MemCell MCP Server

MemCell provides persistent, adaptive memory, working thread continuity, knowledge graph traversal, and grounded document RAG for AI agents across any client harness. This repository contains the official MCP registry manifest, Smithery configuration, and connection guides.

Quickstart

Option A: Local stdio (Recommended)

Run via npx (requires memcell CLI installed or accessible via npm):

npx -y memcell mcp

Option B: Remote Streamable HTTP

Connect directly to the hosted or self-hosted MemCell streamable HTTP endpoint:

• Endpoint: https://memcell.ai/mcp

• Header: Authorization: Bearer <agent-key>

Option C: Air-Gapped Local Server (PGlite)

Run MemCell entirely offline on your local machine with embedded PGlite vector storage:

# 1. Start local server in background
memcell start --daemon

# 2. Wire local MCP tools
memcell connect --url http://localhost:3000

Related MCP server: agent-sleep

Client Configurations

1. Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "memcell": {
      "command": "npx",
      "args": ["-y", "memcell", "mcp"]
    }
  }
}

2. Claude Code

Run:

claude mcp add memcell -- npx -y memcell mcp

3. Cursor

Add to .cursor/mcp.json (or ~/.cursor/mcp.json):

{
  "mcpServers": {
    "memcell": {
      "command": "npx",
      "args": ["-y", "memcell", "mcp"]
    }
  }
}

4. Antigravity

Add to ~/.gemini/antigravity/mcp_config.json:

{
  "mcpServers": {
    "memcell": {
      "command": "npx",
      "args": ["-y", "memcell", "mcp"]
    }
  }
}

5. Windsurf / Cline / OpenCode / Goose

Configure the MCP server command as:

• Command: npx

• Args: ["-y", "memcell", "mcp"]

How It Works

MemCell exposes three core memory tools over MCP:

1. recall: Pre-flight memory retrieval before taking action. Fetches relevant instructions, decisions, facts, preferences (complete with detailed operational specifications and resource anchors), active or resumed session threads (with cumulative milestones and chronological tool activity trails), 1-hop knowledge graph relations, and grounded workspace document chunks.

2. remember: Durable memory storage. Files atomic instructions, decisions, facts, and preferences—including detailed operational specifications (content), affected resources (resources), and enforcement moments (appliesAt)—directly into the active workspace or user profile.

3. report: Outcome feedback calibration. Reports whether recalled memory worked, failed, or was avoided, calibrating confidence dynamically over time.

Ecosystem

• DevKit: TypeScript SDK, Python SDK, and CLI in memcell-ai/devkit

• Engine: Core memory service backend in memcell-ai/memcell

• Documentation: memcell.ai/docs

License

Apache-2.0