AgentMemo MCP Server

Persistent memory and human-in-the-loop approval for AI agents via Model Context Protocol (MCP)

AgentMemo is a Model Context Protocol (MCP) server that gives AI agents persistent memory across sessions and human approval capabilities before sensitive actions.

Features

• 🧠 Persistent Memory — Store and retrieve memories across conversations and sessions

• ✅ Human Approval Gateway — Agents can request approval from humans before critical actions

• 🔌 MCP-Native — Works with any MCP client (Claude Desktop, Cursor, Windsurf, OpenClaw)

• 🌐 Cloud API — Powered by AgentMemo API (https://agentmemo.net)

• 📦 Zero Setup — Just add your API key, no server to deploy

Installation

npm install agentmemo-mcp

Or install globally for MCP clients:

npm install -g agentmemo-mcp

Quick Start

1. Get Your API Key

Sign up for a free API key at agentmemo.net — no credit card required.

2. Configure Your MCP Client

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "agentmemo": {
      "command": "npx",
      "args": ["agentmemo-mcp"],
      "env": {
        "AGENTMEMO_API_KEY": "your_api_key_here"
      }
    }
  }
}

Cursor / Windsurf

Add to your settings:

{
  "mcpServers": {
    "agentmemo": {
      "command": "npx",
      "args": ["agentmemo-mcp"],
      "env": {
        "AGENTMEMO_API_KEY": "your_api_key_here"
      }
    }
  }
}

OpenClaw

Already integrated! Set AGENTMEMO_API_KEY in your .env or OpenClaw config.

3. Use the Tools

Your agent now has access to these tools:

• remember — Store a memory for later recall

• recall — Search stored memories by query

• forget — Delete a memory by ID

• list_memories — List recent memories in a namespace

• request_approval — Ask a human to approve an action

• check_approval — Check the status of an approval request

API Reference

Tool: remember

Store information for later recall.

{
  "content": "User prefers dark mode and concise responses",
  "namespace": "user-preferences"
}

Returns: Memory ID, creation timestamp

Tool: recall

Search across stored memories.

{
  "query": "dark mode preferences",
  "namespace": "user-preferences",
  "limit": 5
}

Returns: List of matching memories with scores

Tool: request_approval

Request human approval before a sensitive action.

{
  "action": "Delete all emails older than 1 year",
  "context": "Freeing up 50GB of storage"
}

Returns: Approval request ID and status

Tool: check_approval

Poll the status of a pending approval.

{
  "id": "approval_12345"
}

Returns: Status (pending/approved/rejected) and decision if available

Memory Namespaces

Organize memories by namespace to keep them separate:

• user-preferences — User settings and preferences

• project-alpha — Project-specific context

• meeting-notes — Meeting transcripts and summaries

• custom/any-name — Any custom namespace

Development

Requirements

• Node.js 18+

• npm 9+

Setup

git clone https://github.com/andrewpetecoleman-cloud/agentmemo-mcp.git
cd agentmemo-mcp
npm install

Testing

npm test

Building

npm run build

How It Works

1. Agent asks for memory — "Remember that the user prefers dark mode"

2. MCP Server handles it — Calls AgentMemo API with your API key

3. Memory is stored — Persisted in AgentMemo cloud (encrypted in transit)

4. Agent recalls later — "What are the user's preferences?"

5. Memory is retrieved — Searched from AgentMemo and returned to agent

For approvals, the agent pauses and waits for human decision before proceeding.

Architecture

Agent (Claude/GPT/etc)
    ↓
MCP Server (agentmemo-mcp)
    ↓
AgentMemo API (agentmemo.net)
    ↓
Memory Storage + Approval Gateway

Pricing

Free Tier:

• 10,000 memories

• 100 searches/day

• No credit card required

Paid Plans:

• Starter: $19/month

• Pro: $99/month

• Team: $499/month

See agentmemo.net for full pricing.

Security

• ✅ HTTPS encrypted in transit

• ✅ API key authentication

• ✅ Namespace isolation

• ✅ No data sharing with third parties

• ✅ User data never used for model training

Support

• 📖 Docs: agentmemo.net/docs

• 💬 Email: hello@agentmemo.net

• 🐛 Issues: GitHub Issues

License

MIT

Contributing

Contributions welcome! Please:

1. Fork this repo

2. Create a feature branch

3. Submit a pull request

Related Projects

• AgentMemo API — Full product repo

• TypeScript SDK — Node.js/browser SDK

• Python SDK — Python integration

Built by Andy Coleman at AgentMemo