agentmem
Enables AI coding agents to store and retrieve persistent contextual memories locally, with support for tagging and querying through CLI or MCP integration.
README
🧠 agentmem
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.
- Fork the Repository
- Create a Feature Branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to Branch (
git push origin feature/amazing-feature) - Open a Pull Request
📄 License
Distributed under the MIT License. See LICENSE for more details.
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