How should crewai-multi-agent-research be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this skill.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

Crawler Summary

crewai-multi-agent-research answer-first brief

This repo demonstrates a CrewAI-based research assistant system that uses multiple specialized agents and tasks to retrieve, summarize, and synthesize information from various sources. Designed for academic and financial domains. Multi-Agent Financial Research (CrewAI) A **CrewAI-based multi-agent research system** for financial and macroeconomic analysis. This project demonstrates how **autonomous agents**, coordinated via **CrewAI**, can collaboratively extract market data, enrich it with macroeconomic and weather signals, and produce **ML-ready datasets** for quantitative research. Designed for **financial research, data science, and appli Capability contract not published. No trust telemetry is available yet. Last updated 5/12/2026.

Freshness

Last checked 5/12/2026

Best For

crewai-multi-agent-research is best for crewai, multi-agent workflows where OpenClaw compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, GITHUB OPENCLEW, runtime-metrics, public facts pack

Card Facts Snapshot Contract Trust

Agent DossierGitHubSafety: 66/100

crewai-multi-agent-research

OpenClawself-declared

Public facts

Change events

Artifacts

Freshness

May 12, 2026

Verifiededitorial-contentNo verified compatibility signals

Capability contract not published. No trust telemetry is available yet. Last updated 5/12/2026.

Trust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

May 12, 2026

Vendor

Crazy Horse

Artifacts

Benchmarks

Last release

Unpublished

Executive Summary

Key links, install path, and a quick operational read before the deeper crawl record.

Verifiededitorial-content

Summary

Capability contract not published. No trust telemetry is available yet. Last updated 5/12/2026.

View Source

Setup snapshot

git clone https://github.com/Crazy-Horse/crewai-multi-agent-research.git

1
Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
2
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Evidence Ledger

Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.

Verifiededitorial-content

Vendor (1)

Vendor

Crazy Horse

profilemedium

Observed May 12, 2026Source link Provenance

Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium

Observed May 12, 2026Source link Provenance

Security (1)

Handshake status

UNKNOWN

trustmedium

Observed unknownSource link Provenance

Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium

Observed Apr 15, 2026Source link Provenance

Release & Crawl Timeline

Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.

Self-declaredagent-index

Docs Update

Docs refreshed: Sign in to GitHub · GitHub

search_documentmedium

Fresh crawlable documentation was indexed for the official domain.

Observed Apr 15, 2026

Artifacts Archive

Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.

Self-declaredGITHUB OPENCLEW

Extracted files

Examples

Snippets

Languages

python

Executable Examples

bash

curl -LsSf https://astral.sh/uv/install.sh | sh

bash

curl -LsSf https://astral.sh/uv/install.sh | sh

bash

uv --version

bash

git clone <YOUR_CREWAI_REPO_URL>
cd multi-agent-research-crewai

bash

uv venv
uv sync

bash

source .venv/bin/activate

Docs & README

Full documentation captured from public sources, including the complete README when available.

Self-declaredGITHUB OPENCLEW

Docs source

GITHUB OPENCLEW

Editorial quality

ready

Full README

Multi-Agent Financial Research (CrewAI)

A CrewAI-based multi-agent research system for financial and macroeconomic analysis. This project demonstrates how autonomous agents, coordinated via CrewAI, can collaboratively extract market data, enrich it with macroeconomic and weather signals, and produce ML-ready datasets for quantitative research.

Designed for financial research, data science, and applied AI engineering use cases.

One-Sentence Audience Framing

For data scientists, quantitative analysts, and AI architects who want to build production-grade financial research agents using modern multi-agent orchestration.

Business Impact

This system enables:

Faster macro + market research cycles
Automated feature engineering pipelines
Reproducible, auditable datasets for modeling
Reduced analyst toil through agent specialization

It is especially valuable for commodities, macro strategy, risk analysis, and alternative data research.

Architecture Overview (CrewAI)

In this implementation:

Agents are role-driven (not graph-driven)
Tasks are executed sequentially
Tools encapsulate deterministic data operations
State flows implicitly via task outputs

Agent Roles

Researcher — frames the problem and research scope
Reporting Analyst — executes data extraction and dataset construction

Tools

Yahoo Finance (OHLCV)
FRED + World Bank (macro & FX)
Open-Meteo (multi-region weather)
Dataset builder (feature engineering + targets)

Key Features

CrewAI multi-agent orchestration
Role-based agent design
Deterministic tool-driven data extraction
Multi-region weather enrichment (Open-Meteo)
Macro + FX integration (FRED + World Bank)
ML-ready feature engineering
Leakage-safe multi-horizon targets
Fully reproducible execution

Technologies Used

CrewAI
Python 3.11+
uv (dependency & environment management)
Yahoo Finance
FRED API
World Bank API
Open-Meteo API
Pandas / NumPy

Quick Start (Using `uv` – Recommended)

This project does not use pip. All dependencies are managed via uv.

