelemetry-semantic-conventions diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-openhands/tests/__init__.py b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-openhands/tests/conftest.py b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/tests/conftest.py new file mode 100644 index 000000000..685e33b35 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/tests/conftest.py @@ -0,0 +1,244 @@ +"""Shared pytest fixtures and stub modules for the OpenHands instrumentation. + +We deliberately don't require ``openhands-ai`` to be installed at test time: +instead we register lightweight stub modules under the same dotted paths so +``wrap_function_wrapper`` can patch them. The wrappers themselves only rely on +the *call signatures* documented in ``execute.md`` — which we faithfully +reproduce in the stubs. +""" + +from __future__ import annotations + +import sys +import types +from dataclasses import dataclass, field + +import pytest +from opentelemetry import trace as trace_api +from opentelemetry.sdk.trace import TracerProvider +from opentelemetry.sdk.trace.export import SimpleSpanProcessor +from opentelemetry.sdk.trace.export.in_memory_span_exporter import ( + InMemorySpanExporter, +) + + +def _ensure_stub_module(name: str) -> types.ModuleType: + if name in sys.modules: + return sys.modules[name] + mod = types.ModuleType(name) + sys.modules[name] = mod + parent_name, _, leaf = name.rpartition(".") + if parent_name: + parent = _ensure_stub_module(parent_name) + setattr(parent, leaf, mod) + return mod + + +def _install_v0_stub_modules() -> None: + """Stubs for the V0 (Legacy CodeAct) hook points.""" + _ensure_stub_module("openhands") + core = _ensure_stub_module("openhands.core") + main_mod = _ensure_stub_module("openhands.core.main") + loop_mod = _ensure_stub_module("openhands.core.loop") + ctrl_pkg = _ensure_stub_module("openhands.controller") + ctrl_mod = _ensure_stub_module("openhands.controller.agent_controller") + rt_pkg = _ensure_stub_module("openhands.runtime") + rt_base = _ensure_stub_module("openhands.runtime.base") + + @dataclass + class _AgentState: + value: str = "finished" + + @dataclass + class _State: + agent_state: _AgentState = field(default_factory=_AgentState) + + @dataclass + class _LLMConfig: + model: str = "qwen3-coder-plus" + + @dataclass + class _LLM: + config: _LLMConfig = field(default_factory=_LLMConfig) + + @dataclass + class _Agent: + name: str = "CodeActAgent" + llm: _LLM = field(default_factory=_LLM) + # Mirrors litellm ChatCompletionToolParam dicts as produced by + # openhands.agenthub.codeact_agent.codeact_agent.CodeActAgent._get_tools. + tools: list = field( + default_factory=lambda: [ + { + "type": "function", + "function": { + "name": "execute_bash", + "description": "Run a bash command on the runtime sandbox.", + "parameters": { + "type": "object", + "properties": { + "command": {"type": "string"}, + }, + "required": ["command"], + }, + }, + }, + ] + ) + + class AgentController: + step_calls = 0 + close_calls = 0 + + def __init__(self, agent=None, sid="sid-test"): + self.agent = agent or _Agent() + self.id = sid + self.state = _State() + self._pending_action = None + self.is_delegate = False + + async def _step(self) -> None: + type(self).step_calls += 1 + class _Pending: + action = "run" + command = "echo step" + thought = "trying" + + self._pending_action = _Pending() + + async def close(self, set_stop_state: bool = True) -> None: + type(self).close_calls += 1 + + ctrl_mod.AgentController = AgentController + + class _ToolCallMetadata: + """Stand-in for :class:`openhands.events.tool.ToolCallMetadata`.""" + + def __init__(self, function_name="", tool_call_id="", arguments=None): + import json as _json + + self.function_name = function_name + self.tool_call_id = tool_call_id + + class _Fn: + def __init__(self, name, args): + self.name = name + self.arguments = _json.dumps(args or {}) + + class _TC: + def __init__(self, tcid, fn): + self.id = tcid + self.function = fn + + class _Msg: + def __init__(self, tcs): + self.tool_calls = tcs + + class _Choice: + def __init__(self, msg): + self.message = msg + + class _ModelResp: + def __init__(self, choices): + self.choices = choices + + self.model_response = _ModelResp( + [_Choice(_Msg([_TC(tool_call_id, _Fn(function_name, arguments))]))] + ) + + class _Action: + def __init__( + self, + action_type="run", + command="echo hi", + tool_call_metadata=None, + ): + self.action = action_type + self.command = command + self.tool_call_metadata = tool_call_metadata + + class _Observation: + def __init__(self, exit_code=0, content=""): + self.exit_code = exit_code + self.content = content + self.observation = "run" + + class Runtime: + run_action_calls = 0 + # Tests can override on the instance to drive observation values. + _next_observation: _Observation | None = None + + def __init__(self, sid="sid-test"): + self.sid = sid + + def run_action(self, action) -> _Observation: + type(self).run_action_calls += 1 + obs = self._next_observation + if obs is not None: + self._next_observation = None + return obs + return _Observation(exit_code=0) + + rt_base.Runtime = Runtime + rt_base.Action = _Action + rt_base.Observation = _Observation + rt_base.ToolCallMetadata = _ToolCallMetadata + + @dataclass + class _State2: + agent_state: _AgentState = field(default_factory=lambda: _AgentState("finished")) + + async def run_controller( + config=None, + initial_user_action=None, + sid: str | None = None, + **kwargs, + ): + # Mirror real V0: invoke the agent loop *inside* run_controller so + # the AGENT span lives within the ENTRY span (and inherits its + # stashed OTel context). Tests can install + # ``main_mod._test_inner_args = (controller, runtime)`` to opt in. + inner_args = getattr(main_mod, "_test_inner_args", None) + if inner_args is not None: + controller, runtime = inner_args + await loop_mod.run_agent_until_done(controller, runtime, None, []) + return _State2() + + main_mod.run_controller = run_controller + + async def run_agent_until_done(controller, runtime, memory, end_states): + # Tests can install a custom inner callback to drive STEP / TOOL + # spans inside the AGENT span; default is a no-op. + cb = getattr(loop_mod, "_test_inner_callback", None) + if callable(cb): + await cb(controller, runtime) + return None + + loop_mod.run_agent_until_done = run_agent_until_done + + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- + + +@pytest.fixture +def tracer_provider() -> TracerProvider: + provider = TracerProvider() + exporter = InMemorySpanExporter() + provider.add_span_processor(SimpleSpanProcessor(exporter)) + provider._exporter = exporter # type: ignore[attr-defined] + return provider + + +@pytest.fixture +def stub_openhands_v0_modules() -> None: + _install_v0_stub_modules() + + +@pytest.fixture(autouse=True) +def _reset_global_tracer(): + """Avoid bleed-through of the SDK provider between tests.""" + yield + trace_api._TRACER_PROVIDER = None # type: ignore[attr-defined] + diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-openhands/tests/test_v0_tool_attributes.py b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/tests/test_v0_tool_attributes.py new file mode 100644 index 000000000..d94ce16bc --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/tests/test_v0_tool_attributes.py @@ -0,0 +1,201 @@ +"""ARMS GenAI semconv §Tool conformance tests for the V0 TOOL wrapper. + +I/O capture is always on (no env-var gating, no truncation), so the +TOOL span must carry every attribute the spec calls out — both +required and recommended — on every run. +""" + +from __future__ import annotations + +import asyncio +import json + +import pytest + + +def _spans_by_kind(exporter, kind: str): + return [ + s + for s in exporter.get_finished_spans() + if s.attributes.get("gen_ai.span.kind") == kind + ] + + +@pytest.fixture +def instrumented(tracer_provider, stub_openhands_v0_modules): + from opentelemetry.instrumentation.openhands import OpenHandsInstrumentor + from opentelemetry.instrumentation.openhands.internal import session_context + + session_context.clear_all() + inst = OpenHandsInstrumentor() + inst.instrument(tracer_provider=tracer_provider, skip_dep_check=True) + try: + yield inst, tracer_provider._exporter # type: ignore[attr-defined] + finally: + try: + inst.uninstrument() + except Exception: + pass + session_context.clear_all() + + +def _run_one_tool_call(rt_base, ctrl_mod, loop_mod, main_mod): + """Drive a single ENTRY → AGENT → STEP → TOOL flow.""" + ctrl = ctrl_mod.AgentController(sid="tool-sid") + runtime = rt_base.Runtime(sid="tool-sid") + + tcm = rt_base.ToolCallMetadata( + function_name="execute_bash", + tool_call_id="call_abc123", + arguments={"command": "ls /tmp", "thought": "list temp"}, + ) + action = rt_base.Action( + action_type="run", + command="ls /tmp", + tool_call_metadata=tcm, + ) + + class MessageAction: + content = "list /tmp" + source = "user" + + async def _inner(_c, _r): + await ctrl._step() + runtime.run_action(action) + + loop_mod._test_inner_callback = _inner + main_mod._test_inner_args = (ctrl, runtime) + + async def _scenario(): + await main_mod.run_controller( + config=None, + initial_user_action=MessageAction(), + sid="tool-sid", + ) + + try: + asyncio.run(_scenario()) + finally: + loop_mod._test_inner_callback = None + main_mod._test_inner_args = None + + +def test_tool_span_carries_all_arms_required_attributes(instrumented): + inst, exporter = instrumented + + import openhands.controller.agent_controller as ctrl_mod + import openhands.core.loop as loop_mod + import openhands.core.main as main_mod + import openhands.runtime.base as rt_base + + _run_one_tool_call(rt_base, ctrl_mod, loop_mod, main_mod) + + tools = _spans_by_kind(exporter, "TOOL") + assert len(tools) == 1 + tool = tools[0] + attrs = tool.attributes + + # Required + assert attrs["gen_ai.span.kind"] == "TOOL" + assert attrs["gen_ai.operation.name"] == "execute_tool" + + # Span name should be `execute_tool {tool_name}` + assert tool.name == "execute_tool execute_bash" + + # Recommended attributes + assert attrs["gen_ai.tool.name"] == "execute_bash" + assert attrs["gen_ai.tool.type"] == "function" + assert attrs["gen_ai.tool.call.id"] == "call_abc123" + assert attrs.get("gen_ai.tool.description") == ( + "Run a bash command on the runtime sandbox." + ) + + # Arguments should be the BARE JSON dict, not the wrapping + # {"tool": ..., "arguments": ...} envelope. + args_json = attrs.get("gen_ai.tool.call.arguments") + assert args_json is not None + args = json.loads(args_json) + assert args == {"command": "ls /tmp", "thought": "list temp"} + + # Result should reflect the observation. + result_json = attrs.get("gen_ai.tool.call.result") + assert result_json is not None + result = json.loads(result_json) + assert result.get("exit_code") == 0 + assert "observation" in result + + +def test_tool_span_falls_back_to_action_field_when_no_tool_call_metadata( + instrumented, +): + """If the action wasn't generated from an LLM tool call (e.g. a + user-initiated agent.action), the wrapper should still produce a + sensible ``gen_ai.tool.name`` derived from the action type.""" + inst, exporter = instrumented + + import openhands.controller.agent_controller as ctrl_mod + import openhands.core.loop as loop_mod + import openhands.core.main as main_mod + import openhands.runtime.base as rt_base + + ctrl = ctrl_mod.AgentController(sid="tool-fallback-sid") + runtime = rt_base.Runtime(sid="tool-fallback-sid") + action = rt_base.Action(action_type="run", command="echo hi") + + class MessageAction: + content = "say hi" + source = "user" + + async def _inner(_c, _r): + await ctrl._step() + runtime.run_action(action) + + loop_mod._test_inner_callback = _inner + main_mod._test_inner_args = (ctrl, runtime) + + async def _scenario(): + await main_mod.run_controller( + config=None, + initial_user_action=MessageAction(), + sid="tool-fallback-sid", + ) + + try: + asyncio.run(_scenario()) + finally: + loop_mod._test_inner_callback = None + main_mod._test_inner_args = None + + tool = _spans_by_kind(exporter, "TOOL")[0] + attrs = tool.attributes + + # Action.action == "run" → tool name "bash" + assert attrs["gen_ai.tool.name"] == "bash" + assert tool.name == "execute_tool bash" + # No tool-call id when the action wasn't from an LLM call + assert attrs.get("gen_ai.tool.call.id", "") == "" + # Arguments still produced from the action's fields + args = json.loads(attrs["gen_ai.tool.call.arguments"]) + assert args.get("command") == "echo hi" + + +def test_agent_span_emits_tool_definitions(instrumented): + """AGENT span should advertise the agent's available tools per the + ARMS GenAI semconv §Agent → ``gen_ai.tool.definitions``.""" + inst, exporter = instrumented + + import openhands.controller.agent_controller as ctrl_mod + import openhands.core.loop as loop_mod + import openhands.core.main as main_mod + import openhands.runtime.base as rt_base + + _run_one_tool_call(rt_base, ctrl_mod, loop_mod, main_mod) + + agent = _spans_by_kind(exporter, "AGENT")[0] + defs_json = agent.attributes.get("gen_ai.tool.definitions") + assert defs_json, "AGENT span should set gen_ai.tool.definitions" + defs = json.loads(defs_json) + assert isinstance(defs, list) and defs + assert defs[0]["type"] == "function" + assert defs[0]["name"] == "execute_bash" + assert "description" in defs[0] diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-openhands/tests/test_v0_trace_continuity.py b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/tests/test_v0_trace_continuity.py new file mode 100644 index 000000000..9025f9991 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/tests/test_v0_trace_continuity.py @@ -0,0 +1,246 @@ +"""Cross-thread / cross-loop trace continuity tests for V0 wrappers. + +These tests model the *real* OpenHands V0 runtime behaviour: events are +delivered by ``EventStream`` via a ``ThreadPoolExecutor`` and the controller +processes them with ``asyncio.get_event_loop().run_until_complete(...)`` — +which spins a brand-new asyncio loop in the worker thread. Without our +session-context bridge, STEP / TOOL spans would start fresh root traces. + +We assert: + +* All ENTRY / AGENT / STEP / TOOL spans share the **same** ``trace_id``. +* Parent-child wiring is correct (STEP is parented under AGENT, TOOL too). +* The session-context store is cleaned up after the entry returns. +* GenAI semantic-convention I/O attributes are populated when content + capture is enabled. +""" + +from __future__ import annotations + +import asyncio +import os +import threading +from concurrent.futures import ThreadPoolExecutor + +import pytest + + +def _spans_by_kind_attr(exporter, kind: str): + return [ + s + for s in exporter.get_finished_spans() + if s.attributes.get("gen_ai.span.kind") == kind + ] + + +@pytest.fixture +def instrumented_v0(tracer_provider, stub_openhands_v0_modules): + from opentelemetry.instrumentation.openhands import OpenHandsInstrumentor + from opentelemetry.instrumentation.openhands.internal import session_context + + session_context.clear_all() + inst = OpenHandsInstrumentor() + inst.instrument(tracer_provider=tracer_provider, skip_dep_check=True) + try: + yield inst, tracer_provider._exporter # type: ignore[attr-defined] + finally: + try: + inst.uninstrument() + except Exception: + pass + session_context.clear_all() + + +def _drive_step_in_worker_thread(controller, runtime, action) -> None: + """Reproduce