Comparisons

How Semvec relates to the other LLM-memory layers Python developers commonly evaluate.

Comparison	Type	Headline
vs mem0	Architectural + head-to-head benchmark	LOCOMO J 0.605 vs mem0 0.669 (within 6 pp, different cost class), zero ingest LLM calls vs mem0's one-per-turn
vs Letta (MemGPT)	Architectural only (not benchmarked)	Math-driven state vs. LLM-managed memory paging
vs LangChain Memory	Architectural only (not benchmarked)	Single O(1) memory layer vs. a family of memory classes you compose

We have only run a head-to-head benchmark against mem0. The Letta and LangChain Memory pages describe architectural differences only, not measured performance gaps.

When should I choose Semvec over mem0, Letta, or LangChain Memory?

Choose Semvec when you need O(1) per-turn input cost, deterministic recall, exact-value preservation (IBANs, amounts, IDs, dates), an LLM-free ingest path, on-premises or air-gapped deployment, or a signed-deletion GDPR compliance pack.

Choose mem0 when you want an OSS-licensed turnkey memory layer and you are comfortable with LLM-driven fact extraction per turn.

Choose Letta (MemGPT) when you want LLM-driven adaptive memory paging out of the box (the agent decides what to keep in context vs. swap out).

Choose LangChain Memory when you want the broader LangChain / LCEL ecosystem and are happy to compose memory primitives yourself.