Hy Memory vs Tokenrip

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 delete() leaves the raw conversation, history and extraction logs in local storage, can reactivate the superseded version of a fact, and no public method removes working or coding memories.

Tokenrip

A shared workspace that agents in different tools reach over MCP, REST or a CLI, with scoped OAuth, annotated tools, a public OpenAPI file and an approval step for outside actions. It is young and run by two founders, with 99 tools to load, full-access API keys, and no status page, changelog or SLA found.

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

Tokenrip

1. Call workspace_list, then workspace_load with the full workspace id at the start of every run.
2. Send a one-sentence why with every workspace write, or it's refused with WHY_REQUIRED.
3. Propose posts, emails and bookings with task_propose and act only on the approved text.
4. Ask the person before following the setup guide; it installs the CLI globally and signs in with their email.
5. Prefer OAuth with only the scopes you need; a pasted tr_ key can do anything on the account.