From e6434ea216f732560da96113d131310feb552494 Mon Sep 17 00:00:00 2001 From: NoamBenS Date: Wed, 20 May 2026 17:48:36 -0400 Subject: [PATCH 1/4] updated langchain docs to reflect simpler integration, moved sdk blocks next to each other --- features/integrations.mdx | 128 +++++++++++++++++++------------------- 1 file changed, 63 insertions(+), 65 deletions(-) diff --git a/features/integrations.mdx b/features/integrations.mdx index 15484ba..0b3114d 100644 --- a/features/integrations.mdx +++ b/features/integrations.mdx @@ -113,6 +113,60 @@ Once configured, PromptLayer will capture Claude Code sessions, LLM calls, tool For troubleshooting and additional details on the direct plugin install path, see the [PromptLayer Claude Code plugin repository](https://github.com/MagnivOrg/promptlayer-claude-plugins). +## OpenAI Agents SDK + +PromptLayer supports the OpenAI Agents SDK in both [JavaScript](https://openai.github.io/openai-agents-js/) and [Python](https://openai.github.io/openai-agents-python/), allowing you to export agent traces directly to PromptLayer with a native PromptLayer trace processor. + +To set up: + +1. Install the required packages + + +```bash JavaScript +npm install promptlayer @openai/agents +``` + +```bash Python +pip install "promptlayer[openai-agents]" +``` + + +2. Register PromptLayer tracing before your first agent run: + + +```ts JavaScript +import { instrumentOpenAIAgents } from "promptlayer/openai-agents"; + +const processor = await instrumentOpenAIAgents(); +``` + +```python Python +from promptlayer.integrations.openai_agents import instrument_openai_agents + +instrument_openai_agents() +``` + + +3. Flush tracing before process exit so PromptLayer receives the final spans: + + +```ts JavaScript +await processor.forceFlush(); +await processor.shutdown(); +``` + +```python Python +# Optional - if you created your own tracer provider +tracer_provider.force_flush() +tracer_provider.shutdown() +``` + + +4. Set your environment variables: + +- `OPENAI_API_KEY` +- `PROMPTLAYER_API_KEY` + ## Vercel AI SDK PromptLayer supports integration with the [Vercel AI SDK](https://ai-sdk.dev/docs), allowing you to export OpenTelemetry traces from your application directly to PromptLayer. @@ -206,7 +260,7 @@ Once configured, PromptLayer will capture Pydantic AI agent runs, LLM calls, too PromptLayer supports ingesting [LangChain](https://js.langchain.com/) traces through [LangSmith's OpenTelemetry bridge](https://docs.langchain.com/langsmith/trace-with-opentelemetry). For new JavaScript applications where you can choose the framework, use the [Vercel AI SDK](#vercel-ai-sdk) integration because its OpenTelemetry spans map more directly into PromptLayer. This is also aligned with LangChain / LangSmith's JavaScript observability path: their docs provide a first-party [Vercel AI SDK tracing guide](https://docs.langchain.com/langsmith/trace-with-vercel-ai-sdk). Use this LangChain / LangSmith path when you already have a LangChain application. -LangChain's native spans can be sparse or framework-shaped rather than LLM-call-shaped. For best PromptLayer results, add manual child spans around important route, LLM, and tool boundaries and include GenAI attributes such as `gen_ai.operation.name`, `gen_ai.system`, `gen_ai.request.model`, `gen_ai.prompt`, and `gen_ai.completion`. +LangSmith's OTEL bridge creates LangChain spans automatically. Add manual spans for application-specific work, custom tool data, or extra inputs and outputs that should appear in PromptLayer traces. To set up: @@ -263,83 +317,27 @@ initializeOTEL({ }); ``` -4. Add manual spans for LLM and tool boundaries when you need request logs to be complete +4. Optionally add manual spans for application-specific data ```ts import { trace } from "@opentelemetry/api"; -await trace.getTracer("langchain-app").startActiveSpan("myapp.llm.call", async (span) => { +await trace.getTracer("langchain-app").startActiveSpan("app.workflow_step", async (span) => { try { - span.setAttribute("langsmith.span.kind", "llm"); - span.setAttribute("gen_ai.operation.name", "chat"); - span.setAttribute("gen_ai.system", "openai"); - span.setAttribute("gen_ai.request.model", "gpt-4o-mini"); - span.setAttribute("gen_ai.prompt", JSON.stringify(messages)); + span.setAttribute("app.operation", "retrieve_context"); + span.setAttribute("app.input", JSON.stringify(input)); - const result = await llm.invoke(messages); + const result = await runApplicationStep(input); - span.setAttribute("gen_ai.completion", String(result.content)); + span.setAttribute("app.result_count", result.items.length); + return result; } finally { span.end(); } }); ``` -Once configured, PromptLayer will capture LangChain and LangSmith spans. Manual GenAI attributes make LLM calls, tool calls, inputs, and outputs easier for PromptLayer to render as request logs. Thinking blocks from extended thinking models in LangChain completions are captured and preserved in the request log view. - -## OpenAI Agents SDK - -PromptLayer supports the OpenAI Agents SDK in both [JavaScript](https://openai.github.io/openai-agents-js/) and [Python](https://openai.github.io/openai-agents-python/), allowing you to export agent traces directly to PromptLayer with a native PromptLayer trace processor. - -To set up: - -1. Install the required packages - - -```bash JavaScript -npm install promptlayer @openai/agents -``` - -```bash Python -pip install "promptlayer[openai-agents]" -``` - - -2. Register