the V0 EventStream → ThreadPoolExecutor → run_until_complete path. + + The worker thread (a) has no shared asyncio loop with the caller and + (b) has a *fresh* ``contextvars.Context`` (Python copies the snapshot + at submit-time, but the snapshot is from this test thread — the same + fresh context the real EventStream queue thread would have). + """ + barrier = threading.Event() + err: list[BaseException] = [] + + def _worker(): + try: + # New event loop per worker — exactly what V0 does. + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + try: + loop.run_until_complete(controller._step()) + # Run_action is sync — call it directly inside the worker. + runtime.run_action(action) + finally: + loop.close() + except BaseException as exc: # pragma: no cover - surfaced via err + err.append(exc) + finally: + barrier.set() + + pool = ThreadPoolExecutor(max_workers=1) + fut = pool.submit(_worker) + fut.result(timeout=5) + pool.shutdown(wait=True) + barrier.wait(timeout=5) + if err: + raise err[0] + + +def test_all_spans_share_one_trace_id_across_threads(instrumented_v0): + """The whole V0 trace must collapse onto a single trace_id even when + STEP / TOOL run in fresh worker threads with fresh asyncio loops.""" + inst, exporter = instrumented_v0 + + import openhands.controller.agent_controller as ctrl_mod + import openhands.core.loop as loop_mod + import openhands.core.main as main_mod + import openhands.runtime.base as rt_base + + ctrl = ctrl_mod.AgentController(sid="bench-001") + runtime = rt_base.Runtime(sid="bench-001") + action = rt_base.Action(action_type="run", command="ls /") + + async def _inner(_controller, _runtime): + for _ in range(2): + _drive_step_in_worker_thread(ctrl, runtime, action) + + loop_mod._test_inner_callback = _inner + main_mod._test_inner_args = (ctrl, runtime) + + class MessageAction: + content = "say hi" + source = "user" + + async def _scenario(): + await main_mod.run_controller( + config=None, + initial_user_action=MessageAction(), + sid="bench-001", + ) + + try: + asyncio.run(_scenario()) + finally: + loop_mod._test_inner_callback = None + main_mod._test_inner_args = None + + spans = exporter.get_finished_spans() + by_kind = {kind: _spans_by_kind_attr(exporter, kind) for kind in ("ENTRY", "AGENT", "STEP", "TOOL")} + + assert len(by_kind["ENTRY"]) == 1 + assert len(by_kind["AGENT"]) == 1 + assert len(by_kind["STEP"]) == 2 + assert len(by_kind["TOOL"]) == 2 + + entry = by_kind["ENTRY"][0] + agent = by_kind["AGENT"][0] + trace_id = entry.context.trace_id + + # Same trace_id for every span + for s in spans: + assert s.context.trace_id == trace_id, ( + f"span {s.name!r} (kind={s.attributes.get('gen_ai.span.kind')}) " + f"has trace_id {s.context.trace_id} but expected {trace_id}" + ) + + # Parent-child links: AGENT under ENTRY, STEP under AGENT, TOOL under AGENT + assert agent.parent is not None and agent.parent.span_id == entry.context.span_id + for s in by_kind["STEP"]: + assert s.parent is not None and s.parent.span_id == agent.context.span_id + for t in by_kind["TOOL"]: + assert t.parent is not None and t.parent.span_id == agent.context.span_id + + +def test_session_context_cleared_after_entry(instrumented_v0): + """The per-sid stash must not leak across runs.""" + inst, exporter = instrumented_v0 + + import openhands.core.loop as loop_mod + import openhands.core.main as main_mod + from opentelemetry.instrumentation.openhands.internal import session_context + + async def _scenario(): + await main_mod.run_controller( + config=None, + initial_user_action=type("Msg", (), {"content": "x", "source": "user"})(), + sid="ephemeral-sid", + ) + + asyncio.run(_scenario()) + assert session_context.get_context("ephemeral-sid") is None + + +def test_io_attributes_on_entry_agent_step(instrumented_v0): + """Verify GenAI / OpenInference I/O attributes are populated.""" + inst, exporter = instrumented_v0 + + import openhands.controller.agent_controller as ctrl_mod + import openhands.core.loop as loop_mod + import openhands.core.main as main_mod + import openhands.runtime.base as rt_base + + ctrl = ctrl_mod.AgentController(sid="io-sid") + runtime = rt_base.Runtime(sid="io-sid") + action = rt_base.Action(action_type="run", command="cat /etc/hosts") + + # Seed history with a *MessageAction*-named instance — that's the type + # name the AGENT wrapper looks for when computing input.messages. + class MessageAction: + content = "do the thing" + source = "user" + + ctrl.state.history = [MessageAction()] + + async def _inner(_c, _r): + await ctrl._step() + runtime.run_action(action) + + loop_mod._test_inner_callback = _inner + main_mod._test_inner_args = (ctrl, runtime) + + async def _scenario(): + await main_mod.run_controller( + config=None, + initial_user_action=MessageAction(), + sid="io-sid", + ) + + try: + asyncio.run(_scenario()) + finally: + loop_mod._test_inner_callback = None + main_mod._test_inner_args = None + + entry = _spans_by_kind_attr(exporter, "ENTRY")[0] + agent = _spans_by_kind_attr(exporter, "AGENT")[0] + step = _spans_by_kind_attr(exporter, "STEP")[0] + tool = _spans_by_kind_attr(exporter, "TOOL")[0] + + # ENTRY + assert entry.attributes.get("gen_ai.framework") == "openhands" + assert entry.attributes.get("gen_ai.system") == "openhands" + assert entry.attributes.get("gen_ai.session.id") == "io-sid" + assert entry.attributes.get("input.value") + assert "do the thing" in entry.attributes.get("input.value") + + # AGENT + assert agent.attributes.get("gen_ai.input.messages") + assert "do the thing" in agent.attributes.get("gen_ai.input.messages") + assert agent.attributes.get("input.value") + assert agent.attributes.get("gen_ai.session.id") == "io-sid" + + # STEP + assert step.attributes.get("input.value") + assert step.attributes.get("output.value") + assert step.attributes.get("gen_ai.output.messages") + assert step.attributes.get("openhands.action.type") == "run" + out = step.attributes.get("output.value") + assert "tool_calls" in out and "echo step" in out + + # TOOL + assert tool.attributes.get("gen_ai.tool.name") == "bash" + assert tool.attributes.get("input.value") + assert "cat /etc/hosts" in tool.attributes.get("input.value") + assert tool.attributes.get("output.value") + assert "exit_code" in tool.attributes.get("output.value") diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-openhands/tests/test_v0_wrappers.py b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/tests/test_v0_wrappers.py new file mode 100644 index 000000000..18dda9a55 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-openhands/tests/test_v0_wrappers.py @@ -0,0 +1,161 @@ +"""Tests for V0 (Legacy CodeAct) wrappers. + +We exercise the four V0 patches (``run_controller``, ``run_agent_until_done``, +``AgentController._step``, ``Runtime.run_action``) and assert that: + +* The ``ENTRY → AGENT → STEP → TOOL`` span tree is produced. +* Parent-child linkage is correct. +* Per-action ``gen_ai.tool.name`` is mapped from the V0 ``action`` field. +""" + +from __future__ import annotations + +import asyncio + +import pytest + + +def _spans_by_kind_attr(exporter, kind: str): + return [ + s + for s in exporter.get_finished_spans() + if s.attributes.get("gen_ai.span.kind") == kind + ] + + +@pytest.fixture +def instrumented_v0(tracer_provider, stub_openhands_v0_modules): + from opentelemetry.instrumentation.openhands import OpenHandsInstrumentor + + inst = OpenHandsInstrumentor() + inst.instrument(tracer_provider=tracer_provider, skip_dep_check=True) + try: + yield inst, tracer_provider._exporter # type: ignore[attr-defined] + finally: + try: + inst.uninstrument() + except Exception: + pass + + +def test_v0_full_span_tree(instrumented_v0): + inst, exporter = instrumented_v0 + + import openhands.controller.agent_controller as ctrl_mod + import openhands.core.loop as loop_mod + import openhands.core.main as main_mod + import openhands.runtime.base as rt_base + + ctrl = ctrl_mod.AgentController() + runtime = rt_base.Runtime() + action = rt_base.Action(action_type="run", command="ls /") + + async def _inner(controller, _runtime): + for _ in range(2): + await ctrl._step() + runtime.run_action(action) + + loop_mod._test_inner_callback = _inner + + async def _scenario(): + # ENTRY span via run_controller wrapper + await main_mod.run_controller( + config=None, + initial_user_action=type("Msg", (), {"content": "hello"})(), + sid="sid-test", + ) + # AGENT span via run_agent_until_done wrapper (which calls _inner) + await loop_mod.run_agent_until_done(ctrl, runtime, None, []) + + try: + asyncio.run(_scenario()) + finally: + loop_mod._test_inner_callback = None + + entry = _spans_by_kind_attr(exporter, "ENTRY") + agent = _spans_by_kind_attr(exporter, "AGENT") + step = _spans_by_kind_attr(exporter, "STEP") + tool = _spans_by_kind_attr(exporter, "TOOL") + + assert len(entry) == 1, f"unexpected ENTRY count: {len(entry)}" + assert len(agent) == 1, f"unexpected AGENT count: {len(agent)}" + assert len(step) == 2, f"unexpected STEP count: {len(step)}" + assert len(tool) == 2, f"unexpected TOOL count: {len(tool)}" + + e = entry[0] + a = agent[0] + assert e.name == "enter openhands" + assert e.attributes.get("gen_ai.framework") == "openhands" + assert e.attributes.get("gen_ai.session.id") == "sid-test" + + assert a.name.startswith("invoke_agent ") + assert a.attributes.get("gen_ai.agent.name") == "CodeActAgent" + assert a.attributes.get("gen_ai.request.model") == "qwen3-coder-plus" + + # All STEP spans share the AGENT as parent. + for s in step: + assert s.parent is not None + assert s.parent.span_id == a.context.span_id + assert s.attributes.get("gen_ai.operation.name") == "react" + assert s.attributes.get("gen_ai.react.round") in (1, 2) + + # TOOL spans are siblings of STEP under AGENT (run_action is called after + # _step returns and is no longer in STEP context). + for t in tool: + assert t.attributes.get("gen_ai.tool.name") == "bash" + assert t.attributes.get("openhands.action.type") == "run" + assert t.attributes.get("openhands.action.exit_code") == 0 + + +def test_v0_step_round_increments_per_controller(instrumented_v0): + inst, exporter = instrumented_v0 + import openhands.controller.agent_controller as ctrl_mod + + ctrl_a = ctrl_mod.AgentController(sid="A") + ctrl_b = ctrl_mod.AgentController(sid="B") + + async def _go(): + await ctrl_a._step() + await ctrl_a._step() + await ctrl_b._step() + + asyncio.run(_go()) + + step_spans = _spans_by_kind_attr(exporter, "STEP") + assert len(step_spans) == 3 + rounds_a = sorted( + s.attributes.get("gen_ai.react.round") + for s in step_spans + if s.attributes.get("gen_ai.session.id") == "A" + ) + rounds_b = sorted( + s.attributes.get("gen_ai.react.round") + for s in step_spans + if s.attributes.get("gen_ai.session.id") == "B" + ) + assert rounds_a == [1, 2] + assert rounds_b == [1] + + +def test_v0_runtime_error_observation_marks_span(instrumented_v0): + inst, exporter = instrumented_v0 + import openhands.runtime.base as rt_base + + runtime = rt_base.Runtime() + + class _ErrAction: + action = "run" + command = "false" + + # Use the conftest hook to make the next run_action return an error obs. + err_obs = rt_base.Observation(exit_code=2) + runtime._next_observation = err_obs + + runtime.run_action(_ErrAction()) + + tool_spans = _spans_by_kind_attr(exporter, "TOOL") + assert len(tool_spans) == 1 + span = tool_spans[0] + assert span.attributes.get("openhands.action.exit_code") == 2 + assert span.status.status_code.name == "ERROR" + diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-terminus2/LICENSE b/instrumentation-loongsuite/loongsuite-instrumentation-terminus2/LICENSE new file mode 100644 index 000000000..261eeb9e9 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-terminus2/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-terminus2/pyproject.toml b/instrumentation-loongsuite/loongsuite-instrumentation-terminus2/pyproject.toml new file mode 100644 index 000000000..62aaa6e5a --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-terminus2/pyproject.toml @@ -0,0 +1,52 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "loongsuite-instrumentation-terminus2" +dynamic = ["version"] +description = "LoongSuite Terminus2 Instrumentation" +license = "Apache-2.0" +requires-python = ">=3.8" +authors = [ + { name = "OpenTelemetry Authors", email = "cncf-opentelemetry-contributors@lists.cncf.io" }, +] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Developers", + "License :: OSI Approved :: Apache Software License", + "Programming Language :: Python", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", +] +dependencies = [ + "wrapt >= 1.0.0, < 2.0.0", +] + +[project.optional-dependencies] +instruments = [ + "terminal-bench >= 0.1.0", +] + +[project.entry-points.opentelemetry_instrumentor] +terminus2 = "opentelemetry.instrumentation.terminus2:Terminus2Instrumentor" + +[project.urls] +Homepage = "https://github.com/alibaba/loongsuite-python-agent/tree/main/instrumentation-loongsuite/loongsuite-instrumentation-terminus2" +Repository = "https://github.com/alibaba/loongsuite-python-agent" + +[tool.hatch.version] +path = "src/opentelemetry/instrumentation/terminus2/version.py" + +[tool.hatch.build.targets.sdist] +include = [ + "/src", + "/tests", +] + +[tool.hatch.build.targets.wheel] +packages = ["src/opentelemetry"] diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-terminus2/src/opentelemetry/instrumentation/terminus2/__init__.py b/instrumentation-loongsuite/loongsuite-instrumentation-terminus2/src/opentelemetry/instrumentation/terminus2/__init__.py new file mode 100644 index 000000000..026ba3c12 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-terminus2/src/opentelemetry/instrumentation/terminus2/__init__.py @@ -0,0 +1,802 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +OpenTelemetry Terminus2 Instrumentation + +Provides automatic instrumentation for the terminus-2 agent from terminal-bench +via external monkey patching (no upstream changes required). + +Span hierarchy & semantic mapping (strictly follows ARMS gen-ai semantic +conventions, see ``arms_docs/trace/gen-ai.md``): + + enter_ai_application_system (ENTRY / enter) + └── invoke_agent terminus-2 (AGENT / invoke_agent) + └── react step (STEP / react) ── episode N + ├── (LLM span produced by ``opentelemetry-instrumentation-litellm``) + ├── run_task parse_response (TASK / run_task) + ├── chain summarize (CHAIN / task) ── on overflow + └── execute_tool terminal (TOOL / execute_tool) + +LLM spans are intentionally **not** produced by this package. The underlying +``LiteLLM.call`` invokes ``litellm.completion`` which is already traced by +``opentelemetry-instrumentation-litellm``; emitting another span here would +duplicate that record. + +Patch targets (all monkey-patched via ``wrapt.wrap_function_wrapper``): + + P0 Terminus2.perform_task → ENTRY span (application entry) + P0 Terminus2._run_agent_loop → AGENT span + episode lifecycle + P0 Terminus2._execute_commands → TOOL span + P1 Terminus2._handle_llm_interaction → STEP span (per ReAct iteration) + P1 TerminusJSONPlainParser.parse_response / + TerminusXMLPlainParser.parse_response → TASK span + P2 Terminus2._summarize → CHAIN span (handoff) +""" + +import contextvars +import json +import logging +from typing import Any, Collection + +from opentelemetry import context as context_api +from opentelemetry import trace as trace_api +from opentelemetry.instrumentation.instrumentor