FalkorDB

Because FalkorDB holds graphs as sparse adjacency matrices, its queries run as linear algebra. The project describes itself as the first queryable property graph database to represent adjacency this way, with low latency for knowledge graphs that feed large language models as the stated goal.

Data follows the property graph model, so nodes and relationships carry attributes, and queries are written in openCypher with extensions of the project's own. A single container starts the database together with a browser interface, and any Redis client can send its graph commands directly.

• Multi-tenancy: one instance holds many graphs, each selected by name from the client.
• Official clients: Python, Java, Node, Rust and Go libraries are maintained by the project itself.
• Browser UI: a visualization interface is reachable on a local port once the container is running.
• Vector search: graph traversal combines with vector similarity for agent memory and recommendations.
• GraphRAG SDK: a companion project builds retrieval applications on top of the database.

The use cases the project names are GraphRAG, agentic AI, chatbots, fraud detection, security graphs and AI retrieval, all places where the relationships matter as much as the records.

The code is published under the Server Side Public License, a managed cloud runs alongside the self-hosted build, and demos in the repository give a first look at the engine.