PromptLayer tracing before your first agent run: - - -```ts JavaScript -import { instrumentOpenAIAgents } from "promptlayer/openai-agents"; - -const processor = await instrumentOpenAIAgents(); -``` - -```python Python -from promptlayer.integrations.openai_agents import instrument_openai_agents - -instrument_openai_agents() -``` - - -3. Flush tracing before process exit so PromptLayer receives the final spans: - - -```ts JavaScript -await processor.forceFlush(); -await processor.shutdown(); -``` - -```python Python -# Optional - if you created your own tracer provider -tracer_provider.force_flush() -tracer_provider.shutdown() -``` - - -4. Set your environment variables: - -- `OPENAI_API_KEY` -- `PROMPTLAYER_API_KEY` +Once configured, PromptLayer will capture LangChain and LangSmith spans. PromptLayer renders request logs from LangSmith and LangChain LLM attributes, including model metadata, messages, outputs, token counts, and thinking blocks from extended thinking models. ## OpenRouter From 0ec7b73cb7cb713c99968056dce071e014f9afee Mon Sep 17 00:00:00 2001 From: NoamBenS Date: Wed, 20 May 2026 18:37:01 -0400 Subject: [PATCH 2/4] added demo apps --- .../claude-promptlayer-nextjs/.env.example | 8 + examples/claude-promptlayer-nextjs/.gitignore | 30 + examples/claude-promptlayer-nextjs/README.md | 20 + .../app/api/run/route.ts | 90 + .../claude-promptlayer-nextjs/app/globals.css | 132 + .../claude-promptlayer-nextjs/app/layout.tsx | 15 + .../claude-promptlayer-nextjs/app/page.tsx | 65 + .../claude-promptlayer-nextjs/next.config.ts | 11 + .../package-lock.json | 5289 +++++++++++ .../claude-promptlayer-nextjs/package.json | 25 + .../claude-promptlayer-nextjs/tsconfig.json | 37 + .../claude-promptlayer-python/.env.example | 5 + examples/claude-promptlayer-python/.gitignore | 46 + examples/claude-promptlayer-python/README.md | 41 + .../claude-promptlayer-python/pyproject.toml | 18 + .../claude_promptlayer_python_lab/__init__.py | 1 + .../src/claude_promptlayer_python_lab/main.py | 141 + examples/langchain-itinerary/.env.example | 27 + examples/langchain-itinerary/.gitignore | 43 + examples/langchain-itinerary/README.md | 84 + .../langchain-itinerary/eslint.config.mjs | 18 + .../langchain-itinerary/instrumentation.ts | 2 + examples/langchain-itinerary/next.config.ts | 10 + .../langchain-itinerary/package-lock.json | 8107 +++++++++++++++++ examples/langchain-itinerary/package.json | 33 + .../src/app/api/chat/route.ts | 46 + .../src/app/chat-client.tsx | 138 + .../langchain-itinerary/src/app/globals.css | 389 + .../langchain-itinerary/src/app/itinerary.ts | 325 + .../langchain-itinerary/src/app/layout.tsx | 19 + examples/langchain-itinerary/src/app/page.tsx | 21 + .../src/instrumentation.ts | 49 + examples/langchain-itinerary/tsconfig.json | 34 + examples/openai-agents-fastapi/.env.example | 8 + examples/openai-agents-fastapi/.gitignore | 24 + examples/openai-agents-fastapi/README.md | 54 + examples/openai-agents-fastapi/app.py | 219 + examples/openai-agents-fastapi/pyproject.toml | 17 + examples/pydantic-ai-chat/.env.example | 17 + examples/pydantic-ai-chat/.gitignore | 9 + examples/pydantic-ai-chat/README.md | 100 + examples/pydantic-ai-chat/app.py | 150 + examples/pydantic-ai-chat/embeddings.py | 101 + examples/pydantic-ai-chat/pyproject.toml | 15 + 44 files changed, 16033 insertions(+) create mode 100644 examples/claude-promptlayer-nextjs/.env.example create mode 100644 examples/claude-promptlayer-nextjs/.gitignore create mode 100644 examples/claude-promptlayer-nextjs/README.md create mode 100644 examples/claude-promptlayer-nextjs/app/api/run/route.ts create mode 100644 examples/claude-promptlayer-nextjs/app/globals.css create mode 100644 examples/claude-promptlayer-nextjs/app/layout.tsx create mode 100644 examples/claude-promptlayer-nextjs/app/page.tsx create mode 100644 examples/claude-promptlayer-nextjs/next.config.ts create mode 100644 examples/claude-promptlayer-nextjs/package-lock.json create mode 100644 examples/claude-promptlayer-nextjs/package.json create mode 100644 examples/claude-promptlayer-nextjs/tsconfig.json create mode 100644 examples/claude-promptlayer-python/.env.example create mode 100644 examples/claude-promptlayer-python/.gitignore create mode 100644 examples/claude-promptlayer-python/README.md create mode 100644 examples/claude-promptlayer-python/pyproject.toml create mode 100644 examples/claude-promptlayer-python/src/claude_promptlayer_python_lab/__init__.py create mode 100644 examples/claude-promptlayer-python/src/claude_promptlayer_python_lab/main.py create mode 100644 examples/langchain-itinerary/.env.example create mode 100644 examples/langchain-itinerary/.gitignore create mode 100644 examples/langchain-itinerary/README.md create mode 100644 examples/langchain-itinerary/eslint.config.mjs create mode 100644 examples/langchain-itinerary/instrumentation.ts create mode 100644 examples/langchain-itinerary/next.config.ts create mode 100644 examples/langchain-itinerary/package-lock.json create mode 100644 examples/langchain-itinerary/package.json create mode 100644 examples/langchain-itinerary/src/app/api/chat/route.ts create mode 100644 examples/langchain-itinerary/src/app/chat-client.tsx create mode 100644 examples/langchain-itinerary/src/app/globals.css create mode 100644 examples/langchain-itinerary/src/app/itinerary.ts create mode 100644 examples/langchain-itinerary/src/app/layout.tsx create mode 100644 examples/langchain-itinerary/src/app/page.tsx create