import BaseInstrumentor +from opentelemetry.instrumentation.utils import unwrap +from opentelemetry.trace import SpanKind, Status, StatusCode +from wrapt import wrap_function_wrapper + +from aliyun.semconv.trace_v2 import ( + CommonAttributes, + GenAiOperationName, + GenAiSpanKind, + GenAiToolType, + LLMAttributes, + ToolAttributes, +) + +from aliyun.sdk.extension.arms.self_monitor.self_monitor_decorator import hook_advice + +from opentelemetry.instrumentation.terminus2.package import _instruments + +logger = logging.getLogger(__name__) + +# ── Framework / agent identifiers ──────────────────────────────────────────── +_FRAMEWORK = "terminal-bench" +_AGENT_NAME = "terminus-2" +_TERMINAL_TOOL_NAME = "terminal" +_TERMINAL_TOOL_DESCRIPTION = "Send keystrokes to a tmux terminal session" + +# Spec-defined tool I/O attribute keys (not yet exposed as constants in +# aliyun.semconv.trace_v2.ToolAttributes; see gen-ai.md §Tool). +_GEN_AI_TOOL_CALL_ARGUMENTS = "gen_ai.tool.call.arguments" +_GEN_AI_TOOL_CALL_RESULT = "gen_ai.tool.call.result" + +# ── Span kind / operation values not present in trace_v2 enums ─────────────── +_SPAN_KIND_ENTRY = "ENTRY" +_SPAN_KIND_STEP = "STEP" +_OP_ENTER = "enter" +_OP_REACT = "react" +_OP_RUN_TASK = "run_task" +_OP_TASK = "task" + +# ── ReAct extension attributes (阿里云扩展规范) ────────────────────────────── +_GEN_AI_REACT_ROUND = "gen_ai.react.round" +_GEN_AI_REACT_FINISH_REASON = "gen_ai.react.finish_reason" + +# ── Content capture ───────────────────────────────────────────────────────── +# Inputs / outputs (instruction text, terminal keystrokes, terminal output, +# AgentResult summary) are captured **unconditionally and untruncated** — +# they are the primary observability signal for terminus-2. If full content +# is undesirable in a given deployment, configure exporter-side filtering or +# attribute-length limits in the SDK instead. + + +def _commands_to_arguments_json(commands) -> str: + """Serialize a list of ``Command`` objects into a JSON string for + ``gen_ai.tool.call.arguments``.""" + serialized = [] + for cmd in commands: + serialized.append({ + "keystrokes": getattr(cmd, "keystrokes", ""), + "duration_sec": getattr(cmd, "duration_sec", None), + }) + try: + return json.dumps(serialized, ensure_ascii=False) + except Exception: + return str(serialized) + +# ── ReAct step lifecycle tracked via contextvars ──────────────────────────── +# A STEP span stays open across `_handle_llm_interaction` ⇒ `_execute_commands` +# so both become its children. It is closed when the next iteration starts or +# when `_run_agent_loop` returns. +_current_step_span = contextvars.ContextVar( + "terminus2_current_step_span", default=None +) +_current_step_token = contextvars.ContextVar( + "terminus2_current_step_token", default=None +) +_react_round_counter = contextvars.ContextVar( + "terminus2_react_round_counter", default=0 +) + + +def _end_current_step(finish_reason: str | None = None) -> None: + """End the active ReAct STEP span (if any) and detach its context.""" + span = _current_step_span.get() + token = _current_step_token.get() + if span is not None: + if finish_reason: + span.set_attribute(_GEN_AI_REACT_FINISH_REASON, finish_reason) + span.end() + _current_step_span.set(None) + if token is not None: + context_api.detach(token) + _current_step_token.set(None) + + +def _infer_provider_name(model_name: str) -> str: + """Infer ``gen_ai.provider.name`` from a model identifier string.""" + if not model_name: + return "unknown" + lower = model_name.lower() + if any(k in lower for k in ("gpt", "o1-", "o3-", "o4-")): + return "openai" + if "claude" in lower or "anthropic" in lower: + return "anthropic" + if "gemini" in lower: + return "google" + if "llama" in lower or "meta" in lower: + return "meta" + if "mistral" in lower: + return "mistral" + if "qwen" in lower: + return "alibaba" + if "deepseek" in lower: + return "deepseek" + if "/" in model_name: + return model_name.split("/", 1)[0] + return "unknown" + + +# Sentinel attribute attached to every target we successfully wrap. Stored +# on the target callable itself (not in module-level state) so that +# duplicate wraps are detected even if this package is loaded as multiple +# module instances (e.g. wheel install + ``pip install -e`` source, or +# under different sys.path roots), or if ``_instrument()`` is invoked +# twice via auto-loader + manual call. +_TERMINUS2_MARKER = "_otel_terminus2_wrapped" + + +def _resolve_target(module: str, name: str): + """Resolve ``module.name`` (where ``name`` may be ``Class.method``). + + Returns ``(parent, attr_name, current_value)``. Raises on missing + module / attribute. + """ + from importlib import import_module + mod = import_module(module) + parts = name.split(".") + parent = mod + for p in parts[:-1]: + parent = getattr(parent, p) + attr = parts[-1] + return parent, attr, getattr(parent, attr, None) + + +def _try_wrap(module: str, name: str, wrapper) -> None: + """Wrap ``module.name`` with ``wrapper`` exactly once. + + Idempotency is enforced via a sentinel attribute attached to the + target — robust against multiple module instances of this package and + repeated ``_instrument()`` invocations. + """ + try: + parent, attr, current = _resolve_target(module, name) + except Exception as e: + logger.warning(f"Could not resolve {module}.{name}: {e}") + return + + if current is None: + logger.warning(f"{module}.{name} not found") + return + + if getattr(current, _TERMINUS2_MARKER, False): + logger.debug( + f"{module}.{name} already wrapped by terminus2 instrumentation, " + "skipping" + ) + return + + try: + wrap_function_wrapper(module=module, name=name, wrapper=wrapper) + except Exception as e: + logger.warning(f"Could not wrap {module}.{name}: {e}") + return + + # Mark the freshly installed wrapper. wrapt's FunctionWrapper proxies + # attribute writes to the underlying wrapped object, but reading the + # attribute back through the proxy returns the same value, so a + # subsequent ``getattr`` check on either layer detects the marker. + new_value = getattr(parent, attr, None) + if new_value is not None: + try: + setattr(new_value, _TERMINUS2_MARKER, True) + except Exception as e: + logger.debug(f"Could not mark {module}.{name}: {e}") + + +def _try_unwrap(module: str, name: str) -> None: + """Reverse of :func:`_try_wrap`.""" + try: + parent, attr, current = _resolve_target(module, name) + except Exception: + return + + if current is None or not getattr(current, _TERMINUS2_MARKER, False): + return + + # Clear the marker on the underlying object first (FunctionWrapper + # forwards delattr to the wrapped object, so the marker — which was + # written through to the original — is removed cleanly). + try: + delattr(current, _TERMINUS2_MARKER) + except (AttributeError, TypeError): + pass + + try: + unwrap(parent, attr) + except Exception as e: + logger.debug(f"Could not unwrap {module}.