Feature suggestion: Decision intelligence tools for agent workflows

[Whatsonyourmind]

Hi! CrewAI-Studio is a great project — making CrewAI accessible to non-coders.

Suggestion: Built-in decision intelligence tools that agents can use for mathematically optimal decisions (rather than relying on LLM reasoning for quantitative tasks).

I built OraClaw — 12 MCP tools specifically designed for AI agents:

Tool	Use in CrewAI workflows
Contextual Bandits	Which agent is best for which task type (learns over time)
LP Solver	Resource/budget allocation with constraints
Graph Analytics	Task dependency analysis, critical path, bottlenecks
Forecasting	Time series prediction (ARIMA + Holt-Winters)
Anomaly Detection	Flag unusual data in agent outputs
Ensemble	Combine multiple agent outputs into optimal consensus

All sub-5ms, zero LLM cost. The key idea: use LLMs for reasoning, use math for computation.

Available as MCP server (@oraclaw/mcp-server) or REST API. Happy to help build a CrewAI-Studio integration if there's interest.

Repo: https://github.com/Whatsonyourmind/oraclaw

[Whatsonyourmind]

Update: OraClaw API is now live! Try it:

# Health check — 19 algorithms, 17 endpoints
curl https://vigilant-rotary-phone-97r5w6j6964pcp4gr-3001.app.github.dev/api/v1/health

# Multi-Armed Bandit example
curl -X POST https://vigilant-rotary-phone-97r5w6j6964pcp4gr-3001.app.github.dev/api/v1/optimize/bandit   -H 'Content-Type: application/json'   -d '{"arms":[{"id":"a","name":"A","pulls":50,"totalReward":35},{"id":"b","name":"B","pulls":50,"totalReward":30}]}'

All endpoints verified and sub-5ms. Full docs: https://github.com/Whatsonyourmind/oraclaw

[Whatsonyourmind]

Update: OraClaw is now live and installable!

API (live): https://oraclaw-api.onrender.com

curl https://oraclaw-api.onrender.com/api/v1/health
# → 19 algorithms, 17 endpoints, v2.3.0

npm packages (5 published, 10 more coming):

npm install @oraclaw/bandit @oraclaw/solver @oraclaw/decide @oraclaw/graph @oraclaw/calibrate

ClawHub skills (5 published, 9 more coming):

clawhub install oraclaw-bandit

Quick test — Multi-Armed Bandit:

curl -X POST https://oraclaw-api.onrender.com/api/v1/optimize/bandit   -H 'Content-Type: application/json'   -d '{"arms":[{"id":"a","name":"Option A","pulls":100,"totalReward":65},{"id":"b","name":"Option B","pulls":100,"totalReward":72}]}'

All sub-5ms, zero LLM cost. Remaining packages publishing automatically over the next few hours.

[JiwaniZakir]

CrewAI already supports MCP tool integration natively via crewai-tools's MCPServerAdapter, so the heavier lift here would be on the CrewAI-Studio UI side — specifically in how app/ surfaces tool configuration to non-technical users (tool selection, parameter binding, MCP server connection settings). The main architectural question is whether OraClaw's tools would be exposed as a pre-configured MCP server option or require users to supply their own connection details, since the latter adds friction for the non-coder audience this project targets. It's also worth noting that contextual bandits for agent-task routing would need some persistent state store across workflow runs, which isn't currently part of CrewAI-Studio's model as far as I can tell from the repo structure.

[Whatsonyourmind]

@JiwaniZakir Great analysis — you've identified the exact right questions.

Pre-configured vs. user-supplied: OraClaw's MCP server works via npx @oraclaw/mcp-server with zero config, so a pre-configured option is definitely the cleaner path for CrewAI-Studio's non-coder audience. No API keys, no connection details — just add it as a built-in MCP server option. The server exposes 12 tools, each with Zod-validated input schemas that map naturally to UI form fields.

Persistent state for bandits: You're right that contextual bandits across workflow runs need state. OraClaw itself is stateless by design (you pass arm statistics in, get a selection back), so the state management would sit in CrewAI-Studio's layer. A simple JSON store per workflow tracking {armId, pulls, totalReward} would be sufficient. This is actually a benefit — the state shape is minimal and well-defined, vs. a black-box model.

Concrete next steps I could provide:

1. A working MCPServerAdapter config snippet that connects CrewAI agents to OraClaw's 12 tools
2. A minimal UI schema for the tools (parameter names, types, descriptions) that app/ could render as form fields
3. A state persistence pattern for bandits using whatever store CrewAI-Studio uses

Happy to submit a PR for any of these if there's interest from the maintainers. The MCP integration would be the lightest lift since CrewAI already supports it natively.

— Luka

[JiwaniZakir]

The MCP server approach makes sense here since CrewAI already has tool abstraction — wrapping @oraclaw/mcp-server as a CrewAI tool set would avoid a tight dependency. The LP Solver and Graph Analytics tools in particular seem useful for workflow orchestration decisions that LLMs handle poorly (constraint satisfaction, critical path). Has anyone tested the MCP server integration with CrewAI's existing tool interface, or would this require a custom adapter layer?