mode 100644 examples/langchain-itinerary/src/instrumentation.ts create mode 100644 examples/langchain-itinerary/tsconfig.json create mode 100644 examples/openai-agents-fastapi/.env.example create mode 100644 examples/openai-agents-fastapi/.gitignore create mode 100644 examples/openai-agents-fastapi/README.md create mode 100644 examples/openai-agents-fastapi/app.py create mode 100644 examples/openai-agents-fastapi/pyproject.toml create mode 100644 examples/pydantic-ai-chat/.env.example create mode 100644 examples/pydantic-ai-chat/.gitignore create mode 100644 examples/pydantic-ai-chat/README.md create mode 100644 examples/pydantic-ai-chat/app.py create mode 100644 examples/pydantic-ai-chat/embeddings.py create mode 100644 examples/pydantic-ai-chat/pyproject.toml diff --git a/examples/claude-promptlayer-nextjs/.env.example b/examples/claude-promptlayer-nextjs/.env.example new file mode 100644 index 0000000..d6754b7 --- /dev/null +++ b/examples/claude-promptlayer-nextjs/.env.example @@ -0,0 +1,8 @@ +# Required for Claude API calls. +ANTHROPIC_API_KEY= + +# Required for PromptLayer tracing. +PROMPTLAYER_API_KEY= + +# Optional. Defaults to 3333. +PORT=3333 diff --git a/examples/claude-promptlayer-nextjs/.gitignore b/examples/claude-promptlayer-nextjs/.gitignore new file mode 100644 index 0000000..10adbb1 --- /dev/null +++ b/examples/claude-promptlayer-nextjs/.gitignore @@ -0,0 +1,30 @@ +# Dependencies +node_modules/ + +# Environment +.env +.env*.local + +# Next.js +.next/ +out/ +next-env.d.ts + +# Production/build output +dist/ +build/ + +# TypeScript +tsconfig.tsbuildinfo + +# Logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* + +# OS/editor +.DS_Store +.vscode/ +.idea/ diff --git a/examples/claude-promptlayer-nextjs/README.md b/examples/claude-promptlayer-nextjs/README.md new file mode 100644 index 0000000..02f3fda --- /dev/null +++ b/examples/claude-promptlayer-nextjs/README.md @@ -0,0 +1,20 @@ +# Claude PromptLayer Next App + +Tiny Next.js app for manually testing PromptLayer's Claude Agent SDK integration. + +## Setup + +```bash +cp .env.example .env.local +npm install +npm run dev +``` + +Open the local URL printed by Next.js. + +## Environment + +- `ANTHROPIC_API_KEY` runs Claude. +- `PROMPTLAYER_API_KEY` enables PromptLayer tracing through `getClaudeConfig()`. + +The development server uses port `3333` by default. diff --git a/examples/claude-promptlayer-nextjs/app/api/run/route.ts b/examples/claude-promptlayer-nextjs/app/api/run/route.ts new file mode 100644 index 0000000..30432bb --- /dev/null +++ b/examples/claude-promptlayer-nextjs/app/api/run/route.ts @@ -0,0 +1,90 @@ +import { NextResponse } from "next/server"; +import { createSdkMcpServer, query, tool, type Options } from "@anthropic-ai/claude-agent-sdk"; +import { getClaudeConfig } from "promptlayer/claude-agents"; + +export const runtime = "nodejs"; + +export async function POST(request: Request) { + try { + const body = await request.json(); + const prompt = String(body.prompt || "").trim(); + const model = String(body.model || "sonnet").trim(); + + if (!prompt) { + return NextResponse.json({ error: "Prompt is required." }, { status: 400 }); + } + + const anthropicKey = requiredEnv("ANTHROPIC_API_KEY"); + const promptLayerKey = requiredEnv("PROMPTLAYER_API_KEY"); + const promptLayer = getClaudeConfig({ apiKey: promptLayerKey }); + const labTools = createSdkMcpServer({ + name: "lab-tools", + version: "0.1.0", + alwaysLoad: true, + tools: [ + tool("random_number", "Generate a random integer from 1 to 100.", {}, async () => { + const value = Math.floor(Math.random() * 100) + 1; + return { + content: [{ type: "text", text: String(value) }], + structuredContent: { value } + }; + }) + ] + }); + + const options: Options = { + cwd: process.cwd(), + model, + maxTurns: 3, + plugins: [promptLayer.plugin], + mcpServers: { + "lab-tools": labTools + }, + allowedTools: ["mcp__lab-tools__random_number"], + env: { + ...process.env, + ANTHROPIC_API_KEY: anthropicKey, + ...promptLayer.env + } + }; + + const messages: unknown[] = []; + for await (const message of query({ prompt, options })) { + messages.push(message); + } + + return NextResponse.json({ + text: extractText(messages), + messages + }); + } catch (error) { + return NextResponse.json( + { error: error instanceof Error ? error.message : String(error) }, + { status: 500 } + ); + } +} + +function requiredEnv(name: "ANTHROPIC_API_KEY" | "PROMPTLAYER_API_KEY") { + const value = process.env[name]?.trim(); + if (!value) throw new Error(`Missing ${name}. Add it to .env and restart npm run dev.`); + return value; +} + +function extractText(value: unknown) { + const parts: string[] = []; + visit(value); + return parts.join("\n"); + + function visit(item: unknown) { + if (Array.isArray(item)) { + item.forEach(visit); + return; + } + if (!item || typeof item !