Build with AI
Memory for AI agents
Context windows forget. An agent whose knowledge lives in a graph traverses what it knows — what did I decide about X, what depends on it, who said what about it last Tuesday — instead of replaying its own history and hoping.
The model
The shape of the problem
Agent memory isn't a list of paragraphs to retrieve by similarity. It's a web of entities — people, tools, facts, decisions, tasks — connected by typed edges that carry meaning. Stored as tokens, that structure is flattened and re-inferred every prompt, at full corpus cost. Stored as a graph, retrieval becomes a traversal with a defined answer, bounded by neighbourhood size — which is why graph retrieval cuts token spend by ~98.7% at 2,000 entities.
A database that's a function call
When the engine starts in milliseconds, a graph can be born when the agent starts and gone when it finishes — context scoped to a run, real-time rather than batch, zero standing footprint while idle. Per-task isolation stops being a prompt-engineering problem and becomes a credential boundary.
MCP makes the graph a first-class tool
The MCP server gives any compatible client two capabilities: read the schema, run a query. The agent discovers what its memory contains and composes its own questions — no bespoke tool per question you anticipated.
Snapshots, replay, and provenance for free
Every fact links to where it came from, so the citation is always a hop away. Backups restore to a new instance, which makes a reasoning session replayable by construction — fork the snapshot, explore the what-if, and the original is never touched.
MATCH (a:Agent {id: $agent})-[:LEARNED]->(f:Fact)-[:ABOUT]->(e:Entity {name: $entity})
MATCH (f)-[:FROM]->(src)
RETURN f.statement, src.title, f.at
ORDER BY f.at DESCQuestions
Agent memory, asked directly.
Why a graph instead of a vector store?
They answer different questions. Similarity search finds text that sounds related; a graph answers structural questions exactly — what depends on this, what did I already decide, which source said so. Many stacks run both; the graph is the part that makes answers auditable.
How does the agent actually query it?
Over MCP: the server exposes schema inspection and Cypher execution to Claude, Cursor and any MCP-compatible client. Read-only mode exists for agents that should explore but never write.
One shared graph or one per agent?
Per agent (or per session) is the pattern CognoDB is built for — instances provision quickly and bill by the second, so isolation costs almost nothing while idle. Share a graph only when agents genuinely need shared memory.
What happens when a session ends?
Your choice: keep the instance as persistent memory, pause it (storage-only cost), or delete it. Deletion is soft for 30 days with a final snapshot.
How do facts get in?
The agent writes them — MERGE for entities so repeated observations converge, CREATE for events. The same Bolt connection the drivers use; nothing agent-specific to deploy.
Can I inspect what an agent has learned?
Yes — it's a database. Open it in the console's browser, run Cypher against it, or point a second read-only MCP client at it and ask.
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~98.7%
token efficiency at 2,000 entities — see the footnotes above
Put your first graph up in a minute.
A free instance takes about a minute and no card. Write two MERGE statements, read them back, and you have a living graph — with provenance on every fact.
First-graph path
LiveCreate a free instance
No card. Ready in about a minute.
Connect your driver
bolt+ssc:// URI into the driver you already use.
Write two MERGEs
That's the entire shape of agent memory.
Point an agent at it
One MCP config block. No integration code.