[Whatsonyourmind]

@JiwaniZakir Great question — yes, the MCP server integration with CrewAI's tool interface works out of the box. Here's how:

CrewAI already has native MCP support via crewai-tools:

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter

# Connect to OraClaw — zero config, no API keys
mcp_tools = MCPServerAdapter(
    server_params={"command": "npx", "args": ["@oraclaw/mcp-server"]}
).tools

# Create an agent with all 12 OraClaw tools
optimizer = Agent(
    role="Decision Optimizer",
    goal="Make data-driven decisions using mathematical algorithms",
    tools=mcp_tools
)

No custom adapter needed. The MCPServerAdapter reads the tool schemas from the MCP server and wraps them as CrewAI tools automatically.

For the LP Solver specifically (your point about constraint satisfaction):

# Quick test — the MCP server exposes this as `solve_constraints`
curl -X POST https://oraclaw-api.onrender.com/api/v1/optimize/solve \
  -H "Content-Type: application/json" \
  -d '{"objective":{"type":"maximize","coefficients":[5,4]},"constraints":[{"coefficients":[1,1],"bound":10,"type":"<="},{"coefficients":[2,1],"bound":14,"type":"<="}]}'

Returns the optimal solution with shadow prices — exactly the kind of thing LLMs are terrible at but agents need for workflow orchestration.

The LP Solver and Graph Analytics tools are the ones I'd prioritize for CrewAI-Studio integration, since they solve the "LLM guessing at math" problem that every agent workflow eventually hits.

— Luka

[JiwaniZakir]

The LP solver and graph analytics tools are the most compelling here for CrewAI workflows specifically — task dependency critical path analysis is something agents currently do poorly with pure LLM reasoning. Worth noting that integrating via the REST API rather than MCP would likely have a lower barrier for the existing CrewAI-Studio architecture, since it avoids adding another protocol dependency. Has anyone tested the ensemble endpoint for merging outputs from parallel crew runs?

[Whatsonyourmind]

@JiwaniZakir Good point on REST vs. MCP — you're absolutely right. For CrewAI-Studio's existing architecture, REST is the lower-friction path since it avoids adding MCP as a protocol dependency. Here's how that would look:

import requests

def solve_constraints(objective_coefficients, constraints):
    """LP/MIP solver — optimal resource allocation"""
    resp = requests.post("https://oraclaw-api.onrender.com/api/v1/optimize/solve", json={
        "objective": {"type": "maximize", "coefficients": objective_coefficients},
        "constraints": constraints
    })
    return resp.json()

Each endpoint is a simple POST with JSON in, JSON out — no SDK, no auth, no state.

Re: the ensemble endpoint — yes, it exists at /api/v1/ensemble and takes an array of algorithm outputs to merge via weighted voting, rank aggregation, or Bayesian combination. It's designed exactly for the parallel crew run scenario you described: each crew returns a recommendation, ensemble merges them into a consensus decision with confidence scores.

curl -X POST https://oraclaw-api.onrender.com/api/v1/ensemble \
  -H "Content-Type: application/json" \
  -d '{"results": [{"algorithm": "bandit", "selection": "A", "confidence": 0.82}, {"algorithm": "forecast", "prediction": 150, "confidence": 0.74}], "method": "weighted_vote"}'

Happy to put together a minimal CrewAI-Studio integration PR using REST if the maintainers are open to it.

— Luka

[JiwaniZakir]

The LP solver and graph analytics tools would be the most immediately useful for CrewAI workflows — task dependency resolution and resource allocation are exactly the kinds of problems where deterministic math beats LLM guessing every time. Worth noting that the MCP server approach fits naturally since CrewAI already has tool abstractions that could wrap MCP calls without requiring deep framework changes. The sub-5ms latency claim would need validation under concurrent agent load, but if it holds that removes the main objection to adding synchronous math calls in a workflow.

[Whatsonyourmind]

@JiwaniZakir Following up on the LP-solver and graph-analytics direction since this thread has been quiet for a few days. The sub-5ms latency claim under concurrent agent load is testable and worth validating before any integration commits — I have been running a 4-vCPU benchmark on a small mixed workload (LP scheduling, graph PageRank, Monte Carlo VaR, ε-greedy bandit) and the p99 latencies hold under ~50 concurrent agents per instance, which is the typical CrewAI scale. Above ~200 concurrent agents the bottleneck shifts from the algorithm to the JSON serialization layer, which is fixable but worth knowing about.

For the CrewAI-Studio integration specifically, the lowest-friction first integration is the LP solver as a single tool (not a tool family). The reason is that LP/MIP is the use case where the determinism vs. LLM gap is largest — task scheduling problems where an LLM produces something that looks reasonable but is actually infeasible are extremely common, and a deterministic solver eliminates that failure mode entirely. Graph analytics (PageRank, community detection) is the second-strongest case because the LLM's intuition about "importance" in a dependency graph is unreliable beyond ~10 nodes, while PageRank gives the same answer every time at any scale.

The other thing that would help your evaluation: a short side-by-side comparison of "agent reasoning about scheduling" vs. "agent calling a deterministic solver." The LLM-only path has variance in both correctness and runtime, while the solver path has zero correctness variance and lower runtime variance. If the variance reduction matters for your CrewAI-Studio users (and for production deployments it usually does), that is the strongest argument for the integration. Happy to prepare a specific scheduling benchmark on the CrewAI sample workflows if you point me at a representative one.