Letta MCP Server

Use This MCP server To

Manage Letta agents and their lifecycle Perform memory operations for agent context Integrate Letta system features into AI workflows Deploy MCP server with Node.js or Docker Enable real-time communication with AI agents Extend functionality via modular tool implementations

README

Letta MCP Server

A server that provides tools for agent management, memory operations, and integration with the Letta system.

Quick Setup

Option 1: Run with Node.js

# Development (with hot reload)
npm run dev:sse     # SSE transport

# Production
npm run build       # Build TypeScript first
npm run start:sse   # SSE transport

Option 2: Run with Docker

# Build and run locally
docker build -t letta-mcp-server .
docker run -d -p 3001:3001 -e PORT=3001 -e NODE_ENV=production --name letta-mcp letta-mcp-server

# Or use the public image
docker run -d -p 3001:3001 -e PORT=3001 -e NODE_ENV=production --name letta-mcp ghcr.io/oculairmedia/letta-mcp-server:latest

Directory Structure

index.js - Main entry point
core/ - Core server functionality
tools/ - Individual tool implementations
transports/ - Server transport implementations (stdio and SSE)

Available Tools

Agent Management

Tool	Description	Required Parameters	Optional Parameters
`create_agent`	Create a new Letta agent	name, description	model, embedding
`list_agents`	List all available agents	-	filter
`prompt_agent`	Send a message to an agent	agent_id, message	-
`get_agent`	Get agent details by ID	agent_id	-
`modify_agent`	Update an existing agent	agent_id, update_data	-
`delete_agent`	Delete an agent	agent_id	-
`clone_agent`	Clone an existing agent	source_agent_id, new_agent_name	override_existing_tools, project_id
`bulk_delete_agents`	Delete multiple agents	-	agent_ids, agent_name_filter, agent_tag_filter

Memory Management

Tool	Description	Required Parameters	Optional Parameters
`list_memory_blocks`	List all memory blocks	-	filter, agent_id, page, pageSize, label
`create_memory_block`	Create a new memory block	name, label, value	agent_id, metadata
`read_memory_block`	Read a memory block	block_id	agent_id
`update_memory_block`	Update a memory block	block_id	value, metadata, agent_id
`attach_memory_block`	Attach memory to an agent	block_id, agent_id	label

Tool Management

Tool	Description	Required Parameters	Optional Parameters
`list_tools`	List all available tools	-	filter, page, pageSize
`list_agent_tools`	List tools for a specific agent	agent_id	-
`attach_tool`	Attach tools to an agent	agent_id	tool_id, tool_ids, tool_names
`upload_tool`	Upload a new tool	name, description, source_code	category, agent_id
`bulk_attach_tool_to_agents`	Attach a tool to multiple agents	tool_id	agent_name_filter, agent_tag_filter

Additional Tools

Model Management: list_llm_models, list_embedding_models
Archive Management: list_passages, create_passage, modify_passage, delete_passage
MCP Server Management: list_mcp_servers, list_mcp_tools_by_server
Import/Export: export_agent, import_agent

Docker Operations

# View container logs
docker logs -f letta-mcp

# Stop the container
docker stop letta-mcp

# Update to latest version
docker pull ghcr.io/oculairmedia/letta-mcp-server:latest
docker stop letta-mcp
docker rm letta-mcp
docker run -d -p 3001:3001 -e PORT=3001 -e NODE_ENV=production --name letta-mcp ghcr.io/oculairmedia/letta-mcp-server:latest

Configuration with MCP Settings

Add the server to your mcp_settings.json:

"letta": {
  "command": "node",
  "args": [
    "--no-warnings",
    "--experimental-modules",
    "path/to/letta-server/index.js"
  ],
  "env": {
    "LETTA_BASE_URL": "https://your-letta-instance.com",
    "LETTA_PASSWORD": "yourPassword"
  },
  "disabled": false,
  "alwaysAllow": [
    "upload_tool",
    "attach_tool",
    "list_agents",
    "list_memory_blocks"
  ],
  "timeout": 300
}

For remote instances, use the URL configuration:

"remote_letta_tools": {
  "url": "http://your-server:3001/sse",
  "disabled": false,
  "alwaysAllow": [
    "attach_tool", 
    "list_agents",
    "list_tools",
    "get_agent"
  ],
  "timeout": 120
}

Letta-MCP-server FAQ

How do I deploy the Letta-MCP-server?

You can deploy it using Node.js with npm scripts or via Docker containers for easy setup.

What functionalities does the Letta-MCP-server provide?

It manages agents, handles memory operations, and integrates with the Letta system for AI workflows.

Can I extend the Letta-MCP-server with custom tools?

Yes, the server supports modular tool implementations located in the 'tools/' directory.

How does the server communicate with agents?

It uses SSE (Server-Sent Events) transport for real-time communication with agents.

Is the Letta-MCP-server production-ready?

Yes, it supports production deployment with build and start scripts and Docker images.

Where can I find the main entry point of the server?

The main entry point is the 'index.js' file in the root directory.

Does the server support hot reload during development?

Yes, running 'npm run dev:sse' enables development mode with hot reload.

What environment variables are required?

Common variables include PORT and NODE_ENV to configure server port and environment.