Open source · Apache 2.0
Context graph DB for AI agents.
A single-binary, Neo4j-compatible graph engine that gives your agents a fast, portable brain.
7 ms
cold start
~15 MB
idle memory
5,588/s
writes
Apache 2.0
forever free
~98.7%
fewer tokens
Token Efficiency
Cut LLM token costs by ~98.7%.
Instead of dumping the whole knowledge base into context every turn, CognoDB retrieves only the connected neighborhood relevant to the current query. At 2,000 entities, that drops from 202,285 tokens per query down to just 2,668 — a savings of ~199,617 tokens per query.
The savings widen as your knowledge base grows. Baseline tokens scale linearly; graph-retrieval tokens stay flat, bounded by neighborhood size — not corpus size.
Baseline tokens
202,285
per query @ 2K entities
Graph retrieval
2,668
per query @ 2K entities
Saved per query
~199,617
tokens avoided
Cost saved / 1K queries
~$599
at $3 / 1M tokens
Explore
Nine chapters. Pick one and go deep.
The Problem
Why every other database fails
Vectors, rows, documents, and analytical graphs all miss what agents actually need.
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The Solution
A graph brain in your pocket
One static binary. Zero dependencies. Bolt + Cypher native.
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Why graphs
The right shape for memory
Agents think in relationships, not rows. Traverse, don't search.
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7 milliseconds
A database that is a function call
Born, used, and killed inside a single agent execution.
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Storage
Pluggable storage backends
In-memory, local, MongoDB today. Postgres, MySQL, Oracle, Cassandra next.
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Comparison
One axis that matters
Fitness for context graphs, head-to-head with Neo4j, Mongo, Postgres, vectors.
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Benchmarks
Numbers you can reproduce
Cold start, memory, writes, traversal. Run the harness yourself.
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Migration
Already on Neo4j?
Same Bolt. Same Cypher. Usually a one-line change.
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How it works
Drop-in. Radically simpler.
From your first MERGE to a sharded knowledge base.
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Give your agents a memory that travels.
Free, open source, and built by Wexa AI.