====== Mem0 ======
**Mem0** is a universal, self-improving memory layer for AI agents and LLM applications that combines vector databases, [[knowledge_graphs|knowledge graphs]], and key-value stores into a hybrid datastore architecture.(([[https://github.com/mem0ai/mem0|Mem0 GitHub Repository]])) 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.(([[https://mem0.ai/research|Mem0 Research Paper]]))

| **Repository** | [[https://[[github|github]].com/mem0ai/mem0|github.com/mem0ai/mem0]] |
| **License** | Apache 2.0 |
| **Language** | Python |
| **Stars** | 51K+ |
| **Category** | Agent Memory Layer |

===== Key Features =====
  * **Hybrid Datastore**, Combines vector stores for similarity search, [[knowledge_graphs|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|OpenAI]], [[anthropic|Anthropic]], and open-source models
  * **Flexible Storage**, Supports [[qdrant|Qdrant]], [[pinecone|Pinecone]], Milvus, Chroma, [[pgvector|pgvector]], Redis, and [[sqlite|SQLite]]

===== Architecture =====
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.

<mermaid>
graph TB
    subgraph Input["Input Processing"]
        Msg[User Message]
        Ctx[Conversation Context]
        Sum[Rolling Summary]
    end
    subgraph Extract["Extraction Phase"]
        LLM[LLM Extractor]
        Entity[Entity Extractor]
        Rel[Relations Generator]
    end
    subgraph Store["Hybrid Datastore"]
        VS[(Vector Store)]
        KG[(Knowledge Graph)]
        KV[(Key-Value Store)]
    end
    subgraph Retrieve["Retrieval Phase"]
        VecSearch[Vector Similarity]
        GraphTraverse[Graph Traversal]
        KVLookup[Key-Value Lookup]
        Rank[Ranking and Fusion]
    end
    subgraph Output["Memory Output"]
        Memories[Ranked Memories]
        Context[Enriched Context]
    end
    Input --> Extract
    LLM --> VS
    Entity --> KG
    Rel --> KG
    Extract --> Store
    Store --> Retrieve
    VecSearch --> Rank
    GraphTraverse --> Rank
    KVLookup --> Rank
    Rank --> Output
</mermaid>

===== Memory Types =====
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

===== Performance =====
On the LOCOMO benchmark:(([[https://mem0.ai/research|Mem0 Research Paper]]))
  * **Mem0**: 66.9% accuracy vs [[openai|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

===== Code Example =====
<code python>
from mem0 import Memory

config = {
    "vector_store": {
        "provider": "[[qdrant|qdrant]]",
        "config": {"host": "localhost", "port": 6333}
    },
    "llm": {
        "provider": "[[openai|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']}")
</code>

===== See Also =====
  * [[memgraph|Memgraph]]
  * [[letta|Letta]]
  * [[agent_memory_frameworks|Agent Memory Frameworks]]
  * [[cognitive_memory_architectures|Cognitive Memory Architectures]]
  * [[continual_learning_agents|Continual Learning Agents]]

===== References =====
  * [[https://github.com/mem0ai/mem0|Mem0 GitHub Repository]]
  * [[https://mem0.ai|Mem0 Official Website]](([[https://mem0.ai|Mem0 Official Website]]))
  * [[https://mem0.ai/research|Mem0 Research Paper]]
  * [[https://docs.mem0.ai|Mem0 Documentation]](([[https://docs.mem0.ai|Mem0 Documentation]]))