Multi-Agent RAG System
Advanced retrieval-augmented generation system with specialized agents for document processing, intelligent retrieval, answer synthesis, and quality verification.
🎯 Buy once, deploy on any framework
Includes implementations for CrewAI. One purchase — all platforms.
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- ✓ All 1 platform implementations
- ✓ Full source code & documentation
- ✓ Commercial license included
- ✓ 30-day money-back guarantee
- ✓ Free updates for 1 year
- ✓ 30-day email support
Choose Your Platform
One purchase includes all 1 implementations. Deploy on whichever framework fits your stack.
CrewAI
CrewAI crew with 4 specialized agents and production-ready tools.
Included in CrewAI version
- ✓crew.py with 4 agents
- ✓Custom tools
- ✓Config templates
- ✓Deployment guide
⚡ Why OpenClaw?
One-click install, automatic orchestration, built-in cron scheduling, and memory integration. Other platforms require manual setup — OpenClaw gets you to production in minutes.
Code Preview — CrewAI
from crewai import Agent, Crew
retriever = Agent(role='Hybrid Retriever', goal='Find relevant context', tools=[vector_search, keyword_search])
verifier = Agent(role='Answer Verifier', goal='Fact-check against sources', tools=[fact_checker, confidence_scorer])Agent Architecture
How the 4 agents work together
Your data, triggers, or requests
Document Processor
Intelligent Ingestion
Chunks and embeds documents with intelligent strategies.
Retriever
Hybrid Search
Uses hybrid search with query expansion for better recall.
Synthesizer
Answer Generation
Combines context into coherent, well-cited answers.
Verifier
Answer Verification
Fact-checks answers and provides confidence scores.
Structured results, reports, and actions
What's Included
Everything you get with this template
The Problem
Basic RAG systems retrieve irrelevant chunks, hallucinate answers, and provide no confidence signals. Production applications need reliable, verifiable answers.
The Solution
A 4-agent RAG system with intelligent chunking, hybrid retrieval, verified synthesis, and confidence scoring — production-ready knowledge bases.
Tools You'll Need
Everything required to build this 4-agent system — click any tool for details
Agent orchestration
LLM for synthesis and verification
Vector database for document embeddings
Document parsing and processing
Alternative vector database
Local vector store for development
RAG pipeline tracing and evaluation
RAG quality evaluation framework
Implementation Guide
8 steps to build this system • 3-4 hours estimated
📋 Prerequisites
Prepare your document corpus
Collect and organize documents. Choose chunking strategy based on content type.
Configure intelligent chunking
Set up semantic chunking that respects document structure.
Build the embedding pipeline
Configure embedding model, metadata extraction, and vector storage.
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Code Preview
Sample agent setup — see platform-specific previews above
from crewai import Agent, Crew
retriever = Agent(role='Hybrid Retriever', goal='Find relevant context', tools=[vector_search, keyword_search])
verifier = Agent(role='Answer Verifier', goal='Fact-check against sources', tools=[fact_checker, confidence_scorer])Example Input & Output
See what goes in and what comes out
{"query": "What is our refund policy for enterprise customers?", "collection": "company_policies"}{"answer": "Enterprise customers can request refunds within 60 days...", "confidence": 0.94, "sources": 3, "verified": true}Requirements
Reviews
What builders are saying
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Frequently Asked Questions
Do I get all platform implementations?+
Yes — one purchase includes all platform implementations.
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