agentmem

agentmem

Enables AI coding agents to store and retrieve persistent contextual memories locally, with support for tagging and querying through CLI or MCP integration.

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README

🧠 agentmem

PyPI Version License: MIT Python 3.10+ MCP Compliant

agentmem is a high-performance, local-first persistent contextual memory & knowledge-base engine designed specifically for AI coding agents (Claude Code, OpenAI Codex, Cursor, Antigravity) and Model Context Protocol (MCP) servers.


🌟 Why agentmem?

AI agents frequently forget critical architectural decisions, environment setup constraints, and session conclusions across chat restarts. agentmem acts as a zero-latency, local memory bank that seamlessly indexes and retrieves codebase knowledge without ballooning prompt token context.

Key Features

  • Zero-Latency Local Storage: File-backed JSON key-value & structured memory store.
  • 🔌 Native MCP Integration: Plug directly into Claude Desktop, Cursor, or any MCP-compatible agent.
  • 🏷️ Context Tagging & Retrieval: Store memories tagged by scope (architecture, bugfix, decision, setup, todo).
  • 💻 Intuitive CLI: Query, insert, export, and manage context directly from the terminal.
  • 🛡️ Privacy First: 100% local execution — no external API calls required for storage.

🚀 Quick Start

1. Installation

pip install agentmem

2. CLI Usage

# Remember an architectural decision
agentmem add "Used SQLite/JSON for agentmem storage to guarantee single-file zero-dependency portability" --tag architecture

# Query stored memories
agentmem search "SQLite"

# List all memories in the current workspace
agentmem list

# Clear memories
agentmem clear

🔌 Using as an MCP Server

agentmem includes support for Model Context Protocol integration. Add the following to your claude_desktop_config.json or editor settings:

{
  "mcpServers": {
    "agentmem": {
      "command": "agentmem",
      "args": ["mcp"]
    }
  }
}

🤝 Contributing

We welcome community contributions! Please check out CONTRIBUTING.md for setup instructions, coding conventions, and pull request workflows.

  1. Fork the Repository
  2. Create a Feature Branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add amazing feature')
  4. Push to Branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

📄 License

Distributed under the MIT License. See LICENSE for more details.

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