Xpersona

Crawler Summary

Crewai_Tutorial answer-first brief

Learning CrewAI CrewAI Research Tutorial A learning project demonstrating the power of multi-agent AI systems using **CrewAI** framework. 📚 Project Overview This project is created for **educational purposes** to learn and explore how multi-agent AI systems work. It implements an automated research and reporting pipeline where specialized AI agents work together to: 1. **Research** — Gather information on a given topic using web se Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

Freshness

Last checked 10/9/2026

Best For

Crewai_Tutorial 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 REPOS, runtime-metrics, public facts pack

CardFactsSnapshotContractTrust
Claim this agent
Agent DossierGITHUB REPOSSafety: 66/100

Crewai_Tutorial

Learning CrewAI CrewAI Research Tutorial A learning project demonstrating the power of multi-agent AI systems using **CrewAI** framework. 📚 Project Overview This project is created for **educational purposes** to learn and explore how multi-agent AI systems work. It implements an automated research and reporting pipeline where specialized AI agents work together to: 1. **Research** — Gather information on a given topic using web se

OpenClawself-declared

Public facts

4

Change events

1

Artifacts

0

Freshness

Oct 9, 2026

Verifiededitorial-contentNo verified compatibility signals

Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

Trust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Oct 9, 2026

Vendor

Priyagupta27

Artifacts

0

Benchmarks

0

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Unpublished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 10/9/2026.

View Source

Setup snapshot

  1. 1

    Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.

  2. 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

Priyagupta27

profilemedium
Observed Oct 9, 2026Source linkProvenance
Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium
Observed Oct 9, 2026Source linkProvenance
Security (1)

Handshake status

UNKNOWN

trustmedium
Observed unknownSource linkProvenance
Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium
Observed Apr 15, 2026Source linkProvenance

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 REPOS

Extracted files

0

Examples

3

Snippets

0

Languages

python

Executable Examples

text

src/
  agents/           # AI agent definitions
    researcher.py   # Gathers information
    analyst.py      # Analyzes data
    writer.py       # Generates reports
  crew/
    research_crew.py # Orchestrates agents and tasks
  tasks/            # Task definitions for each agent
    research_task.py
    analysis_task.py
    report_task.py
  models/
    schema.py       # Data schemas
  tools/
    web_search.py   # Search tool implementations
config/
  llm.py            # LLM configuration
main.py             # Entry point

text

GROQ_API_KEY=your_api_key_here

bash

python main.py

Docs & README

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

Self-declaredGITHUB REPOS

Docs source

GITHUB REPOS

Editorial quality

ready

Learning CrewAI CrewAI Research Tutorial A learning project demonstrating the power of multi-agent AI systems using **CrewAI** framework. 📚 Project Overview This project is created for **educational purposes** to learn and explore how multi-agent AI systems work. It implements an automated research and reporting pipeline where specialized AI agents work together to: 1. **Research** — Gather information on a given topic using web se

Full README

CrewAI Research Tutorial

A learning project demonstrating the power of multi-agent AI systems using CrewAI framework.

📚 Project Overview

This project is created for educational purposes to learn and explore how multi-agent AI systems work. It implements an automated research and reporting pipeline where specialized AI agents work together to:

  1. Research — Gather information on a given topic using web search
  2. Analyze — Process and analyze the collected information
  3. Report — Generate comprehensive reports based on the analysis

The agents collaborate in a sequential workflow, each using specialized tools and capabilities to accomplish their assigned tasks.

🛠 Technologies & Dependencies

  • CrewAI (>=1.15.17) — Multi-agent orchestration framework
  • LangChain — LLM framework and utilities
  • Groq — Fast LLM inference API
  • DDGS — DuckDuckGo search integration
  • Python (>=3.11) — Programming language
  • python-dotenv — Environment variable management

📁 Project Structure

src/
  agents/           # AI agent definitions
    researcher.py   # Gathers information
    analyst.py      # Analyzes data
    writer.py       # Generates reports
  crew/
    research_crew.py # Orchestrates agents and tasks
  tasks/            # Task definitions for each agent
    research_task.py
    analysis_task.py
    report_task.py
  models/
    schema.py       # Data schemas
  tools/
    web_search.py   # Search tool implementations
config/
  llm.py            # LLM configuration
main.py             # Entry point

🚀 Quick Start

  1. Setup environment variables in a .env file:

    GROQ_API_KEY=your_api_key_here

  2. Run the research pipeline:

    python main.py

  3. Enter your research topic when prompted

💡 Learning Outcomes

This project demonstrates:

  • Multi-agent AI system architecture
  • Agent collaboration and task sequencing
  • Integration with LLM providers
  • Tool usage within AI agents
  • Workflow orchestration patterns

This is an educational project created to understand and practice multi-agent AI concepts.

Contract & API

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

MissingGITHUB REPOS

Endpoints

Dossier APISnapshot APIContract APITrust API

Contract coverage

Status

missing

Auth

None

Streaming

No

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-priyagupta27-crewai-tutorial/snapshot"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-priyagupta27-crewai-tutorial/contract"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-priyagupta27-crewai-tutorial/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.

Related Agents

Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.

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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-priyagupta27-crewai-tutorial/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/crewai-priyagupta27-crewai-tutorial/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/crewai-priyagupta27-crewai-tutorial/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-priyagupta27-crewai-tutorial/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-priyagupta27-crewai-tutorial/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-priyagupta27-crewai-tutorial/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_REPOS",
      "generatedAt": "2026-10-09T20:26:06.477Z"
    }
  },
  "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",
    "category": "vendor",
    "label": "Vendor",
    "value": "Priyagupta27",
    "href": "https://github.com/PriyaGupta27/Crewai_Tutorial",
    "sourceUrl": "https://github.com/PriyaGupta27/Crewai_Tutorial",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-10-09T14:16:29.169Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-priyagupta27-crewai-tutorial/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-priyagupta27-crewai-tutorial/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-10-09T14:16:29.169Z",
    "isPublic": true
  },
  {
    "factKey": "docs_crawl",
    "category": "integration",
    "label": "Crawlable docs",
    "value": "6 indexed pages on 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
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-priyagupta27-crewai-tutorial/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-priyagupta27-crewai-tutorial/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true
  }
]

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
  }
]

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