Mem0 is a universal, self-improving memory layer for AI agents and LLM applications that combines vector databases, knowledge graphs, and key-value stores into a hybrid datastore architecture. With over 51,000 GitHub stars, Mem0 delivers 26% higher accuracy and 90% token savings compared to baselines like OpenAI Memory on the LOCOMO benchmark.

• Hybrid Datastore – Combines vector stores for similarity search, knowledge graphs for entity relationships, and key-value stores for metadata filtering
• Multi-Level Memory – Short-term, long-term, user-scoped, session-scoped, and agent-scoped memory namespaces
• Self-Improving – Dynamic consolidation and rolling summaries for coherent long-horizon reasoning
• Graph Enhancement (Mem0g) – Entity-relation graphs for structured recall beyond vector similarity
• LLM-Agnostic – Works with any LLM provider including OpenAI, Anthropic, and open-source models
• Flexible Storage – Supports Qdrant, Pinecone, Milvus, Chroma, pgvector, Redis, and SQLite

Mem0 employs a two-phase memory pipeline:

Extraction Phase: Ingests the latest user exchange, a rolling long-term summary, and the most recent messages. An LLM extracts concise candidate memories as salient factual phrases. For graph-enhanced Mem0g, an Entity Extractor identifies nodes and a Relations Generator infers labeled edges.

Update Phase: Consolidates memories asynchronously to maintain a coherent, non-redundant store. Background processes refresh summaries without stalling inference.

Mem0 supports multi-level namespaces for scoped persistence:

• Short-term – Derived from recent messages; stored as atomic semantic facts to minimize size
• Long-term – Consolidated facts across sessions using rolling summaries and graph structures
• User-scoped – Isolated to individual user_id; stores preferences and history
• Session-scoped – Temporary, conversation-specific memories
• Agent-scoped – Global or shared facts across users and agents

On the LOCOMO benchmark:

• Mem0: 66.9% accuracy vs OpenAI Memory's 52.9%
• Mem0g (graph-enhanced): 68.4% accuracy
• p95 latency: 1.44s (91% lower than baselines)
• Token savings: 90% compared to full-context RAG approaches

from mem0 import Memory
 
config = {
    "vector_store": {
        "provider": "qdrant",
        "config": {"host": "localhost", "port": 6333}
    },
    "llm": {
        "provider": "openai",
        "config": {"model": "gpt-4o", "temperature": 0}
    }
}
m = Memory.from_config(config)
 
# Add memories from a conversation
m.add("I prefer Python for backend and React for frontend",
      user_id="alice", metadata={"topic": "preferences"})
m.add("My current project uses FastAPI with PostgreSQL",
      user_id="alice", metadata={"topic": "project"})
 
# Retrieve relevant memories
memories = m.search("What tech stack does Alice use?",
                     user_id="alice", limit=5)
for mem in memories:
    print(f"[{mem['score']:.2f}] {mem['memory']}")

• Mem0 GitHub Repository
• Mem0 Official Website
• Mem0 Research Paper
• Mem0 Documentation

• Qdrant – Vector database backend for Mem0
• Milvus – Alternative vector database backend
• ChromaDB – Lightweight embedding database
• Dify – Agentic workflow platform
• MCP Servers – MCP protocol for agent integrations