Graphiti vs LangMem

Graphiti

Apache-2.0, with FalkorDB, Neo4j or Amazon Neptune as the store. Every add runs LLM extraction, so ingestion costs tokens and time.

LangMem

Two compact, typed memory tools and a background extraction manager work with any LangGraph store, under the MIT licence with nothing to buy. The newest PyPI release, 0.0.30, dates from 27 October 2025. The repository has no changelog, tags or test workflow, and 23 of 54 open issues have no reply.

Graphiti

1. Set OPENAI_API_KEY (or another provider's key) and MODEL_NAME before starting the MCP server
2. Use search_memory_facts for facts and search_nodes for entities instead of reading whole episodes
3. Never call clear_graph to fix one fact. Use delete_episode or delete_entity_edge
4. Pass group_ids on every search and add so one user's graph doesn't leak into another's
5. Call get_status to check the database connection before a long ingest

LangMem

1. Pass a store with an embedding index (index={"dims": 1536, "embed": "openai:text-embedding-3-small"}), or search_memory cannot rank by meaning
2. Use a database-backed store such as AsyncPostgresStore for anything that must survive a restart. InMemoryStore loses everything
3. Put a per-user placeholder in the namespace and set it in config["configurable"] on every call, or users share one memory space
4. Send the memory id with update and delete, and never with create. A retried create writes a duplicate under a new UUID
5. Run on Python 3.11 or later. src/langmem/knowledge/extraction.py uses typing.NotRequired, which Python 3.10 lacks