Which integrations are available for this server?

Provides web search capabilities through Brave's search engine, allowing agents to retrieve information from the internet

How do I use LW MCP Agents?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@LW MCP Agents create a research agent that can summarize documents and search the web" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

🚀 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.

🔍 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.

Related MCP server: SupaUI MCP Server

🧠 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

🏗️ Architecture at a Glance

LW-MCP-agents-diagram

📚 Table of Contents

🚀 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.

🖥️ 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": { "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" } } } }

📦 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.

⚙️ Running Agents

🔹 Basic Command

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

🔸 Advanced Options

Option	Description
`--server-mode`	Exposes the agent as an MCP server
`--server-name`	Assigns a custom MCP server name

🛠️ 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"] } } }

🧬 How It Works

🧩 Capabilities as Reasoning Units

Each capability:

Fills in a prompt using provided arguments
Executes internal reasoning using LLMs
Uses tools or external agents
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

🧱 Technical Architecture

🧠 Key Components

Component	Role
`AgentServer`	Starts, configures, and runs an agent
`MCPServerWrapper`	Wraps the agent to expose it over MCP
`CapabilityRegistry`	Loads reasoning tasks from config
`ToolRegistry`	Discovers tools from other agents

🌐 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

🙌 Acknowledgements

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

LW MCP Agents