MemMachine/MemMachine

Universal memory layer for AI Agents. It provides scalable, extensible, and interoperable memory storage and retrieval to streamline AI agent state management for next-generation autonomous systems.

What it solves

MemMachine provides a persistent long-term memory layer for AI agents, preventing them from being stateless. It allows agents to remember user preferences, past conversations, and specific facts across different sessions, restarts, and even when the underlying LLM is changed.

How it works

MemMachine acts as an external memory system that agents interact with via a RESTful API, Python/TypeScript SDKs, or a Model Context Protocol (MCP) server. It categorizes memory into three types:

  • Working Memory: Short-term context for the current session.
  • Episodic Memory: Long-term conversational context stored in a graph database (Neo4j).
  • Profile Memory: Long-term user facts and preferences stored in SQL.

Who it’s for

  • Developers building AI agents, autonomous workflows, or personalized assistants.
  • Researchers exploring cognitive models and agent architectures.
  • Teams requiring cross-session persistence for LLM applications.

Highlights

  • Multi-tier Memory: Separate systems for short-term, episodic (graph-based), and profile (SQL-based) memory.
  • Broad Integration: Compatible with LangChain, LangGraph, CrewAI, LlamaIndex, and n8n.
  • LLM Agnostic: Works with any provider, including OpenAI, Anthropic, Bedrock, and Ollama.
  • MCP Support: Native support for Model Context Protocol to integrate with tools like Claude Desktop and Cursor.

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