Mem0 — Memory Layer for AI Agents

Introduction

Mem0 (pronounced "mem-zero") is a self-improving memory layer for AI applications. It solves the stateless nature of LLMs by providing persistent, intelligent memory that learns from user interactions, remembers preferences, and adapts over time.

Core capabilities:

• Multi-Level Memory — Store memories at user, session, and agent levels. User preferences persist across sessions while session context stays scoped
• Intelligent Extraction — Automatically identifies and stores relevant facts from conversations without manual tagging
• Semantic Search — Find relevant memories using natural language queries, not just keyword matching
• Memory Consolidation — Automatically merges related memories, resolves conflicts, and keeps the memory store clean
• Graph Memory — Optional knowledge graph storage for complex relationship tracking between entities
• Self-Improving — Memory quality improves over time as the system learns what information is actually retrieved and useful
• Multiple Backends — Works with Qdrant, ChromaDB, pgvector, and other vector stores. Supports OpenAI, Anthropic, and local embeddings

51,000+ GitHub stars. Used by 100,000+ developers. Reduces token usage by 90% for returning users by loading only relevant context.

FAQ

Q: How is Mem0 different from just using a vector database? A: Vector databases store and retrieve embeddings, but Mem0 handles the full memory lifecycle: extraction from conversations, deduplication, conflict resolution, temporal decay, and intelligent retrieval. It's a memory system, not just storage.

Q: Does it work with any LLM? A: Yes. Mem0 supports OpenAI, Anthropic Claude, Google Gemini, Mistral, and any LiteLLM-compatible provider for both memory extraction and retrieval.

Q: Where are memories stored? A: By default, memories are stored locally using an embedded vector store. For production, you can configure external stores like Qdrant, ChromaDB, or PostgreSQL with pgvector.

Q: Can I use it with existing chat applications? A: Yes. Mem0 integrates with LangChain, CrewAI, AutoGen, and any Python or Node.js application. Add 3-5 lines of code to any chat loop to enable memory.

Works With

• OpenAI / Anthropic / Google / local LLMs for memory extraction
• Qdrant / ChromaDB / pgvector for vector storage
• Neo4j for graph memory (optional)
• LangChain / CrewAI / AutoGen / any agent framework
• Python and Node.js SDKs