Honcho vs LangMem

Honcho

Pay per call over x402 or MPP at agentcash.honcho.dev, with free read endpoints. No published rate limits, 429 guidance or SLA.

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.

Honcho

1. Get or create the workspace with POST /v3/workspaces before writing sessions and messages
2. Batch up to 100 messages per request, each under 25,000 characters
3. Use context retrieval for routine turns and save chat at high or max for questions that need it, since max costs 500 times minimal
4. Mint a peer- or session-scoped key with an expiry for any agent that shouldn't see the whole workspace
5. Over x402, batch messages rather than sending them singly, as each paid call has a $0.001 floor

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