内容摘录
MemMachine
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!MemMachine: Long Term Memory for AI Agents
**The open-source memory layer for AI agents.**
*Stop building stateless agents. Give your AI persistent memory with just 5 lines of code.*
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!GitHub Release Version
!GitHub License
Ask DeepWiki
!Discord
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!Docker Pulls
!GitHub Downloads
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!PyPI Downloads - memmachine-client
!PyPI Downloads - memmachine-server
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What is MemMachine?
MemMachine is an open-source **long-term memory layer** for AI agents and LLM-powered applications. It enables your AI to **learn, store, and recall** information from past sessions—transforming stateless chatbots into personalized, context-aware assistants.
Key Capabilities
**Episodic Memory**: Graph-based conversational context that persists across sessions
**Profile Memory**: Long-term user facts and preferences stored in SQL
**Working Memory**: Short-term context for the current session
**Agent Memory Persistence**: Memory that survives restarts, sessions, and even model changes
Quick Start
Get up and running in under 5 minutes:
**Prerequisites:** This code requires a running MemMachine Server.
Start a server locally or create a free account on the MemMachine Platform.
For full installation options (Docker, self-hosted, cloud), visit the
Quick Start Guide.
Integrations
MemMachine works seamlessly with your favorite AI frameworks:
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| Framework | Description |
|-----------|-------------|
| **LangChain** | Memory provider for LangChain agents |
| **LangGraph** | Stateful memory for LangGraph workflows |
| **CrewAI** | Persistent memory for CrewAI multi-agent systems |
| **LlamaIndex** | Memory integration for LlamaIndex applications |
| **AWS Strands** | Memory for AWS Strands Agent SDK |
| **n8n** | No-code workflow automation integration |
| **Dify** | Memory backend for Dify AI applications |
| **FastGPT** | Integration with FastGPT platform |
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MCP Server Support
MemMachine includes a native **Model Context Protocol (MCP)** server for seamless integration with Claude Desktop, Cursor, and other MCP-compatible clients:
See the MCP documentation for setup instructions.
Who Is MemMachine For?
**Developers** building AI agents, assistants, or autonomous workflows
**Researchers** experimenting with agent architectures and cognitive models
**Teams** who need persistent, cross-session memory for their LLM applications
Key Features
**Multiple Memory Types**: Working (short-term), Episodic (long-term conversational), and Profile (user facts) memory
**Developer-Friendly APIs**: Python SDK, RESTful API, TypeScript SDK, and MCP server interfaces
**Flexible Storage**: Graph database (Neo4j) for episodic memory, SQL for profiles
**LLM Agnostic**: Works with OpenAI, Anthropic, Bedrock, Ollama, and any LLM provider
**Self-Hosted or Cloud**: Run locally, in Docker, or use our managed service
For more information, refer to the API Reference Guide.
Architecture
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!MemMachine Architecture
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**Agents interact via the API Layer**: Users interact with an agent, which connects to MemMachine through a RESTful API, Python SDK, or MCP Server.
**MemMachine manages memory**: Processes interactions and stores them as Episodic Memory (conversational context) and Profile Memory (long-term user facts).
**Data is persisted**: Episodic memory is stored in a graph database; profile memory is stored in SQL.
Use Cases & Example Agents
MemMachine's versatile memory architecture can be applied across any domain. Explore our examples to see memory-powered agents in action:
| Agent | Description |
|-------|-------------|
| **CRM Agent** | Recalls client history and deal stages to help sales teams close faster |
| **Healthcare Navigator** | Remembers medical history and tracks treatment progress |
| **Personal Finance Advisor** | Stores portfolio preferences and risk tolerance for personalized insights |
| **Writing Assistant** | Learns your style guide and terminology for consistent content |
Built with MemMachine
Are you using MemMachine in your project? We'd love to feature you!
Share your project in GitHub Discussions → Showcase
Drop a message in our Discord #showcase channel
Growing Community
MemMachine is a growing community of builders and developers. Help us grow by clicking the ⭐ **Star** button above!
<img src="https://starchart.cc/MemMachine/MemMachine.svg?variant=light" alt="MemMachine Star History" height="300"/>
Documentation
**Main Website** – Learn about MemMachine
**Docs & API Reference** – Full documentation
**Quick Start Guide** – Get started in minutes
Community & Support
**Discord**: Join our community for support, updates, and discussions:
https://discord.gg/usydANvKqD
**Issues & Feature Requests**: Use GitHub
Issues
Contributing
We welcome contributions! Please see our CONTRIBUTING.md for guidelines.
License
MemMachine is released under the Apache 2.0 License.