Xpersona

Crawler Summary

Agentic-RAG-with-Multi-Agent-Collaboration-CrewAI-LangGraph- answer-first brief

Agentic RAG system orchestrating specialized AI agents via CrewAI & LangGraph. Features a multi-LLM setup (Gemini Flash + Llama 3.3 70B) for high-speed synthesis and rigorous technical auditing. Implements Model Context Protocol (MCP) for local filesystem persistence, direct FAISS search for speed, and production-ready RPM handling. --- title: Agentic RAG Chatbot emoji: 🤖 colorFrom: blue colorTo: indigo sdk: docker app_port: 7860 pinned: false --- Agentic RAG with Multi-Agent Collaboration (CrewAI + LangGraph) $1 $1 $1 A sophisticated Agentic RAG system that orchestrates a "Crew" of specialized AI agents to retrieve, summarize, architect, and audit information from local documents. This project implements a high-integrity workflow that separate Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

Freshness

Last checked 10/9/2026

Best For

Agentic-RAG-with-Multi-Agent-Collaboration-CrewAI-LangGraph- 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
Agent DossierGITHUB REPOSSafety: 66/100

Agentic-RAG-with-Multi-Agent-Collaboration-CrewAI-LangGraph-

Agentic RAG system orchestrating specialized AI agents via CrewAI & LangGraph. Features a multi-LLM setup (Gemini Flash + Llama 3.3 70B) for high-speed synthesis and rigorous technical auditing. Implements Model Context Protocol (MCP) for local filesystem persistence, direct FAISS search for speed, and production-ready RPM handling. --- title: Agentic RAG Chatbot emoji: 🤖 colorFrom: blue colorTo: indigo sdk: docker app_port: 7860 pinned: false --- Agentic RAG with Multi-Agent Collaboration (CrewAI + LangGraph) $1 $1 $1 A sophisticated Agentic RAG system that orchestrates a "Crew" of specialized AI agents to retrieve, summarize, architect, and audit information from local documents. This project implements a high-integrity workflow that separate

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

Mohamed Azaudeen

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

Mohamed Azaudeen

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

0

Snippets

0

Languages

python

Docs & README

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

Self-declaredGITHUB REPOS

Docs source

GITHUB REPOS

Editorial quality

ready

Agentic RAG system orchestrating specialized AI agents via CrewAI & LangGraph. Features a multi-LLM setup (Gemini Flash + Llama 3.3 70B) for high-speed synthesis and rigorous technical auditing. Implements Model Context Protocol (MCP) for local filesystem persistence, direct FAISS search for speed, and production-ready RPM handling. --- title: Agentic RAG Chatbot emoji: 🤖 colorFrom: blue colorTo: indigo sdk: docker app_port: 7860 pinned: false --- Agentic RAG with Multi-Agent Collaboration (CrewAI + LangGraph) $1 $1 $1 A sophisticated Agentic RAG system that orchestrates a "Crew" of specialized AI agents to retrieve, summarize, architect, and audit information from local documents. This project implements a high-integrity workflow that separate

Full README

title: Agentic RAG Chatbot emoji: 🤖 colorFrom: blue colorTo: indigo sdk: docker app_port: 7860 pinned: false

Agentic RAG with Multi-Agent Collaboration (CrewAI + LangGraph)

CrewAI LangGraph MCP

A sophisticated Agentic RAG system that orchestrates a "Crew" of specialized AI agents to retrieve, summarize, architect, and audit information from local documents. This project implements a high-integrity workflow that separates content generation from technical auditing to ensure zero hallucinations.

🚀 Key Features

  • Multi-Agent Orchestration: Managed via LangGraph for reliable state transitions and CrewAI for autonomous task execution.
  • Model Context Protocol (MCP): Utilizes the MCP Filesystem server to allow agents to persist technical summaries to the local disk, creating a "source of truth" for the audit phase.
  • Hybrid Multi-LLM Setup:
    • Gemini 2.5 Flash: Optimized for high-speed summarization and drafting.
    • Llama 3.3 70B (via Groq): Acts as the "Technical Auditor" for rigorous quality control.
  • Production Reliability: Implements Sequential Process and Max RPM handling to navigate Free Tier API limits while maintaining deep analysis.
  • Direct FAISS Integration: Python-native vector search for sub-5s retrieval latency.

🏗 Workflow Architecture

  1. Retrieve Node: Direct FAISS search retrieves relevant document chunks.
  2. Summarize Node: Data Summarizer (Gemini) condenses info and saves it via MCP.
  3. Generate Node: Response Architect (Gemini) crafts the professional response.
  4. Verify Node: Citation Manager & Technical Auditor (Llama 70B) perform a final two-step audit for citations and accuracy.

📁 Project Structure

├── agents/ # CrewAI Agent definitions ├── workflow/ # LangGraph state machine & nodes ├── mcp_config/ # MCP tool & server configurations ├── rag/ # FAISS indexing & embedding logic ├── summaries/ # Local storage for AI-generated summaries (MCP managed) └── app/ # FastAPI entry point

🛠 Setup & Installation

Prerequisites

  • Python 3.10+
  • Node.js (Required for the MCP Filesystem server)

1. Installation

git clone https://github.com/mohamed-azaudeen/Agentic-RAG-with-Multi-Agent-Collaboration-CrewAI-LangGraph-.git cd local_rag_mcp_bot pip install -r requirements.txt

2. Configure Environment

Create a .env file in the root directory:

GOOGLE_API_KEY= your_gemini_api_key GROQ_API_KEY= your_groq_api_key

3. Execution

You need to run three components simultaneously in separate terminals:

- Terminal 1 (MCP Server): npx -y @modelcontextprotocol/server-filesystem "./summaries"

- Terminal 2 (FastAPI Backend): uvicorn app.main:app --reload

- Terminal 3 (Streamlit UI): streamlit run ui.py

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-mohamed-azaudeen-agentic-rag-with-multi-agent-collaborat/snapshot"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-mohamed-azaudeen-agentic-rag-with-multi-agent-collaborat/contract"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-mohamed-azaudeen-agentic-rag-with-multi-agent-collaborat/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-mohamed-azaudeen-agentic-rag-with-multi-agent-collaborat/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/crewai-mohamed-azaudeen-agentic-rag-with-multi-agent-collaborat/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/crewai-mohamed-azaudeen-agentic-rag-with-multi-agent-collaborat/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-mohamed-azaudeen-agentic-rag-with-multi-agent-collaborat/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-mohamed-azaudeen-agentic-rag-with-multi-agent-collaborat/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-mohamed-azaudeen-agentic-rag-with-multi-agent-collaborat/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_REPOS",
      "generatedAt": "2026-10-10T02:05:05.740Z"
    }
  },
  "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": "Mohamed Azaudeen",
    "href": "https://github.com/mohamed-azaudeen/Agentic-RAG-with-Multi-Agent-Collaboration-CrewAI-LangGraph-",
    "sourceUrl": "https://github.com/mohamed-azaudeen/Agentic-RAG-with-Multi-Agent-Collaboration-CrewAI-LangGraph-",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-10-09T22:27:21.245Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-mohamed-azaudeen-agentic-rag-with-multi-agent-collaborat/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-mohamed-azaudeen-agentic-rag-with-multi-agent-collaborat/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-10-09T22:27:21.245Z",
    "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-mohamed-azaudeen-agentic-rag-with-multi-agent-collaborat/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-mohamed-azaudeen-agentic-rag-with-multi-agent-collaborat/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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