== "object") return; + + const record = item as Record; + if (typeof record.text === "string") parts.push(record.text); + Object.values(record).forEach(visit); + } +} diff --git a/examples/claude-promptlayer-nextjs/app/globals.css b/examples/claude-promptlayer-nextjs/app/globals.css new file mode 100644 index 0000000..ba88091 --- /dev/null +++ b/examples/claude-promptlayer-nextjs/app/globals.css @@ -0,0 +1,132 @@ +* { + box-sizing: border-box; +} + +body { + margin: 0; + background: #f6f7f8; + color: #172026; + font-family: ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif; +} + +button, +input, +textarea { + font: inherit; +} + +button { + min-height: 38px; + border: 1px solid #d6dde2; + border-radius: 6px; + background: #fff; + padding: 0 14px; + cursor: pointer; +} + +button:disabled { + cursor: wait; + opacity: 0.7; +} + +.shell { + width: min(900px, calc(100vw - 32px)); + margin: 0 auto; + padding: 28px 0 40px; +} + +.panelHead { + display: flex; + align-items: flex-start; + justify-content: space-between; + gap: 16px; +} + +h1, +h2, +p { + margin: 0; +} + +h1 { + font-size: 28px; +} + +h2 { + font-size: 18px; +} + +p, +label { + color: #5d6b76; +} + +p { + margin-top: 6px; +} + +.panel, +.output { + border: 1px solid #d6dde2; + border-radius: 8px; + background: #fff; +} + +.panel, +.output { + margin-top: 18px; + padding: 18px; +} + +label { + display: grid; + gap: 6px; + margin-top: 14px; + font-size: 13px; +} + +label:first-child { + margin-top: 0; +} + +input, +textarea { + width: 100%; + border: 1px solid #d6dde2; + border-radius: 6px; + padding: 9px 10px; +} + +textarea { + min-height: 140px; + resize: vertical; +} + +.primary { + width: fit-content; + margin-top: 16px; + border-color: #0f766e; + background: #0f766e; + color: #fff; +} + +pre { + min-height: 260px; + max-height: 560px; + overflow: auto; + margin: 14px 0 0; + padding: 14px; + border-radius: 6px; + background: #101820; + color: #d7f7ef; + font-size: 13px; + line-height: 1.5; + white-space: pre-wrap; +} + +@media (max-width: 720px) { + .panelHead { + display: grid; + grid-template-columns: 1fr; + } +} diff --git a/examples/claude-promptlayer-nextjs/app/layout.tsx b/examples/claude-promptlayer-nextjs/app/layout.tsx new file mode 100644 index 0000000..dec73ab --- /dev/null +++ b/examples/claude-promptlayer-nextjs/app/layout.tsx @@ -0,0 +1,15 @@ +import type { Metadata } from "next"; +import "./globals.css"; + +export const metadata: Metadata = { + title: "Claude PromptLayer Next App", + description: "Local manual tester for Claude Agent SDK plus PromptLayer." +}; + +export default function RootLayout({ children }: { children: React.ReactNode }) { + return ( + + {children} + + ); +} diff --git a/examples/claude-promptlayer-nextjs/app/page.tsx b/examples/claude-promptlayer-nextjs/app/page.tsx new file mode 100644 index 0000000..349de43 --- /dev/null +++ b/examples/claude-promptlayer-nextjs/app/page.tsx @@ -0,0 +1,65 @@ +"use client"; + +import { useState } from "react"; + +const defaultPrompt = "Use the random_number tool, then tell me the number it returned."; + +export default function Home() { + const [model, setModel] = useState("sonnet"); + const [prompt, setPrompt] = useState(defaultPrompt); + const [output, setOutput] = useState("Ready."); + const [running, setRunning] = useState(false); + + async function runPrompt() { + setRunning(true); + setOutput("Running..."); + + try { + const response = await fetch("/api/run", { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ model, prompt }) + }); + const data = await response.json(); + + setOutput(JSON.stringify(data, null, 2)); + } catch (error) { + setOutput(JSON.stringify({ error: error instanceof Error ? error.message : String(error) }, null, 2)); + } finally { + setRunning(false); + } + } + + return ( +
+
+

Claude PromptLayer Lab

+

One route, one SDK call, PromptLayer tracing enabled through code.

+
+ +
+ + +
+ + + +""" + + +@app.post("/api/agent") +async def run_agent(request: Request) -> dict[str, str]: + body = await request.json() + message = body.get("message", "Say hello in one sentence.") + if not isinstance(message, str) or not message.strip(): + raise HTTPException(status_code=400, detail="message is required") + + processor = get_promptlayer_processor() + model = os.getenv("OPENAI_MODEL", DEFAULT_MODEL) + + agent = Agent( + name="PromptLayer Demo Agent", + instructions=( + "You are concise and practical. " + "If the user asks for weather, call get_demo_weather before answering. " + "If the user asks to embed text or asks about embedding metadata, call embed_text before answering." + ), + model=model, + model_settings=ModelSettings( + reasoning=Reasoning(summary="auto"), + response_include=["reasoning.encrypted_content"], + ), + tools=[get_demo_weather, embed_text], + ) + + try: + result = await Runner.run(agent, message) + except Exception as exc: + processor.force_flush() + raise HTTPException(status_code=500, detail=f"Agent request failed: {exc}") from exc + finally: + processor.force_flush() + + return { + "model": model, + "answer": str(result.final_output), + } diff --git a/examples/openai-agents-fastapi/pyproject.toml b/examples/openai-agents-fastapi/pyproject.toml new file mode 100644 index 0000000..061f828 --- /dev/null +++ b/examples/openai-agents-fastapi/pyproject.toml @@ -0,0 +1,17 @@ +[project] +name = "promptlayer-openai-agents-example" +version = "0.1.0" +description = "Minimal PromptLayer OpenAI Agents SDK tracing example" +requires-python = ">=3.9" +dependencies = [ + "fastapi>=0.115.0", + "promptlayer[openai-agents]", + "uvicorn[standard]>=0.30.0", +] + +[build-system] +requires = ["setuptools>=68"] +build-backend = "setuptools.build_meta" + +[tool.setuptools] +py-modules = ["app"] diff --git a/examples/pydantic-ai-chat/.env.example b/examples/pydantic-ai-chat/.env.example new file mode 100644 index 0000000..fb78200 --- /dev/null +++ b/examples/pydantic-ai-chat/.env.example @@ -0,0 +1,17 @@ +# Required for Anthropic model calls. +ANTHROPIC_API_KEY= + +# Required for OpenAI model calls and the default embeddings