awesome-llm-apps/mcp_ai_agents/multi_mcp_agent_router

Multi-MCP Agent Router

A Streamlit app that demonstrates the multi-agent + MCP pattern: specialized AI agents that each connect to different MCP servers to handle domain-specific tasks.

Instead of one agent with all tools, The router sends your request to a specialist — a code reviewer, security auditor, researcher, or BIM engineer — each with access to only the MCP tools they need.

Features

• 4 Specialized Agents: Code Reviewer, Security Auditor, Researcher, and BIM Engineer
• MCP Tool Routing: Each agent connects to different MCP servers (GitHub, filesystem, fetch, etc.)
• Agent Selection: Automatic routing based on query type, or manual agent selection
• Streaming Responses: Real-time output from Claude via the Anthropic API
• Conversation Memory: Per-agent conversation history within a session

Architecture

User Query
    |
    v
[Router] --> Classifies intent
    |
    +-- Code Review  --> GitHub MCP + Filesystem MCP
    +-- Security     --> GitHub MCP + Fetch MCP  
    +-- Research     --> Fetch MCP + Filesystem MCP
    +-- BIM/Revit    --> Custom MCP (named pipes)

Setup

Requirements

• Python 3.10+
• Anthropic API Key
• MCP servers (optional — the app works with or without them)

Installation

1. Clone this repository:

   git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
   cd mcp_ai_agents/multi_mcp_agent_forge
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Run the app:

   streamlit run agent_forge.py
   

4. Enter your Anthropic API key in the sidebar and start asking questions.

How It Works

1. Agent Definitions: Each agent has a name, system prompt, and list of MCP server configs
2. Router: Classifies the user's query and selects the best agent
3. MCP Connection: The selected agent connects to its assigned MCP servers
4. Execution: Claude processes the query with access to the agent's specific tools
5. Response: Results stream back to the Streamlit UI

Extending

Add new agents by defining them in the AGENTS dictionary:

AGENTS["my_agent"] = Agent(
    name="My Agent",
    description="Handles X tasks",
    system_prompt="You are an expert in X...",
    mcp_servers=[{"command": "npx", "args": ["-y", "@some/mcp-server"]}]
)

Credits

Inspired by cadre-ai/Agent Forge — a production multi-agent framework for Claude Code with 17 specialized agents, persistent memory, and desktop automation.