MemMachine
MemMachine is the most accurate open-source memory for stateful AI agents and LLM applications.
About
MemMachine is an open-source memory layer that transforms AI agents and applications into intelligent, personalized assistants. Unlike traditional AI apps that start fresh each time, MemMachine enables applications to learn, store, and recall data from past sessions, enriching every interaction with context. Key Features: • Persistent Memory - Maintains memory across sessions, agents, and LLMs, building evolving user profiles • Multi-Platform Integration - Works with OpenAI, AWS Bedrock, Ollama, and more via MCP server capability • Flexible Deployment - Run locally, in the cloud, or install via pip with full data control • Open-Source - Comprehensive documentation, active community support
Key Features
Persistent Episodic & Profile Memory
Stores short-term conversational context (episodic) and long-term user facts (profile) so agents can recall past interactions, preferences, and evolving user profiles across sessions.
Multi-Platform Integration (MCP Server)
Connects with OpenAI, AWS Bedrock, Ollama, and other LLMs via the MCP server, letting agents use MemMachine memory regardless of the underlying model provider.
Flexible Deployment & Data Control
Run locally, self-host in the cloud, or install via pip. Full control over data storage and persistence, with pluggable database backends (graph DB for episodic, SQL for profile memory).
SDKs, REST API & Playground
Provides a Python SDK, RESTful API and an interactive playground plus documentation and community support to simplify integration and development.
Open-Source with Extensible Architecture
Open-source codebase and docs enable customization, community contributions, and extension for domain-specific memory strategies and retrieval policies.
How to Use MemMachine
1) Install or deploy MemMachine: pip install or self-host the MemMachine server (follow docs). 2) Configure storage: set up the database backends (graph DB for episodic memory, SQL for profile memory) and update connection settings. 3) Integrate your agent/LLM: use the Python SDK, REST API, or MCP server to connect your agent to MemMachine and define what to record (episodic vs profile). 4) Test and tune: use the playground to simulate conversations, verify retrieval/relevance, and adjust memory retention and retrieval policies.