demo. +OPENAI_API_KEY= + +# Required for PromptLayer OTLP trace export. +PROMPTLAYER_API_KEY= +OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=https://api.promptlayer.com/v1/traces + +# Optional defaults. +OTEL_SERVICE_NAME=pydantic-ai-promptlayer-demo +PYDANTIC_AI_MODEL=anthropic:claude-sonnet-4-6 +PYDANTIC_AI_THINKING=medium +PYDANTIC_AI_EMBEDDING_MODEL=openai:text-embedding-3-small +SEND_TO_LOGFIRE=false +ENABLE_HTTPX_CAPTURE=false diff --git a/examples/pydantic-ai-chat/.gitignore b/examples/pydantic-ai-chat/.gitignore new file mode 100644 index 0000000..cfeae31 --- /dev/null +++ b/examples/pydantic-ai-chat/.gitignore @@ -0,0 +1,9 @@ +.env +.venv/ +uv.lock +__pycache__/ +.pytest_cache/ +.ruff_cache/ +.mypy_cache/ +.coverage +htmlcov/ diff --git a/examples/pydantic-ai-chat/README.md b/examples/pydantic-ai-chat/README.md new file mode 100644 index 0000000..943f978 --- /dev/null +++ b/examples/pydantic-ai-chat/README.md @@ -0,0 +1,100 @@ +# Pydantic AI Chat App With PromptLayer OTel + +Minimal local Pydantic AI web chat app that forwards Pydantic AI OpenTelemetry traces to PromptLayer. + +## Structure + +- `app.py` defines the chat agent, weather tool, dynamic embedding context, and telemetry setup. +- `embeddings.py` contains the native Pydantic AI embeddings demo. + +## Stack + +- Python 3.11+ +- `uv` +- `pydantic-ai-slim[web,openai,anthropic,logfire]` +- `logfire` +- OpenTelemetry OTLP HTTP exporter +- PromptLayer OTLP ingest + +## Setup + +```bash +cp .env.example .env +``` + +Fill in: + +- `ANTHROPIC_API_KEY` for the default Anthropic chat model +- `OPENAI_API_KEY` for OpenAI chat models and the default embeddings demo +- `PROMPTLAYER_API_KEY` +- `OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=https://api.promptlayer.com/v1/traces` +- `SEND_TO_LOGFIRE=true` if you also want to send traces to Logfire +- `PYDANTIC_AI_MODEL` to choose a Pydantic AI model +- `PYDANTIC_AI_THINKING` to control reasoning effort +- `PYDANTIC_AI_EMBEDDING_MODEL` if you want to override the embeddings demo model + +The app validates the API key that matches `PYDANTIC_AI_MODEL`. For example, +`anthropic:*` requires `ANTHROPIC_API_KEY`, while `openai:*`, +`openai-chat:*`, and `openai-responses:*` require `OPENAI_API_KEY`. + +Install dependencies: + +```bash +uv sync +``` + +Run the local web chat: + +```bash +uv run uvicorn app:app --port 7932 +``` + +Open the local URL printed by Uvicorn. + +## Telemetry + +By default, the app forwards Pydantic AI OpenTelemetry traces to PromptLayer at `https://api.promptlayer.com/v1/traces` when these env vars are configured: + +- `OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=https://api.promptlayer.com/v1/traces` +- `OTEL_EXPORTER_OTLP_HEADERS=X-API-KEY=` +- `OTEL_SERVICE_NAME=pydantic-ai-promptlayer-demo` + +Logfire export is optional. To also send traces to Logfire, set this in `.env`: + +```bash +SEND_TO_LOGFIRE=true +``` + +Leave `SEND_TO_LOGFIRE` unset to skip Logfire export. + +Then authenticate locally: + +```bash +uv run logfire auth +``` + +PromptLayer should show a trace after you send a chat message. Tool calls appear as child spans when the model invokes `get_weather`. + +## Embeddings Demo + +To see how native embeddings appear inside an agent run without using a tool, send a question like this: + +```text +Use embeddings to find the local demo topic closest to "how are traces exported to PromptLayer?" +``` + +```text +Use embeddings to search for "what part of the demo uses tools?" +``` + +```text +Use embeddings to find the closest topic for "how does semantic search compare text?" +``` + +PromptLayer should show the agent run with native Pydantic AI embedding spans and the chat model span. The embedding spans use `gen_ai.operation.name=embeddings`. + +Raw HTTP capture is disabled by default because it can include prompt and response content. Enable it only when you need provider-level request debugging: + +```bash +ENABLE_HTTPX_CAPTURE=true uv run uvicorn app:app --port 7932 +``` diff --git a/examples/pydantic-ai-chat/app.py b/examples/pydantic-ai-chat/app.py new file mode 100644 index 0000000..edfc708 --- /dev/null +++ b/examples/pydantic-ai-chat/app.py @@ -0,0 +1,150 @@ +from __future__ import annotations + +"""Local Pydantic AI web app wired to PromptLayer through OpenTelemetry.""" + +import os +from pathlib import Path + +import logfire +from dotenv import load_dotenv +from opentelemetry import trace +from pydantic_ai import Agent, RunContext +from pydantic_ai.models import parse_model_id + +from embeddings import search_demo_embeddings + + +PROMPTLAYER_SERVICE_NAME = "pydantic-ai-promptlayer-demo" +PROMPTLAYER_TRACE_ENDPOINT = "https://api.promptlayer.com/v1/traces" +BASE_DIR = Path(__file__).resolve().parent +ENV_FILE = BASE_DIR / ".env" +TRUTHY = {"1", "true", "yes"} +FALSY = {"0", "false", "no"} +THINKING_LEVELS = {"minimal", "low", "medium", "high", "xhigh"} +MODEL_API_KEYS = { + "anthropic": ("ANTHROPIC_API_KEY",), + "openai": ("OPENAI_API_KEY",), + "openai-chat": ("OPENAI_API_KEY",), + "openai-responses": ("OPENAI_API_KEY",), +} + + +load_dotenv(dotenv_path=ENV_FILE, override=True) + + +def env_enabled(name: str) -> bool: + return os.getenv(name, "").lower() in TRUTHY + + +def required_env(name: str) -> str: + value = os.getenv(name, "").strip() + if not value: + raise RuntimeError(f"Missing {name}. Add it to {ENV_FILE} or export it.") + return value + + +def thinking_setting() -> dict[str, bool | str]: + """Configure