plano/demos/use_cases/rag_agent/README.md

RAG Agent with MCP Protocol

A multi-agent RAG system using the Model Context Protocol (MCP) for agent communication.

Architecture

This demo consists of three MCP agents:

1. Query Rewriter - Rewrites user queries for better retrieval
2. Context Builder - Retrieves relevant context from knowledge base
3. Response Generator - Generates final responses with context

Each agent runs as an independent MCP server and exposes tools that can be called via the MCP protocol.

MCP Tools

Query Rewriter Agent

• Tool: rewrite_query_with_archgw
• Description: Rewrites user queries using LLM for better retrieval
• Port: 10500

Context Builder Agent

• Tool: chat_completions
• Description: Augments queries with relevant context from knowledge base
• Port: 10501

Response Generator Agent

• Port: 10502

Setup and Running

1. Start archgw

archgw up --foreground

2. Start Individual Agents

Query Rewriter:

uv run python -m rag_agent \
  --agent query_rewriter \
  --host 0.0.0.0 \
  --port 10500 \
  --transport sse

Context Builder:

uv run python -m rag_agent \
  --agent context_builder \
  --host 0.0.0.0 \
  --port 10501 \
  --transport sse

Response Generator:

uv run python -m rag_agent \
  --agent response_generator \
  --host 0.0.0.0 \
  --port 10502 \
  --transport sse

3. Start All Agents at Once

./start_agents.sh

Configuration

The arch_config.yaml defines how agents are connected:

agent_filters:
  - id: query_rewriter
    url: mcp://host.docker.internal:10500
    tool: rewrite_query_with_archgw  # MCP tool name

  - id: context_builder
    url: mcp://host.docker.internal:10501
    tool: chat_completions

MCP Tool Invocation Patterns

The config supports different ways to specify MCP tools:

1. Separate tool field (recommended):

- id: query_rewriter
  url: mcp://host.docker.internal:10500
  tool: rewrite_query_with_archgw

2. Tool in URL path:

- id: query_rewriter
  url: mcp://host.docker.internal:10500/rewrite_query_with_archgw

3. Tool as query parameter:

- id: query_rewriter
  url: mcp://host.docker.internal:10500?tool=rewrite_query_with_archgw

CLI Options

uv run python -m rag_agent --help

Options:
  --transport TEXT   Transport type: stdio or sse (default: sse)
  --host TEXT       Host to bind MCP server to (default: localhost)
  --port INTEGER    Port for MCP server (default: 10500)
  --agent TEXT      Agent name: query_rewriter, context_builder, or response_generator (required)
  --name TEXT       Custom MCP server name (optional)

Environment Variables

# archgw LLM Gateway base URL (default: http://localhost:12000/v1)
export LLM_GATEWAY_ENDPOINT="http://localhost:12000/v1"

# OpenAI API Key for model providers
export OPENAI_API_KEY="your-key-here"

Testing

See sample_queries.md for example queries to test the RAG system.

Example request:

curl -X POST http://localhost:8001/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4o",
    "messages": [
      {
        "role": "user",
        "content": "What is the guaranteed uptime for TechCorp?"
      }
    ]
  }'