Honcho vs Hy Memory

Honcho

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

Hy Memory

An Apache-2.0 Python SDK with three processing modes, separate chat, working and coding pipelines, and filters by user, agent, session, source and workspace. Deletion does not stick everywhere. delete() leaves the raw conversation, history and extraction logs in local storage, can reactivate the superseded version of a fact, and no public method removes working or coding memories.

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

Hy Memory

1. Pass user_id on every add() and user_ids on every search(). Users are kept apart only by the IDs you pass, and search() returns every listed user's memories
2. Choose the mode before writing. lite makes no LLM calls and stores raw text. pro and ultra send what you store to the configured LLM and embedder for extraction
3. Don't rely on delete() to erase a fact. The raw conversation, history.db and cache.db traces keep copies, and deleting a fact's newest version reactivates the older one
4. Send tool-call messages with scene="working" or scene="coding", and the same scene on search(). Without tool messages a write falls back to chat memory
5. Keep python -m hy_memory.server on 127.0.0.1. It has no authentication and allows any web origin