Hy Memory vs Zep

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 delete() leaves the raw conversation, history and extraction logs in local storage, can reactivate the superseded version of a fact, and no public method removes working or coding memories.

Zep

Facts that new data invalidates keep the time they stopped being true. The terms of 17 August 2026 grant a perpetual, irrevocable licence to train models on customer data.

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

Zep

1. Create the user and a thread before adding messages, as the quickstart does
2. Set return_context when adding messages to skip a second round trip
3. Keep episodes short. Every 350 bytes past the first costs another credit
4. Read Retry-After on a 429 and back off with jitter. Free accounts over quota drop to 5 requests a minute
5. Expect a delay after a graph add (202 Accepted) before new facts show up in search