How should rag-multi-agent-template 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

rag-multi-agent-template answer-first brief

RAG enabled multi agent template using CrewAI and WatsonxAI. Supports ChromaDB, FAISS, Pinecone with document processing for PDF/DOCX/TXT. Includes legal, technical, and customer support examples. Multi-Agent RAG Template A comprehensive template for creating RAG-enabled multi-agent systems using CrewAI and IBM WatsonxAI. Overview This template provides both basic multi-agent functionality and advanced RAG (Retrieval-Augmented Generation) capabilities. Choose from two implementations: Basic Multi-Agent System (agent.py) - **Researcher Agent** - Conducts web research using search tools - **Writer Agent** - Crea Capability contract not published. No trust telemetry is available yet. Last updated 2/25/2026.

Freshness

Last checked 2/25/2026

Best For

rag-multi-agent-template 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

Card Facts Snapshot Contract Trust

Claim this agent

Agent DossierGITHUB REPOSSafety: 66/100

rag-multi-agent-template

OpenClawself-declared

Public facts

Change events

Artifacts

Freshness

Feb 25, 2026

Verifiededitorial-contentNo verified compatibility signals

Capability contract not published. No trust telemetry is available yet. Last updated 2/25/2026.

Trust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Feb 25, 2026

Vendor

Theogyeezy

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Unpublished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 2/25/2026.

View Source

Setup snapshot

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

Theogyeezy

profilemedium

Observed Feb 25, 2026Source link Provenance

Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium

Observed Feb 25, 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 REPOS

Extracted files

Examples

Snippets

Languages

python

Executable Examples

bash

pip install -r requirements.txt

bash

export API_KEY="your_watsonx_api_key"
export SERPER_API_KEY="your_serper_api_key"  # Optional for web search

bash

python agent.py

bash

python rag_agent.py

python

class RAGConfig:
    VECTOR_DB = "chroma"  # or "faiss", "pinecone"
    EMBEDDING_MODEL = "sentence-transformers"
    CHUNK_SIZE = 500
    RETRIEVAL_K = 3
    # ... more options

python

# See examples/legal_research_example.py
python examples/legal_research_example.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

Full README

Multi-Agent RAG Template

A comprehensive template for creating RAG-enabled multi-agent systems using CrewAI and IBM WatsonxAI.

Overview

This template provides both basic multi-agent functionality and advanced RAG (Retrieval-Augmented Generation) capabilities. Choose from two implementations:

Basic Multi-Agent System (`agent.py`)

Researcher Agent - Conducts web research using search tools
Writer Agent - Creates written content based on research findings

RAG-Enabled Multi-Agent System (`rag_agent.py`)

RAG-Powered Agents - Access local knowledge bases for document-based research
Configurable Vector Databases - Support for ChromaDB, FAISS, and Pinecone
Document Processing - Handle PDF, DOCX, TXT, and Markdown files
Flexible Templates - Pre-built configurations for legal, technical, and support use cases

Quick Start

1. Install Dependencies

pip install -r requirements.txt

2. Configure API Keys

Set your environment variables:

export API_KEY="your_watsonx_api_key"
export SERPER_API_KEY="your_serper_api_key"  # Optional for web search

3. Choose Your Implementation

Basic Multi-Agent System

python agent.py

RAG-Enabled System

Configure your settings in config/rag_config_template.py
Add documents to data/documents/
Run the RAG system:

python rag_agent.py

RAG Features

Vector Database Support

ChromaDB - Local vector database (default)
FAISS - High-performance similarity search
Pinecone - Cloud-based vector database

Document Processing

PDF - Extract text from PDF documents
DOCX - Process Word documents
TXT/MD - Plain text and Markdown files
Chunking - Intelligent text splitting with overlap

Embedding Models

Sentence Transformers - Local embeddings (default)
OpenAI - Cloud-based embeddings
Custom - Easy integration of other models

Configuration

RAG Configuration (`config/rag_config_template.py`)

class RAGConfig:
    VECTOR_DB = "chroma"  # or "faiss", "pinecone"
    EMBEDDING_MODEL = "sentence-transformers"
    CHUNK_SIZE = 500
    RETRIEVAL_K = 3
    # ... more options

Agent Templates

Pre-built templates for common use cases:

Legal Research - Legal document analysis and research
Technical Documentation - API docs and technical guides
Customer Support - FAQ and troubleshooting assistance
Medical Research - Clinical literature review
Business Analysis - Market research and intelligence

Examples

Legal Research System

# See examples/legal_research_example.py
python examples/legal_research_example.py

Technical Documentation

# See examples/technical_docs_example.py
python examples/technical_docs_example.py

Customer Support

# See examples/customer_support_example.py
python examples/customer_support_example.py

Project Structure

Multi Agent/
├── agent.py                 # Basic multi-agent system
├── rag_agent.py            # RAG-enabled system
├── config/
│   └── rag_config_template.py
├── rag/
│   ├── vector_stores/       # Vector database implementations
│   ├── document_processors/ # Document processing pipeline
│   ├── embeddings/         # Embedding model abstractions
│   ├── tools/              # RAG retrieval tools
│   └── knowledge_base_manager.py
├── templates/
│   ├── agent_templates.py   # Pre-built agent configurations
│   └── task_templates.py    # Pre-built task templates
├── examples/               # Complete use case examples
├── data/
│   └── documents/          # Your document storage
└── requirements.txt

Customization

Creating Custom Agents

from templates import AgentTemplates

custom_agent = AgentTemplates.create_custom_rag_agent(
    llm=llm,
    rag_tool=rag_tool,
    role="Your Custom Role",
    goal="Your Custom Goal",
    backstory="Your Custom Backstory"
)

