Hy Memory vs Memory (MCP reference server)

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 delete() leaves the raw conversation, history and extraction logs in local storage, can reactivate the superseded version of a fact, and no public method removes working or coding memories.

Memory (MCP reference server)

Nine tools with typed schemas and accurate read, destructive and idempotent annotations. No pagination or limits. read_graph returns everything and search_nodes every match.

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

Memory (MCP reference server)

1. Set MEMORY_FILE_PATH to an absolute path. The default sits inside the npx cache
2. Use search_nodes or open_nodes instead of read_graph once the graph has more than a few hundred entities
3. Make memory writes one at a time. Parallel calls in one turn can overwrite each other in 2026.8.31
4. Create both entities before create_relations. A missing endpoint fails the whole batch
5. Keep observations short and factual. They come back verbatim in every later read