MemMachine

MemMachine

The open-source memory layer for AI agents.

Stop building stateless agents. Give your AI persistent memory with just 5 lines of code.

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.

pip install memmachine-client

from memmachine_client import import MemMachineClient

# Initialize the client
client = MemMachineClient(base_url="http://localhost:8080")

# Get or create a project
project = client.get_or_create_project(org_id="my_org", project_id="my_project")

# Create a memory instance for a user session
memory = project.memory(
    group_id="default",
    agent_id="travel_agent",
    user_id="alice",
    session_id="session_001"
)

# Add a memory
memory.add("I prefer aisle seats on flights", metadata={"category": "travel"})
# => [AddMemoryResult(uid='...')]

# Search memories
results = memory.search("What are my flight preferences?")
print(results.content.episodic_memory.long_term_memory.episodes[0].content)
# => "I prefer aisle seats on flights"

For full installation options (Docker, self-hosted, cloud), visit the Quick Start Guide.

Integrations

MemMachine works seamlessly with your favorite AI frameworks:

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

MCP Server Support

MemMachine includes a native Model Context Protocol (MCP) server for seamless integration with Claude Desktop, Cursor, and other MCP-compatible clients:

# Stdio mode (for Claude Desktop)
memmachine-mcp-stdio

# HTTP mode (for web clients)
memmachine-mcp-http

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

1. Agents interact via the API Layer: Users interact with an agent, which connects to MemMachine through a RESTful API, Python SDK, or MCP Server.
2. MemMachine manages memory: Processes interactions and stores them as Episodic Memory (conversational context) and Profile Memory (long-term user facts).
3. 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!

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.

References

@misc{luo2025agentlightningtrainai,
  title={Agent Lightning: Train ANY AI Agents with Reinforcement Learning},
  author={Xufang Luo and Yuge Zhang and Zhiyuan He and Zilong Wang and Siyun Zhao and Dongsheng Li and Luna K. Qiu and Yuqing Yang},
  year={2025},
  eprint={2508.03680},
  archivePrefix={arXiv},
  primaryClass={cs.AI},
  url={https://arxiv.org/abs/2508.03680},
}

License

MemMachine is released under the Apache 2.0 License.