Summary
What an agent worked out last session is still there in the next one.
The model is deliberately small — entities carry observations, relations join entities in active voice, and everything else is a way in or out of that — which is what makes it predictable to write against. Deletion is the part worth noticing: delete_observations drops one fact without touching the entity holding it, so a long-lived graph gets corrected rather than rebuilt.
What it is
A persistent knowledge-graph store with 9 tools over entities, the relations between them, and the observations attached to each.
What you get
- Entities and relations created in batches, so a set of facts learned together goes in as one call
- add_observations attaching new facts to entities that already exist, without rewriting the entity
- Deletion at three levels: whole entities with their relations, individual observations, or specific relations
- read_graph for the whole structure, search_nodes for a query across it, and open_nodes to pull named nodes straight out
Requirements
Nothing to supply — no account, no key; the graph is the server's own store. MIT licensed.
Setup effort
One command — npx -y @modelcontextprotocol/server-memory