model reasoning through Pydantic AI's unified thinking setting.""" + raw_value = required_env("PYDANTIC_AI_THINKING").lower() + if raw_value in TRUTHY: + return {"thinking": True} + if raw_value in FALSY: + return {"thinking": False} + if raw_value in THINKING_LEVELS: + return {"thinking": raw_value} + raise ValueError( + "PYDANTIC_AI_THINKING must be one of: true, false, " + f"{', '.join(sorted(THINKING_LEVELS))}" + ) + + +def selected_model() -> str: + return required_env("PYDANTIC_AI_MODEL") + + +def validate_model_api_key(model: str) -> None: + provider, _model_name = parse_model_id(model) + if provider is None: + raise ValueError( + f"Could not infer provider for PYDANTIC_AI_MODEL={model!r}. " + "Use an explicit provider prefix like 'anthropic:' or 'openai:'." + ) + + required_keys = MODEL_API_KEYS.get(provider) + if not required_keys or any(os.getenv(key) for key in required_keys): + return + + formatted_keys = " or ".join(required_keys) + raise ValueError( + f"PYDANTIC_AI_MODEL={model!r} uses provider {provider!r}, " + f"so set {formatted_keys} in {ENV_FILE} or your shell environment." + ) + + +def configure_telemetry() -> None: + """Configure Pydantic AI instrumentation for PromptLayer and optional Logfire export.""" + if promptlayer_api_key := os.getenv("PROMPTLAYER_API_KEY"): + os.environ.setdefault( + "OTEL_EXPORTER_OTLP_TRACES_ENDPOINT", + PROMPTLAYER_TRACE_ENDPOINT, + ) + os.environ.setdefault( + "OTEL_EXPORTER_OTLP_HEADERS", + f"X-API-KEY={promptlayer_api_key}", + ) + os.environ.setdefault( + "OTEL_SERVICE_NAME", + PROMPTLAYER_SERVICE_NAME, + ) + + logfire.configure(send_to_logfire=env_enabled("SEND_TO_LOGFIRE")) + logfire.instrument_pydantic_ai() + + if env_enabled("ENABLE_HTTPX_CAPTURE"): + logfire.instrument_httpx(capture_all=True) + + +configure_telemetry() +MODEL = selected_model() +validate_model_api_key(MODEL) + +agent = Agent( + MODEL, + model_settings=thinking_setting(), + instructions=( + "You are a concise local demo assistant. Use tools when they help, " + "and keep answers practical." + ), +) + + +@agent.tool +def get_weather(_ctx: RunContext[None], city: str) -> str: + """Tiny demo tool that tags its span with PromptLayer prompt metadata.""" + span = trace.get_current_span() + span.set_attribute("promptlayer.prompt.name", "pydantic-chat-weather-demo") + span.set_attribute("promptlayer.prompt.label", "development") + return f"The weather in {city} is sunny and 72F." + + +@agent.instructions +async def embedding_context(ctx: RunContext[None]) -> str: + """Inject semantic-search context when the user asks about embeddings.""" + if not isinstance(ctx.prompt, str) or "embedding" not in ctx.prompt.lower(): + return "" + + result = await search_demo_embeddings(ctx.prompt) + return ( + "Embedding demo context: the user's prompt is closest to " + f"{result.matched_item.title!r} with similarity {result.similarity:.3f}. " + f"Topic text: {result.matched_item.text}" + ) + + +app = agent.to_web( + instructions=( + "This is a local development chat app. If the user asks about weather, " + "call the weather tool. If embedding demo context is available, use it " + "to answer the user." + ) +) diff --git a/examples/pydantic-ai-chat/embeddings.py b/examples/pydantic-ai-chat/embeddings.py new file mode 100644 index 0000000..556270c --- /dev/null +++ b/examples/pydantic-ai-chat/embeddings.py @@ -0,0 +1,101 @@ +from __future__ import annotations + +"""Small native Pydantic AI embeddings demo used by the chat agent.""" + +import os +from collections.abc import Sequence + +from pydantic import BaseModel, Field +from pydantic_ai import Embedder + + +DEFAULT_EMBEDDING_MODEL = "openai:text-embedding-3-small" + + +class DemoKnowledgeItem(BaseModel): + id: str + title: str + text: str + + +class EmbeddingSearchResult(BaseModel): + query: str + matched_item: DemoKnowledgeItem + similarity: float = Field(ge=-1, le=1) + embedding_model: str + embedded_document_count: int + + +DEMO_KNOWLEDGE_ITEMS = ( + DemoKnowledgeItem( + id="telemetry", + title="OpenTelemetry export", + text=( + "This app configures Pydantic AI instrumentation and exports " + "OpenTelemetry traces to PromptLayer through OTLP." + ), + ), + DemoKnowledgeItem( + id="weather_tool", + title="Weather tool", + text=( + "The weather tool is an agent tool that adds PromptLayer prompt " + "metadata to the current span and returns demo weather." + ), + ), + DemoKnowledgeItem( + id="embeddings", + title="Embedding search", + text=( + "Embedding search converts text into vectors, compares vectors with " + "cosine similarity, and finds semantically related content." + ), + ), +) + + +_embedder: Embedder | None = None + + +def get_embedder() -> Embedder: + """Create the instrumented embedder lazily so env vars load before use.""" + global _embedder + + if _embedder is None: + _embedder = Embedder( + os.getenv("PYDANTIC_AI_EMBEDDING_MODEL", DEFAULT_EMBEDDING_MODEL), + instrument=True, + ) + + return _embedder + + +def cosine_similarity(left: Sequence[float], right: Sequence[float]) -> float: + dot_product = sum(a * b for a, b in zip(left, right)) + left_norm = sum(value * value for value in left) ** 0.5 + right_norm = sum(value * value for value in right) ** 0.5 + if left_norm == 0 or right_norm == 0: + return 0 + return dot_product / (left_norm * right_norm) + + +async def search_demo_embeddings(query: str) -> EmbeddingSearchResult: + """Embed the query and local demo topics, then return the closest topic.""" + embedder = get_embedder() + query_result = await embedder.embed_query(query) + documents_result = await embedder.embed_documents([item.text for item in DEMO_KNOWLEDGE_ITEMS]) + query_embedding = query_result.embeddings[0] + + scored_items = [ + (cosine_similarity(query_embedding, document_embedding), item) + for item, document_embedding in zip(DEMO_KNOWLEDGE_ITEMS, documents_result.embeddings) + ] + similarity, matched_item = max(scored_items, key=lambda scored_item: scored_item[0]) + + return EmbeddingSearchResult( + query=query, + matched_item=matched_item, + similarity=similarity, + embedding_model=query_result.model_name, + embedded_document_count=len(DEMO_KNOWLEDGE_ITEMS), + ) diff --git a/examples/pydantic-ai-chat/pyproject.toml b/examples/pydantic-ai-chat/pyproject.toml new file mode 100644 index 0000000..1c93270 --- /dev/null +++ b/examples/pydantic-ai-chat/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "pydantic-ai-chat" +version = "0.1.0" +description = "Minimal Pydantic AI web chat app exporting OpenTelemetry traces to PromptLayer." +readme = "README.md" +requires-python = ">=3.11" +dependencies = [ + "logfire>=4.15.1", + "opentelemetry-exporter-otlp-proto-http>=1.38.0", + "pydantic-ai-slim[anthropic,logfire,openai,web]>=1.25.0", + "python-dotenv>=1.2.1", +] + +[tool.uv] +package = false From 556be2ca12f577cb932bea841e4881a194ff7960 Mon Sep 17 00:00:00 2001 From: NoamBenS Date: Wed, 20 May 2026 18:42:27 -0400 Subject: [PATCH 3/4] removed package-locks --- .../package-lock.json | 5289 ----------- .../langchain-itinerary/package-lock.json | 8107 ----------------- 2 files changed, 13396 deletions(-) delete mode 100644 examples/claude-promptlayer-nextjs/package-lock.json delete mode 100644 examples/langchain-itinerary/package-lock.json diff --git a/examples/claude-promptlayer-nextjs/package-lock.json b/examples/claude-promptlayer-nextjs/package-lock.json deleted file mode 100644 index 26d31e6..0000000 --- a/examples/claude-promptlayer-nextjs/package-lock.json +++ /dev/null @@ -1,5289 +0,0 @@ -{ - 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} - } - } -} From 59c1b47d589bc4e195512cad0b46d5e5c9a432cf Mon Sep 17 00:00:00 2001 From: NoamBenS Date: Wed, 20 May 2026 18:51:46 -0400 Subject: [PATCH 4/4] renamed folder --- examples/openai-agents-fastapi/app.py | 219 ------------------ .../.env.example | 0 .../.gitignore | 0 .../README.md | 18 +- examples/openai-agents-sdk/main.py | 103 ++++++++ .../pyproject.toml | 5 +- 6 files changed, 112 insertions(+), 233 deletions(-) delete mode 100644 examples/openai-agents-fastapi/app.py rename examples/{openai-agents-fastapi => openai-agents-sdk}/.env.example (100%) rename examples/{openai-agents-fastapi => openai-agents-sdk}/.gitignore (100%) rename examples/{openai-agents-fastapi => openai-agents-sdk}/README.md (60%) create mode 100644 examples/openai-agents-sdk/main.py rename examples/{openai-agents-fastapi => openai-agents-sdk}/pyproject.toml (80%) diff --git a/examples/openai-agents-fastapi/app.py b/examples/openai-agents-fastapi/app.py deleted file mode 100644 index 07d42b2..0000000 --- a/examples/openai-agents-fastapi/app.py +++ /dev/null @@ -1,219 +0,0 @@ -import os -from typing import Any - -from agents import Agent, ModelSettings, Runner, function_tool -from fastapi import FastAPI, HTTPException, Request -from fastapi.responses import HTMLResponse -from openai import OpenAI -from openai.types.shared.reasoning import Reasoning -from promptlayer.integrations.openai_agents import instrument_openai_agents - - -DEFAULT_MODEL = "gpt-5" -EMBEDDING_MODEL = "text-embedding-3-small" - -app = FastAPI(title="PromptLayer OpenAI Agents Example") -promptlayer_processor: Any = None -openai_client = OpenAI() - - -@function_tool -def get_demo_weather(city: str) -> str: - """Return demo weather for a city.""" - forecasts = { - "new york": "New York is 61F and cloudy.", - "san francisco": "San Francisco is 58F and breezy.", - "tokyo": "Tokyo is 72F with light rain.", - "london": "London is 55F and overcast.", - } - return forecasts.get(city.strip().lower(), f"{city} is 68F and clear in the demo forecast.") - - -@function_tool -def embed_text(text: str) -> str: - """Embed text with OpenAI embeddings and return metadata about the result.""" - response = openai_client.embeddings.create( - model=EMBEDDING_MODEL, - input=text, - ) - embedding = response.data[0].embedding - return ( - f"embedding model: {response.model}; " - f"dimensions: {len(embedding)}; " - f"prompt tokens: {response.usage.prompt_tokens}" - ) - - -def require_env(name: str) -> str: - value = os.getenv(name) - if not value: - raise HTTPException(status_code=500, detail=f"Missing required environment variable: {name}") - return value - - -def get_promptlayer_processor(): - global promptlayer_processor - - require_env("OPENAI_API_KEY") - require_env("PROMPTLAYER_API_KEY") - - if promptlayer_processor is None: - promptlayer_processor = instrument_openai_agents() - - return promptlayer_processor - - -@app.on_event("shutdown") -def shutdown_promptlayer_processor() -> None: - if promptlayer_processor is not None: - promptlayer_processor.force_flush() - promptlayer_processor.shutdown() - - -@app.get("/", response_class=HTMLResponse) -def index() -> str: - return """ - - - - - - PromptLayer OpenAI Agents Example - - - -
-

PromptLayer OpenAI Agents Example

-

Run an OpenAI Agents SDK agent with PromptLayer tracing enabled.

-
- - -
-

Response

- 
Waiting for a request...