Adding Custom Tasks

from templates import TaskTemplates

custom_task = TaskTemplates.create_custom_task(
    agent=agent,
    description="Your task description",
    expected_output="Your expected output format",
    output_file="output.md"
)

Knowledge Base Management

from rag.knowledge_base_manager import KnowledgeBaseManager

kb = KnowledgeBaseManager(config)
result = kb.add_documents_from_path("path/to/documents")
search_results = kb.search_knowledge_base("query", k=5)

Requirements

Core Dependencies

CrewAI >= 0.28.8
langchain-ibm >= 0.1.0
IBM WatsonxAI access

Vector Database Options (choose one)

chromadb >= 0.4.15 (local)
faiss-cpu >= 1.7.4 (local)
pinecone-client >= 2.2.4 (cloud)

Document Processing

PyPDF2 >= 3.0.1 (PDF support)
python-docx >= 0.8.11 (DOCX support)
sentence-transformers >= 2.2.2 (embeddings)

Advanced Features

Multiple Vector Databases

Switch between vector databases by changing configuration:

config = {"vector_db": "chroma"}  # or "faiss", "pinecone"

Custom Embedding Models

Implement custom embeddings:

from rag.embeddings import BaseEmbeddings

class CustomEmbeddings(BaseEmbeddings):
    def embed_documents(self, texts):
        # Your implementation
        pass

Batch Document Processing

Process multiple document types:

from rag.document_processors import DocumentProcessorFactory

chunks = DocumentProcessorFactory.process_documents(file_paths, config)

License

This template is provided as-is for educational and development purposes.

Contract & API

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

MissingGITHUB REPOS

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://xpersona.co/api/v1/agents/crewai-theogyeezy-rag-multi-agent-template/snapshot"

curl -s "https://xpersona.co/api/v1/agents/crewai-theogyeezy-rag-multi-agent-template/contract"

curl -s "https://xpersona.co/api/v1/agents/crewai-theogyeezy-rag-multi-agent-template/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.

Self-declaredprotocol-neighbors

GITHUB_REPOSactivepieces

Rank

AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents

Traction

No public download signal

Freshness

Updated 2d ago

OPENCLAW

GITHUB_REPOScherry-studio

Rank

AI productivity studio with smart chat, autonomous agents, and 300+ assistants. Unified access to frontier LLMs

Traction

No public download signal

Freshness

Updated 6d ago

MCPOPENCLAW

GITHUB_REPOSAionUi

Rank

Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!

Traction

No public download signal

Freshness

Updated 6d ago

MCPOPENCLAW

GITHUB_REPOSCopilotKit

Rank

The Frontend for Agents & Generative UI. React + Angular

Traction

No public download signal

Freshness

Updated 23d ago

OPENCLAW

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://xpersona.co/api/v1/agents/crewai-theogyeezy-rag-multi-agent-template/snapshot",
    "contractUrl": "https://xpersona.co/api/v1/agents/crewai-theogyeezy-rag-multi-agent-template/contract",
    "trustUrl": "https://xpersona.co/api/v1/agents/crewai-theogyeezy-rag-multi-agent-template/trust"
  },
  "curlExamples": [
    "curl -s \"https://xpersona.co/api/v1/agents/crewai-theogyeezy-rag-multi-agent-template/snapshot\"",
    "curl -s \"https://xpersona.co/api/v1/agents/crewai-theogyeezy-rag-multi-agent-template/contract\"",
    "curl -s \"https://xpersona.co/api/v1/agents/crewai-theogyeezy-rag-multi-agent-template/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_REPOS",
      "generatedAt": "2026-04-17T02:19:06.180Z"
    }
  },
  "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": "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": "vendor",
    "category": "vendor",
    "label": "Vendor",
    "value": "Theogyeezy",
    "href": "https://github.com/theogyeezy/rag-multi-agent-template",
    "sourceUrl": "https://github.com/theogyeezy/rag-multi-agent-template",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-02-25T05:06:49.799Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://xpersona.co/api/v1/agents/crewai-theogyeezy-rag-multi-agent-template/contract",
    "sourceUrl": "https://xpersona.co/api/v1/agents/crewai-theogyeezy-rag-multi-agent-template/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-02-25T05:06:49.799Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://xpersona.co/api/v1/agents/crewai-theogyeezy-rag-multi-agent-template/trust",
    "sourceUrl": "https://xpersona.co/api/v1/agents/crewai-theogyeezy-rag-multi-agent-template/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
  }
]

rag-multi-agent-template answer-first brief

Executive Summary

Evidence Ledger

Release & Crawl Timeline

Artifacts Archive

Docs & README

Multi-Agent RAG Template

Overview

Basic Multi-Agent System (agent.py)

RAG-Enabled Multi-Agent System (rag_agent.py)

Quick Start

1. Install Dependencies

2. Configure API Keys

3. Choose Your Implementation

Basic Multi-Agent System

RAG-Enabled System

RAG Features

Vector Database Support

Document Processing

Embedding Models

Configuration

RAG Configuration (config/rag_config_template.py)

Agent Templates

Examples

Legal Research System

Technical Documentation

Customer Support

Project Structure

Customization

Creating Custom Agents

Adding Custom Tasks

Knowledge Base Management

Requirements

Core Dependencies

Vector Database Options (choose one)

Document Processing

Advanced Features

Multiple Vector Databases

Custom Embedding Models

Batch Document Processing

License

Contract & API

Reliability & Benchmarks

Media & Demo

Related Agents

Basic Multi-Agent System (`agent.py`)

RAG-Enabled Multi-Agent System (`rag_agent.py`)

RAG Configuration (`config/rag_config_template.py`)