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README.md
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agent_forge.py
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README.md

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.