🚀 LW MCP Agents

LW MCP Agents is a lightweight, modular framework for building and orchestrating AI agents using the Model Context Protocol (MCP). It empowers you to rapidly design multi-agent systems where each agent can specialize, collaborate, delegate, and reason—without writing complex orchestration logic.

Build scalable, composable AI systems using only configuration files.

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🔍 Why Use LW MCP Agents?

• ✅ Plug-and-Play Agents: Launch intelligent agents with zero boilerplate using simple JSON configs.
• ✅ Multi-Agent Orchestration: Chain agents together to solve complex tasks—no extra code required.
• ✅ Share & Reuse: Distribute and run agent configurations across environments effortlessly.
• ✅ MCP-Native: Seamlessly integrates with any MCP-compatible platform, including Claude Desktop.

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🧠 What Can You Build?

• Research agents that summarize documents or search the web
• Orchestrators that delegate tasks to domain-specific agents
• Systems that scale reasoning recursively and aggregate capabilities dynamically

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🏗️ Architecture at a Glance

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📚 Table of Contents

• Getting Started
• Example Agents
• Running Agents
• Custom Agent Creation
• How It Works
• Technical Architecture
• Acknowledgements

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🚀 Getting Started

🔧 Installation

git clone https://github.com/Autumn-AIs/LW-MCP-agents.git
cd LW-MCP-agents
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

▶️ Run Your First Agent

python src/agent/agent_runner.py --config examples/base_agent/base_agent_config.json

🤖 Try a Multi-Agent Setup

Terminal 1 (Research Agent Server):

python src/agent/agent_runner.py --config examples/orchestrator_researcher/research_agent_config.json --server-mode

Terminal 2 (Orchestrator Agent):

python src/agent/agent_runner.py --config examples/orchestrator_researcher/master_orchestrator_config.json

Your orchestrator now intelligently delegates research tasks to the research agent.

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🖥️ Claude Desktop Integration

Configure agents to run inside Claude Desktop:

1. Locate your Claude config file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json
• Linux: ~/.config/Claude/claude_desktop_config.json

2. Add your agent under mcpServers:

{
  "mcpServers": {
    "research-agent": {
      "command": "/bin/bash",
      "args": ["-c", "/path/to/venv/bin/python /path/to/agent_runner.py --config=/path/to/agent_config.json --server-mode"],
      "env": {
        "PYTHONPATH": "/path/to/project",
        "PATH": "/path/to/venv/bin:/usr/local/bin:/usr/bin"
      }
    }
  }
}

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📦 Example Agents

• Base Agent
  A minimal agent that connects to tools via MCP.
  📁 examples/base_agent/

• Orchestrator + Researcher
  Demonstrates hierarchical delegation and capability sharing.
  📁 examples/orchestrator_researcher/

💡 Contribute your own example! Submit a PR or reach out to the maintainers.

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⚙️ Running Agents

🔹 Basic Command

python src/agent/agent_runner.py --config <your_config.json>

🔸 Advanced Options

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🛠️ Custom Agent Creation

🧱 Minimal Config

{
  "agent_name": "my-agent",
  "llm_provider": "groq",
  "llm_api_key": "YOUR_API_KEY",
  "server_mode": false
}

🧠 Adding Capabilities

Define specialized functions the agent can reason over:

"capabilities": [
  {
    "name": "summarize_document",
    "description": "Summarize a document in a concise way",
    "input_schema": {
      "type": "object",
      "properties": {
        "document_text": { "type": "string" },
        "max_length": { "type": "integer", "default": 200 }
      },
      "required": ["document_text"]
    },
    "prompt_template": "Summarize the following document in {max_length} words:\n\n{document_text}"
  }
]

🔄 Orchestrator Agent

{
  "agent_name": "master-orchestrator",
  "servers": {
    "research-agent": {
      "command": "python",
      "args": ["src/agent/agent_runner.py", "--config=research_agent_config.json", "--server-mode"]
    }
  }
}

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🧬 How It Works

🧩 Capabilities as Reasoning Units

Each capability:

1. Fills in a prompt using provided arguments
2. Executes internal reasoning using LLMs
3. Uses tools or external agents
4. Returns the result

📖 Research Example

[INFO] agent:master-orchestrator - Executing tool: research_topic
[INFO] agent:research-agent - Using tool: brave_web_search
[INFO] agent:research-agent - Finished capability: research_topic

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🧱 Technical Architecture

🧠 Key Components

🌐 Architecture Highlights

• Hierarchical Design: Compose systems of agents with recursive reasoning
• Delegated Capabilities: Agents delegate intelligently to peers
• Tool Sharing: Tools available in one agent become accessible to others
• Code-Free Composition: Create entire systems via configuration

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🙌 Acknowledgements

This project draws inspiration from the brilliant work on mcp-agents by LastMile AI.