Cognee vs LangMem

Cognee

Apache-2.0 library, REST server and MCP server, all self-hostable, with local models and no LLM key since 1.6.0. No status page, rate limits, SLA or SOC 2 for Cloud, and Cloud runs in AWS us-east-1 with no EU region.

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.

Cognee

1. Use remember, recall and forget. The older cognify, search and delete MCP tools are gone
2. Always include /api/v1 in REST paths
3. Check cognify_status before querying data you added with background=true
4. Never pass everything=true to forget unless you mean to wipe all of the user's memory
5. Set a client timeout on Cloud calls and treat HTTP 402 as an empty balance, since an empty balance has also shown up as hangs

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