{name}: {e}") + + +# ═══════════════════════════════════════════════════════════════════════════ +# Instrumentor +# ═══════════════════════════════════════════════════════════════════════════ + +class Terminus2Instrumentor(BaseInstrumentor): + """Instrumentor for the terminus-2 agent from terminal-bench.""" + + def instrumentation_dependencies(self) -> Collection[str]: + return _instruments + + def _instrument(self, **kwargs: Any) -> None: + tracer_provider = kwargs.get("tracer_provider") + tracer = trace_api.get_tracer(__name__, "", tracer_provider=tracer_provider) + + # P0 – ENTRY span (application entry point) + _try_wrap( + "terminal_bench.agents.terminus_2.terminus_2", + "Terminus2.perform_task", + _PerformTaskWrapper(tracer), + ) + + # P0 – AGENT span (agent invocation) + ReAct loop lifecycle + _try_wrap( + "terminal_bench.agents.terminus_2.terminus_2", + "Terminus2._run_agent_loop", + _RunAgentLoopWrapper(tracer), + ) + + # NOTE: LLM spans for ``LiteLLM.call`` are NOT produced here — + # ``opentelemetry-instrumentation-litellm`` already traces the + # underlying ``litellm.completion`` invocation. Wrapping again would + # produce duplicate LLM spans for every model call. + + # P0 – TOOL span for terminal command batch + _try_wrap( + "terminal_bench.agents.terminus_2.terminus_2", + "Terminus2._execute_commands", + _ExecuteCommandsWrapper(tracer), + ) + + # P1 – STEP span per ReAct iteration + _try_wrap( + "terminal_bench.agents.terminus_2.terminus_2", + "Terminus2._handle_llm_interaction", + _HandleLLMInteractionWrapper(tracer), + ) + + # P1 – TASK span for parser (json + xml) + _try_wrap( + "terminal_bench.agents.terminus_2.terminus_json_plain_parser", + "TerminusJSONPlainParser.parse_response", + _ParseResponseWrapper(tracer, "json"), + ) + _try_wrap( + "terminal_bench.agents.terminus_2.terminus_xml_plain_parser", + "TerminusXMLPlainParser.parse_response", + _ParseResponseWrapper(tracer, "xml"), + ) + + # P2 – CHAIN span for context-overflow handoff + _try_wrap( + "terminal_bench.agents.terminus_2.terminus_2", + "Terminus2._summarize", + _SummarizeWrapper(tracer), + ) + + def _uninstrument(self, **kwargs: Any) -> None: + _try_unwrap( + "terminal_bench.agents.terminus_2.terminus_2", + "Terminus2.perform_task", + ) + _try_unwrap( + "terminal_bench.agents.terminus_2.terminus_2", + "Terminus2._run_agent_loop", + ) + _try_unwrap( + "terminal_bench.agents.terminus_2.terminus_2", + "Terminus2._execute_commands", + ) + _try_unwrap( + "terminal_bench.agents.terminus_2.terminus_2", + "Terminus2._handle_llm_interaction", + ) + _try_unwrap( + "terminal_bench.agents.terminus_2.terminus_json_plain_parser", + "TerminusJSONPlainParser.parse_response", + ) + _try_unwrap( + "terminal_bench.agents.terminus_2.terminus_xml_plain_parser", + "TerminusXMLPlainParser.parse_response", + ) + _try_unwrap( + "terminal_bench.agents.terminus_2.terminus_2", + "Terminus2._summarize", + ) + _end_current_step() + + +# ═══════════════════════════════════════════════════════════════════════════ +# P0 — ENTRY span: Terminus2.perform_task +# ═══════════════════════════════════════════════════════════════════════════ + +class _PerformTaskWrapper: + """Wrap ``Terminus2.perform_task`` to produce the **ENTRY** span. + + Per spec: span name ``enter_ai_application_system``, + ``gen_ai.span.kind=ENTRY``, ``gen_ai.operation.name=enter``. + + Records the user instruction as ``input.value`` and a serialized summary + of ``AgentResult`` (failure_mode, token totals, marker count) as + ``output.value`` once the task completes. + """ + + def __init__(self, tracer): + self._tracer = tracer + + @hook_advice( + instrumentation_name="terminus2", + advice_method="perform_task", + throw_exception=True, + ) + def __call__(self, wrapped, instance, args, kwargs): + model_name = getattr(instance, "_model_name", "unknown") + instruction = args[0] if args else kwargs.get("instruction", "") + + with self._tracer.start_as_current_span( + "enter_ai_application_system", + kind=SpanKind.SERVER, + ) as span: + span.set_attribute(CommonAttributes.GEN_AI_SPAN_KIND, _SPAN_KIND_ENTRY) + span.set_attribute(CommonAttributes.GEN_AI_OPERATION_NAME, _OP_ENTER) + span.set_attribute(CommonAttributes.GEN_AI_FRAMEWORK, _FRAMEWORK) + span.set_attribute(LLMAttributes.GEN_AI_REQUEST_MODEL, model_name) + span.set_attribute( + LLMAttributes.GEN_AI_PROVIDER_NAME, + _infer_provider_name(model_name), + ) + + if instruction: + span.set_attribute( + CommonAttributes.INPUT_VALUE, str(instruction) + ) + span.set_attribute(CommonAttributes.INPUT_MIME_TYPE, "text/plain") + + try: + result = wrapped(*args, **kwargs) + except Exception as e: + span.record_exception(e) + span.set_status(Status(StatusCode.ERROR)) + raise + + input_tokens = getattr(result, "total_input_tokens", 0) or 0 + output_tokens = getattr(result, "total_output_tokens", 0) or 0 + failure_mode = getattr(result, "failure_mode", None) + failure_mode_str = str( + getattr(failure_mode, "value", failure_mode) + ) if failure_mode is not None else "none" + markers = getattr(result, "timestamped_markers", None) or [] + + output_summary = { + "failure_mode": failure_mode_str, + "total_input_tokens": input_tokens, + "total_output_tokens": output_tokens, + "marker_count": len(markers), + } + try: + output_value = json.dumps(output_summary, ensure_ascii=False) + except Exception: + output_value = str(output_summary) + + span.set_attribute(CommonAttributes.OUTPUT_VALUE, output_value) + span.set_attribute(CommonAttributes.OUTPUT_MIME_TYPE, "application/json") + span.set_attribute("terminus2.failure_mode", failure_mode_str) + + span.set_status(Status(StatusCode.OK)) + return result + + +# ═══════════════════════════════════════════════════════════════════════════ +# P0 — AGENT span: Terminus2._run_agent_loop +# ═══════════════════════════════════════════════════════════════════════════ + +class _RunAgentLoopWrapper: + """Wrap ``Terminus2._run_agent_loop`` to produce the **AGENT** span. + + Per spec: span name ``invoke_agent {agent.name}``, + ``gen_ai.span.kind=AGENT``, ``gen_ai.operation.name=invoke_agent``. + + The AGENT span precisely brackets the ReAct loop body — STEP / TOOL / + TASK / CHAIN children all hang off it. Token totals are aggregated + from the ``Chat`` cumulative counters once the loop returns. Also + cleans up any trailing STEP span on loop exit. + """ + + def __init__(self, tracer): + self._tracer = tracer + + @hook_advice( + instrumentation_name="terminus2", + advice_method="run_agent_loop", + throw_exception=True, + ) + def __call__(self, wrapped, instance, args, kwargs): + # Reset per-loop ReAct state + _react_round_counter.set(0) + _end_current_step() + + model_name = getattr(instance, "_model_name", "unknown") + parser_name = getattr(instance, "_parser_name", "unknown") + + # _run_agent_loop signature: + # (initial_prompt, session, chat, logging_dir=None, + # original_instruction="") + chat = args[2] if len(args) > 2 else kwargs.get("chat") + original_instruction = ( + args[4] if len(args) > 4 else kwargs.get("original_instruction", "") + ) + + with self._tracer.start_as_current_span( + f"invoke_agent {_AGENT_NAME}", + kind=SpanKind.INTERNAL, + ) as span: + span.set_attribute( + CommonAttributes.GEN_AI_SPAN_KIND, GenAiSpanKind.AGENT.value + ) + span.set_attribute( + CommonAttributes.GEN_AI_OPERATION_NAME, + GenAiOperationName.INVOKE_AGENT.value, + ) + span.set_attribute(CommonAttributes.GEN_AI_FRAMEWORK, _FRAMEWORK) + span.set_attribute("gen_ai.agent.name", _AGENT_NAME) + span.set_attribute( + "gen_ai.agent.description", + "Terminus-2 terminal-bench agent (ReAct loop over a tmux session)", + ) + span.set_attribute(LLMAttributes.GEN_AI_REQUEST_MODEL, model_name) + span.set_attribute( + LLMAttributes.GEN_AI_PROVIDER_NAME, + _infer_provider_name(model_name), + ) + span.set_attribute("terminus2.parser", parser_name) + + if original_instruction: + span.set_attribute( + CommonAttributes.INPUT_VALUE, + str(original_instruction), + ) + span.set_attribute(CommonAttributes.INPUT_MIME_TYPE, "text/plain") + + try: + result = wrapped(*args, **kwargs) + except Exception as e: + span.record_exception(e) + span.set_status(Status(StatusCode.ERROR)) + _end_current_step(finish_reason="loop_end") + raise + + _end_current_step(finish_reason="loop_end") + + # Aggregate token usage from the Chat object — captured here so + # the totals reflect the full loop, including the bare + # ``chat._model.call`` invoked inside ``_summarize``. + # ``Chat.total_*_tokens`` returns cumulative counters that + # survive context unwinding. + if chat is not None: + input_tokens = getattr(chat, "total_input_tokens", 0) or 0 + output_tokens = getattr(chat, "total_output_tokens", 0) or 0 + span.set_attribute( + LLMAttributes.GEN_AI_USAGE_INPUT_TOKENS, input_tokens + ) + span.set_attribute( + LLMAttributes.GEN_AI_USAGE_OUTPUT_TOKENS, output_tokens + ) + span.set_attribute( + LLMAttributes.GEN_AI_USAGE_TOTAL_TOKENS, + input_tokens + output_tokens, + ) + + span.set_attribute( + "terminus2.react.rounds", _react_round_counter.get() + ) + + span.set_status(Status(StatusCode.OK)) + return result + + +# ═══════════════════════════════════════════════════════════════════════════ +# P0 — TOOL span: Terminus2._execute_commands +# ═══════════════════════════════════════════════════════════════════════════ + +class _ExecuteCommandsWrapper: + """Wrap ``Terminus2._execute_commands`` to produce a **TOOL** span. + + Per spec: span name ``execute_tool {tool_name}``, + ``gen_ai.span.kind=TOOL``, ``gen_ai.operation.name=execute_tool``. + """ + + def __init__(self, tracer): + self._tracer = tracer + + @hook_advice( + instrumentation_name="terminus2", + advice_method="execute_commands", + throw_exception=True, + ) + def __call__(self, wrapped, instance, args, kwargs): + commands = args[0] if args else kwargs.get("commands", []) + + with self._tracer.start_as_current_span( + f"execute_tool {_TERMINAL_TOOL_NAME}", + kind=SpanKind.INTERNAL, + ) as span: + span.set_attribute( + CommonAttributes.GEN_AI_SPAN_KIND, GenAiSpanKind.TOOL.value + ) + span.set_attribute( + CommonAttributes.GEN_AI_OPERATION_NAME, + GenAiOperationName.EXECUTE_TOOL.value, + ) + span.set_attribute(CommonAttributes.GEN_AI_FRAMEWORK, _FRAMEWORK) + span.set_attribute(ToolAttributes.GEN_AI_TOOL_NAME, _TERMINAL_TOOL_NAME) + span.set_attribute( + ToolAttributes.GEN_AI_TOOL_DESCRIPTION, _TERMINAL_TOOL_DESCRIPTION + ) + span.set_attribute( + ToolAttributes.GEN_AI_TOOL_TYPE, GenAiToolType.EXTENSION.value + ) + span.set_attribute("terminus2.commands.count", len(commands)) + + arguments_json = _commands_to_arguments_json(commands) + # Spec attribute (gen-ai.md §Tool) + span.set_attribute(_GEN_AI_TOOL_CALL_ARGUMENTS, arguments_json) + # Common input.value mirror — many viewers only render this + span.set_attribute(CommonAttributes.INPUT_VALUE, arguments_json) + span.set_attribute(CommonAttributes.INPUT_MIME_TYPE, "application/json") + + try: + result = wrapped(*args, **kwargs) + except Exception as e: + span.record_exception(e) + span.set_status(Status(StatusCode.ERROR)) + raise + + timeout_occurred, terminal_output = result + span.set_attribute("terminus2.terminal.timeout", timeout_occurred) + + if terminal_output is not None: + output_text = str(terminal_output) + # Spec attribute (gen-ai.md §Tool) + span.set_attribute(_GEN_AI_TOOL_CALL_RESULT, output_text) + # Common output.value mirror + span.set_attribute(CommonAttributes.OUTPUT_VALUE, output_text) + span.set_attribute(CommonAttributes.OUTPUT_MIME_TYPE, "text/plain") + + span.set_status(Status(StatusCode.OK)) + return result + + +# ═══════════════════════════════════════════════════════════════════════════ +# P1 — STEP span: Terminus2._handle_llm_interaction +# ═══════════════════════════════════════════════════════════════════════════ + +class _HandleLLMInteractionWrapper: + """Wrap ``Terminus2._handle_llm_interaction`` to produce a **STEP** span. + + The STEP span represents one ReAct iteration. It opens here, stays open + after this method returns (so the subsequent ``_execute_commands`` call + in ``_run_agent_loop`` becomes its child), and is closed on the next + iteration entry or by ``_RunAgentLoopWrapper`` cleanup. + """ + + def __init__(self, tracer): + self._tracer = tracer + + @hook_advice( + instrumentation_name="terminus2", + advice_method="handle_llm_interaction", + throw_exception=True, + ) + def __call__(self, wrapped, instance, args, kwargs): + # Close previous STEP first (if any) + _end_current_step(finish_reason="next_round") + + round_num = _react_round_counter.get() + 1 + _react_round_counter.set(round_num) + + step_span = self._tracer.start_span( + "react step", + kind=SpanKind.INTERNAL, + ) + step_span.set_attribute(CommonAttributes.GEN_AI_SPAN_KIND, _SPAN_KIND_STEP) + step_span.set_attribute(CommonAttributes.GEN_AI_OPERATION_NAME, _OP_REACT) + step_span.set_attribute(CommonAttributes.GEN_AI_FRAMEWORK, _FRAMEWORK) + step_span.set_attribute(_GEN_AI_REACT_ROUND, round_num) + + ctx = trace_api.set_span_in_context(step_span) + token = context_api.attach(ctx) + _current_step_span.set(step_span) + _current_step_token.set(token) + + try: + result = wrapped(*args, **kwargs) + except Exception as e: + step_span.set_attribute(_GEN_AI_REACT_FINISH_REASON, "error") + step_span.record_exception(e) + step_span.set_status(Status(StatusCode.ERROR)) + raise + + commands, is_task_complete, feedback = result + + if is_task_complete: + step_span.set_attribute(_GEN_AI_REACT_FINISH_REASON, "complete") + elif feedback and "ERROR:" in feedback: + step_span.set_attribute(_GEN_AI_REACT_FINISH_REASON, "parse_error") + + # Span stays open: closed by next iteration or _RunAgentLoopWrapper + return result + + +# ═══════════════════════════════════════════════════════════════════════════ +# P1 — TASK span: parser.parse_response +# ═══════════════════════════════════════════════════════════════════════════ + +class _ParseResponseWrapper: + """Wrap ``parser.parse_response`` to produce a **TASK** span. + + Per spec: span name ``run_task {task_name}``, + ``gen_ai.span.kind=TASK``, ``gen_ai.operation.name=run_task``. + """ + + def __init__(self, tracer, parser_type): + self._tracer = tracer + self._parser_type = parser_type + + @hook_advice( + instrumentation_name="terminus2", + advice_method="parse_response", + throw_exception=True, + ) + def __call__(self, wrapped, instance, args, kwargs): + # parse_response signature: (self, response: str) + response_text = args[0] if args else kwargs.get("response", "") + + with self._tracer.start_as_current_span( + "run_task parse_response", + kind=SpanKind.INTERNAL, + ) as span: + span.set_attribute( + CommonAttributes.GEN_AI_SPAN_KIND, GenAiSpanKind.TASK.value + ) + span.set_attribute(CommonAttributes.GEN_AI_OPERATION_NAME, _OP_RUN_TASK) + span.set_attribute(CommonAttributes.GEN_AI_FRAMEWORK, _FRAMEWORK) + span.set_attribute("terminus2.parser", self._parser_type) + + if response_text is not None: + span.set_attribute( + CommonAttributes.INPUT_VALUE, str(response_text) + ) + span.set_attribute(CommonAttributes.INPUT_MIME_TYPE, "text/plain") + + try: + result = wrapped(*args, **kwargs) + except Exception as e: + span.record_exception(e) + span.set_status(Status(StatusCode.ERROR)) + raise + + span.set_attribute("terminus2.task_complete", result.is_task_complete) + span.set_attribute("terminus2.commands.count", len(result.commands)) + + output_summary = { + "is_task_complete": result.is_task_complete, + "commands": [ + { + "keystrokes": getattr(c, "keystrokes", ""), + "duration": getattr(c, "duration", None), + } + for c in result.commands + ], + "error": result.error or "", + "warning": result.warning or "", + } + try: + output_value = json.dumps(output_summary, ensure_ascii=False) + except Exception: + output_value = str(output_summary) + span.set_attribute(CommonAttributes.OUTPUT_VALUE, output_value) + span.set_attribute(CommonAttributes.OUTPUT_MIME_TYPE, "application/json") + + if result.error: + span.set_attribute("terminus2.parse.error", str(result.error)) + + if result.warning: + span.set_attribute("terminus2.parse.warning", str(result.warning)) + + span.set_status(Status(StatusCode.OK)) + return result + + +# ═══════════════════════════════════════════════════════════════════════════ +# P2 — CHAIN span: Terminus2._summarize +# ═══════════════════════════════════════════════════════════════════════════ + +class _SummarizeWrapper: + """Wrap ``Terminus2._summarize`` to produce a **CHAIN** span. + + Per spec: span name ``chain {chain_name}``, + ``gen_ai.span.kind=CHAIN``. The summarize handoff itself triggers + multiple inner LLM calls so it semantically maps to a Chain. + """ + + def __init__(self, tracer): + self._tracer = tracer + + @hook_advice( + instrumentation_name="terminus2", + advice_method="summarize", + throw_exception=True, + ) + def __call__(self, wrapped, instance, args, kwargs): + with self._tracer.start_as_current_span( + "chain summarize", + kind=SpanKind.INTERNAL, + ) as span: + span.set_attribute( + CommonAttributes.GEN_AI_SPAN_KIND, GenAiSpanKind.CHAIN.value + ) + span.set_attribute(CommonAttributes.GEN_AI_OPERATION_NAME, _OP_TASK) + span.set_attribute(CommonAttributes.GEN_AI_FRAMEWORK, _FRAMEWORK) + + try: + result = wrapped(*args, **kwargs) + except Exception as e: + span.record_exception(e) + span.set_status(Status(StatusCode.ERROR)) + raise + + span.set_status(Status(StatusCode.OK)) + return result diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-terminus2/src/opentelemetry/instrumentation/terminus2/package.py b/instrumentation-loongsuite/loongsuite-instrumentation-terminus2/src/opentelemetry/instrumentation/terminus2/package.py new file mode 100644 index 000000000..d92c81333 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-terminus2/src/opentelemetry/instrumentation/terminus2/package.py @@ -0,0 +1,15 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +_instruments = ("terminal-bench >= 0.1.0",) diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-terminus2/src/opentelemetry/instrumentation/terminus2/version.py b/instrumentation-loongsuite/loongsuite-instrumentation-terminus2/src/opentelemetry/instrumentation/terminus2/version.py new file mode 100644 index 000000000..5fd301e2e --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-terminus2/src/opentelemetry/instrumentation/terminus2/version.py @@ -0,0 +1,15 @@ +# Copyright The OpenTelemetry Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +__version__ = "0.1.0" diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-terminus2/test-requirements.txt b/instrumentation-loongsuite/loongsuite-instrumentation-terminus2/test-requirements.txt new file mode 100644 index 000000000..f98537dd8 --- /dev/null +++ b/instrumentation-loongsuite/loongsuite-instrumentation-terminus2/test-requirements.txt @@ -0,0 +1,4 @@ +terminal-bench>=0.1.0 +-e aliyun-semantic-conventions +-e util/opentelemetry-util-http +-e instrumentation-loongsuite/loongsuite-instrumentation-terminus2 diff --git a/instrumentation-loongsuite/loongsuite-instrumentation-terminus2/tests/__init__.py b/instrumentation-loongsuite/loongsuite-instrumentation-terminus2/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/packages.txt b/packages.txt new file mode 100644 index 000000000..cee224898 --- /dev/null +++ b/packages.txt @@ -0,0 +1,112 @@ +aiohappyeyeballs==2.6.1 +aiohttp==3.10.2 +aiosignal==1.3.1 +aliyun-instrumentation-sglang @ file:///Users/liuziming/Desktop/loongsuite-python-agent/instrumentation/aliyun-instrumentation-sglang +aliyun-instrumentation-vllm @ file:///Users/liuziming/Desktop/loongsuite-python-agent/instrumentation/aliyun-instrumentation-vllm +-e git+https://github.com/alibaba/loongsuite-python-agent.git@fe5b8bf1938dcd449dfa335234b58af81b00bc98#egg=aliyun_sdk_extension_arms&subdirectory=sdk-extension/aliyun-sdk-extension-arms +aliyun-semantic-conventions==1.2.0 +annotated-types==0.7.0 +anyio==4.10.0 +asgiref==3.8.1 +asttokens==3.0.0 +async-timeout==4.0.3 +attrs==25.3.0 +blinker==1.7.0 +build==1.3.0 +bytecode==0.17.0 +certifi==2024.7.4 +chardet==5.2.0 +charset-normalizer==3.3.2 +click==8.1.7 +cramjam==2.10.0 +crcmod==1.7 +decorator==5.2.1 +Deprecated==1.2.14 +Django==5.2.4 +executing==2.2.1 +fastapi==0.116.1 +filelock==3.19.1 +Flask==3.0.2 +frozenlist==1.4.1 +fsspec==2025.9.0 +googleapis-common-protos==1.70.0 +h11==0.16.0 +http_server_mock==1.7 +httpcore==1.0.9 +httpretty==1.1.4 +httpx==0.28.1 +idna==3.7 +importlib_metadata==8.4.0 +iniconfig==2.0.0 +ipython==9.5.0 +ipython_pygments_lexers==1.1.1 +itsdangerous==2.1.2 +jedi==0.19.2 +Jinja2==3.1.4 +jsonpath==0.82.2 +MarkupSafe==2.1.5 +matplotlib-inline==0.1.7 +mpmath==1.3.0 +multidict==6.0.5 +networkx==3.5 +numpy==2.3.2 +opentelemetry-api==1.30.0 +-e git+https://github.com/alibaba/loongsuite-python-agent.git@fe5b8bf1938dcd449dfa335234b58af81b00bc98#egg=opentelemetry_exporter_otlp_proto_http&subdirectory=exporter/opentelemetry-exporter-otlp-proto-http +-e git+https://github.com/alibaba/loongsuite-python-agent.git@fe5b8bf1938dcd449dfa335234b58af81b00bc98#egg=opentelemetry_instrumentation&subdirectory=opentelemetry-instrumentation +-e git+https://github.com/alibaba/loongsuite-python-agent.git@fe5b8bf1938dcd449dfa335234b58af81b00bc98#egg=opentelemetry_instrumentation_aiohttp_client&subdirectory=instrumentation/opentelemetry-instrumentation-aiohttp-client +opentelemetry-instrumentation-asgi @ file:///Users/liuziming/Desktop/loongsuite-python-agent/instrumentation/opentelemetry-instrumentation-asgi +-e git+https://github.com/alibaba/loongsuite-python-agent.git@fe5b8bf1938dcd449dfa335234b58af81b00bc98#egg=opentelemetry_instrumentation_django&subdirectory=instrumentation/opentelemetry-instrumentation-django +opentelemetry-instrumentation-fastapi @ file:///Users/liuziming/Desktop/loongsuite-python-agent/instrumentation/opentelemetry-instrumentation-fastapi +opentelemetry-instrumentation-flask @ file:///Users/liuziming/Desktop/loongsuite-python-agent/instrumentation/opentelemetry-instrumentation-flask +opentelemetry-instrumentation-httpx @ file:///Users/liuziming/Desktop/loongsuite-python-agent/instrumentation/opentelemetry-instrumentation-httpx +opentelemetry-instrumentation-requests @ file:///Users/liuziming/Desktop/loongsuite-python-agent/instrumentation/opentelemetry-instrumentation-requests +opentelemetry-instrumentation-tornado @ file:///Users/liuziming/Desktop/loongsuite-python-agent/instrumentation/opentelemetry-instrumentation-tornado +opentelemetry-instrumentation-wsgi==0.51b0 +-e git+https://github.com/alibaba/loongsuite-python-agent.git@fe5b8bf1938dcd449dfa335234b58af81b00bc98#egg=opentelemetry_sdk&subdirectory=opentelemetry-sdk +-e git+https://github.com/alibaba/loongsuite-python-agent.git@fe5b8bf1938dcd449dfa335234b58af81b00bc98#egg=opentelemetry_semantic_conventions&subdirectory=opentelemetry-semantic-conventions +-e git+https://github.com/alibaba/loongsuite-python-agent.git@fe5b8bf1938dcd449dfa335234b58af81b00bc98#egg=opentelemetry_test_utils&subdirectory=opentelemetry-test-utils +-e git+https://github.com/alibaba/loongsuite-python-agent.git@fe5b8bf1938dcd449dfa335234b58af81b00bc98#egg=opentelemetry_util_http&subdirectory=util/opentelemetry-util-http +packaging==24.0 +parso==0.8.5 +pexpect==4.9.0 +pillow==11.3.0 +pluggy==1.5.0 +prompt_toolkit==3.0.52 +propcache==0.3.2 +protobuf==6.32.0 +psutil==7.0.0 +ptyprocess==0.7.0 +pure_eval==0.2.3 +py-cpuinfo==9.0.0 +pydantic==2.11.7 +pydantic_core==2.33.2 +Pygments==2.19.2 +pyproject_hooks==1.2.0 +pytest==7.4.4 +python-snappy==0.7.3 +PyYAML==6.0.2 +requests==2.32.3 +setproctitle==1.3.6 +setuptools==80.9.0 +sglang==0.4.8 +sniffio==1.3.1 +sqlparse==0.5.3 +stack-data==0.6.3 +starlette==0.47.2 +sympy==1.14.0 +tomli==2.0.1 +tomlkit==0.13.3 +torch==2.8.0 +tornado==6.5.2 +tqdm==4.67.1 +traitlets==5.14.3 +typing-inspection==0.4.1 +typing_extensions==4.12.2 +urllib3==2.2.2 +uvloop==0.21.0 +wcwidth==0.2.13 +Werkzeug==3.0.6 +wheel==0.45.1 +wrapt==1.16.0 +yarl==1.9.4 +zipp==3.19.2