1. Install `uv`

curl -LsSf https://astral.sh/uv/install.sh | sh

Restart your shell, then verify:

uv --version

2. Clone the Repository

git clone <YOUR_CREWAI_REPO_URL>
cd multi-agent-research-crewai

3. Create and Sync the Virtual Environment

uv venv
uv sync

Activate the environment:

source .venv/bin/activate

4. Configure Environment Variables

Create a .env file in the project root:

FRED_API_KEY=your_fred_api_key
OPENAI_API_KEY=your_openai_api_key

Optional:

ANTHROPIC_API_KEY=your_anthropic_api_key

5. Run the Crew

uv run python main.py

Or, if exposed as a CLI:

uv run crewai run

The crew executes tasks sequentially:

Research framing
Market data extraction
Macro + FX enrichment
Weather enrichment (6 coffee regions)
Feature engineering
Dataset generation

Output Artifacts

Generated outputs include:

data/raw/
data/processed/features_daily.csv
data/processed/features_daily_data_dictionary.md

These datasets are:

Time-aligned
Leakage-safe
Suitable for linear or nonlinear models
Reproducible across runs

How This Differs from the LangGraph Version

| Dimension | CrewAI | LangGraph | | -------------------- | -------------------- | ------------------- | | Control Flow | Task-based | Graph-based | | State | Implicit | Explicit | | Determinism | Medium | High | | Debuggability | Moderate | Strong | | Production Readiness | Fast prototyping | Strong pipelines | | Best For | Exploratory research | Regulated workflows |

This repo exists specifically to compare CrewAI vs LangGraph using the same financial research problem.

Use Cases

Commodity price modeling
Macro-driven return prediction
Weather-impact analysis
Research automation
Feature engineering pipelines
AI-assisted financial research teams

Why `uv`

This project uses uv instead of pip because:

Faster dependency resolution
Deterministic installs
Built-in virtual environments
Cleaner CI/CD
Modern Python best practice

Repository Structure

.
├── crew.py
├── main.py
├── tasks.yaml
├── tools/
│   ├── yahoo_finance_tools.py
│   ├── fred_tools.py
│   ├── open_meteo_tools.py
│   └── dataset_build_tools.py
├── data/
│   ├── raw/
│   └── processed/
├── pyproject.toml
└── README.md

Related Projects

LangGraph version: LANGGRAPH_REPO_URL
Blog post: BLOG_POST
Architecture diagrams: <DIAGRAMS_URL>

License

MIT License

Contract & API

Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.

MissingGITHUB OPENCLEW

Endpoints

Dossier API Snapshot API Contract API Trust API

Contract coverage

Status

missing

Auth

None

Streaming

Data region

Unspecified

Protocol support

OpenClaw: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.

Invocation examples

curl -s "https://www.xpersona.co/api/v1/agents/crewai-crazy-horse-crewai-multi-agent-research/snapshot"

curl -s "https://www.xpersona.co/api/v1/agents/crewai-crazy-horse-crewai-multi-agent-research/contract"

curl -s "https://www.xpersona.co/api/v1/agents/crewai-crazy-horse-crewai-multi-agent-research/trust"

Reliability & Benchmarks

Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.

Missingruntime-metrics

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

Do not use if

Contract metadata is missing or unavailable for deterministic execution.

No benchmark suites or observed failure patterns are available.

Media & Demo

Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.

Missingno-media

No screenshots, media assets, or demo links are available.

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Machine Appendix

Contract JSON

{
  "contractStatus": "missing",
  "authModes": [],
  "requires": [],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": null,
  "outputSchemaRef": null,
  "dataRegion": null,
  "contractUpdatedAt": null,
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://www.xpersona.co/api/v1/agents/crewai-crazy-horse-crewai-multi-agent-research/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/crewai-crazy-horse-crewai-multi-agent-research/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/crewai-crazy-horse-crewai-multi-agent-research/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-crazy-horse-crewai-multi-agent-research/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-crazy-horse-crewai-multi-agent-research/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-crazy-horse-crewai-multi-agent-research/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_OPENCLEW",
      "generatedAt": "2026-10-09T21:40:39.308Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "rows": [
    {
      "key": "OPENCLEW",
      "type": "protocol",
      "support": "unknown",
      "confidenceSource": "profile",
      "notes": "Listed on profile"
    },
    {
      "key": "crewai",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "multi-agent",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    }
  ],
  "flattenedTokens": "protocol:OPENCLEW|unknown|profile capability:crewai|supported|profile capability:multi-agent|supported|profile"
}

Facts JSON

[
  {
    "factKey": "vendor",
    "label": "Vendor",
    "value": "Crazy Horse",
    "category": "vendor",
    "href": "https://github.com/Crazy-Horse/crewai-multi-agent-research",
    "sourceUrl": "https://github.com/Crazy-Horse/crewai-multi-agent-research",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-05-12T06:46:15.100Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "protocols",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "category": "compatibility",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-crazy-horse-crewai-multi-agent-research/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-crazy-horse-crewai-multi-agent-research/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-05-12T06:46:15.100Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "docs_crawl",
    "label": "Crawlable docs",
    "value": "6 indexed pages on the official domain",
    "category": "integration",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "handshake_status",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "category": "security",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-crazy-horse-crewai-multi-agent-research/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-crazy-horse-crewai-multi-agent-research/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true,
    "metadata": {}
  }
]

Change Events JSON

[
  {
    "eventType": "docs_update",
    "title": "Docs refreshed: Sign in to GitHub · GitHub",
    "description": "Fresh crawlable documentation was indexed for the official domain.",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true,
    "metadata": {}
  }
]

crewai-multi-agent-research answer-first brief

Executive Summary

Evidence Ledger

Release & Crawl Timeline

Artifacts Archive

Docs & README

Multi-Agent Financial Research (CrewAI)

One-Sentence Audience Framing

Business Impact

Architecture Overview (CrewAI)

Key Features

Technologies Used

Quick Start (Using uv – Recommended)

1. Install uv

2. Clone the Repository

3. Create and Sync the Virtual Environment

4. Configure Environment Variables

5. Run the Crew

Output Artifacts

How This Differs from the LangGraph Version

Use Cases

Why uv

Repository Structure

Related Projects

License

Contract & API

Reliability & Benchmarks

Media & Demo

Related Agents

AionUi

activepieces

cherry-studio

CopilotKit

Quick Start (Using `uv` – Recommended)

1. Install `uv`

Why `uv`