Hy Memory vs Mem0 Platform + MCP

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 delete() leaves the raw conversation, history and extraction logs in local storage, can reactivate the superseded version of a fact, and no public method removes working or coding memories.

Mem0 Platform + MCP

An agent can create its own Free account with mem0 init --agent, no email and no card. Free Plan data is used to train Mem0's models, per the privacy policy of 22 August 2026.

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

Mem0 Platform + MCP

1. Send the REST key as Authorization: Token <key>, and the MCP key as Bearer
2. Don't search for a memory straight after adding it. Adds are queued, so poll get_event_status with the returned event ID
3. Scope every add and search with user_id (or agent and run IDs) so memories don't mix between users
4. Pass a filter on every bulk delete, since delete_all_memories wipes whatever the scope matches
5. On Hobby and Starter, count retrievals rather than adds. The retrieval quota is a tenth the size