-
- - - -""" - - -@app.post("/api/agent") -async def run_agent(request: Request) -> dict[str, str]: - body = await request.json() - message = body.get("message", "Say hello in one sentence.") - if not isinstance(message, str) or not message.strip(): - raise HTTPException(status_code=400, detail="message is required") - - processor = get_promptlayer_processor() - model = os.getenv("OPENAI_MODEL", DEFAULT_MODEL) - - agent = Agent( - name="PromptLayer Demo Agent", - instructions=( - "You are concise and practical. " - "If the user asks for weather, call get_demo_weather before answering. " - "If the user asks to embed text or asks about embedding metadata, call embed_text before answering." - ), - model=model, - model_settings=ModelSettings( - reasoning=Reasoning(summary="auto"), - response_include=["reasoning.encrypted_content"], - ), - tools=[get_demo_weather, embed_text], - ) - - try: - result = await Runner.run(agent, message) - except Exception as exc: - processor.force_flush() - raise HTTPException(status_code=500, detail=f"Agent request failed: {exc}") from exc - finally: - processor.force_flush() - - return { - "model": model, - "answer": str(result.final_output), - } diff --git a/examples/openai-agents-fastapi/.env.example b/examples/openai-agents-sdk/.env.example similarity index 100% rename from examples/openai-agents-fastapi/.env.example rename to examples/openai-agents-sdk/.env.example diff --git a/examples/openai-agents-fastapi/.gitignore b/examples/openai-agents-sdk/.gitignore similarity index 100% rename from examples/openai-agents-fastapi/.gitignore rename to examples/openai-agents-sdk/.gitignore diff --git a/examples/openai-agents-fastapi/README.md b/examples/openai-agents-sdk/README.md similarity index 60% rename from examples/openai-agents-fastapi/README.md rename to examples/openai-agents-sdk/README.md index e7abf3f..f16b2e3 100644 --- a/examples/openai-agents-fastapi/README.md +++ b/examples/openai-agents-sdk/README.md @@ -1,8 +1,8 @@ -# PromptLayer OpenAI Agents Web Example +# PromptLayer OpenAI Agents SDK Example -Minimal FastAPI app using PromptLayer's OpenAI Agents SDK tracing integration. +Minimal CLI example using PromptLayer's OpenAI Agents SDK tracing integration. -The app follows the PromptLayer OpenAI Agents SDK integration pattern and exports traces to `https://api.promptlayer.com/v1/traces`: +This example follows the PromptLayer OpenAI Agents SDK integration pattern and exports traces to `https://api.promptlayer.com/v1/traces`: ```python from agents import Agent, Runner @@ -23,7 +23,7 @@ processor.force_flush() ## Setup ```bash -cd examples/openai-agents-fastapi +cd examples/openai-agents-sdk python -m venv .venv source .venv/bin/activate pip install -e . @@ -33,17 +33,13 @@ cp .env.example .env Edit `.env`, then run: ```bash -.venv/bin/uvicorn app:app --port 8000 --env-file .env +python main.py ``` -Open the local URL printed by Uvicorn in your browser. - -You can also call the app route directly: +You can also pass a custom message: ```bash -curl -X POST /api/agent \ - -H "Content-Type: application/json" \ - -d '{"message":"Use the embedding tool to embed exactly this text: PromptLayer OpenAI embedding trace probe. Then report the embedding dimensions and token count."}' +python main.py "What is the weather in Tokyo?" ``` ## Notes diff --git a/examples/openai-agents-sdk/main.py b/examples/openai-agents-sdk/main.py new file mode 100644 index 0000000..a875047 --- /dev/null +++ b/examples/openai-agents-sdk/main.py @@ -0,0 +1,103 @@ +import asyncio +import os +from argparse import ArgumentParser + +from agents import Agent, ModelSettings, Runner, function_tool +from dotenv import load_dotenv +from openai import OpenAI +from openai.types.shared.reasoning import Reasoning +from promptlayer.integrations.openai_agents import instrument_openai_agents + + +DEFAULT_MODEL = "gpt-5" +DEFAULT_MESSAGE = ( + "Use the embedding tool to embed exactly this text: " + "PromptLayer OpenAI embedding trace probe. Then report the embedding " + "dimensions and token count." +) +EMBEDDING_MODEL = "text-embedding-3-small" + +openai_client = OpenAI() + + +@function_tool +def get_demo_weather(city: str) -> str: + """Return demo weather for a city.""" + forecasts = { + "new york": "New York is 61F and cloudy.", + "san francisco": "San Francisco is 58F and breezy.", + "tokyo": "Tokyo is 72F with light rain.", + "london": "London is 55F and overcast.", + } + return forecasts.get(city.strip().lower(), f"{city} is 68F and clear in the demo forecast.") + + +@function_tool +def embed_text(text: str) -> str: + """Embed text with OpenAI embeddings and return metadata about the result.""" + response = openai_client.embeddings.create( + model=EMBEDDING_MODEL, + input=text, + ) + embedding = response.data[0].embedding + return ( + f"embedding model: {response.model}; " + f"dimensions: {len(embedding)}; " + f"prompt tokens: {response.usage.prompt_tokens}" + ) + + +def required_env(name: str) -> str: + value = os.getenv(name, "").strip() + if not value: + raise RuntimeError(f"Missing {name}. Add it to .env or export it.") + return value + + +async def run_agent(message: str) -> str: + required_env("OPENAI_API_KEY") + required_env("PROMPTLAYER_API_KEY") + + processor = instrument_openai_agents() + model = os.getenv("OPENAI_MODEL", DEFAULT_MODEL) + agent = Agent( + name="PromptLayer Demo Agent", + instructions=( + "You are concise and practical. " + "If the user asks for weather, call get_demo_weather before answering. " + "If the user asks to embed text or asks about embedding metadata, call embed_text before answering." + ), + model=model, + model_settings=ModelSettings( + reasoning=Reasoning(summary="auto"), + response_include=["reasoning.encrypted_content"], + ), + tools=[get_demo_weather, embed_text], + ) + + try: + result = await Runner.run(agent, message) + return str(result.final_output) + finally: + processor.force_flush() + processor.shutdown() + + +def parse_args() -> str: + parser = ArgumentParser(description="Run an OpenAI Agents SDK example with PromptLayer tracing.") + parser.add_argument( + "message", + nargs="?", + default=DEFAULT_MESSAGE, + help="Message to send to the agent.", + ) + return parser.parse_args().message + + +def main() -> None: + load_dotenv() + print(asyncio.run(run_agent(parse_args()))) + + +if __name__ == "__main__": + main() diff --git a/examples/openai-agents-fastapi/pyproject.toml b/examples/openai-agents-sdk/pyproject.toml similarity index 80% rename from examples/openai-agents-fastapi/pyproject.toml rename to examples/openai-agents-sdk/pyproject.toml index 061f828..a4f1caa 100644 --- a/examples/openai-agents-fastapi/pyproject.toml +++ b/examples/openai-agents-sdk/pyproject.toml @@ -4,9 +4,8 @@ version = "0.1.0" description = "Minimal PromptLayer OpenAI Agents SDK tracing example" requires-python = ">=3.9" dependencies = [ - "fastapi>=0.115.0", "promptlayer[openai-agents]", - "uvicorn[standard]>=0.30.0", + "python-dotenv>=1.2.0", ] [build-system] @@ -14,4 +13,4 @@ requires = ["setuptools>=68"] build-backend = "setuptools.build_meta" [tool.setuptools] -py-modules = ["app"] +py-modules = ["main"]