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FalkorDB

FalkorDB

Low-latency, multi-tenant property graph database that runs queries as sparse-matrix linear algebra (GraphBLAS); OpenCypher over the Redis protocol, built for GraphRAG and agent memory.

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Curator's take

Choose it when a knowledge graph or agent memory sits in the hot path and Neo4j feels heavy — Redis-speaking, millisecond OpenCypher, many isolated graphs per instance, one `docker run` with a browser UI. The license is SSPLv1, not OSI open source: fine to use internally, a problem if you plan to offer it as a hosted service. Need vectors and full-text fused in one engine? helix-db.

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README.md1 min read
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FalkorDB

Ultra-fast, Multi-tenant Graph Database

Powering Generative AI, Agent Memory, Cloud Security, and Fraud Detection

FalkorDB%2FFalkorDB | Trendshift

FalkorDB GitHub Repo - Video - 640x365

UNIQUE FEATURES

Our goal is to build a high-performance Knowledge Graph tailored for Large Language Models (LLMs), prioritizing exceptionally low latency to ensure fast and efficient information delivery through our Graph Database.

🆕 FalkorDB is the first queryable Property Graph database to leverage sparse matrices for representing the adjacency matrix in graphs and linear algebra for querying.

Key Features

  • Sparse Matrix Representation: Utilizes sparse matrices to represent adjacency matrices, optimizing storage and performance.

  • Linear Algebra Querying: Employs linear algebra for query execution, enhancing computational efficiency.

  • Property Graph Model Compliance: Supports nodes and relationships with attributes, adhering to the Property Graph Model.

  • OpenCypher Support: Compatible with OpenCypher query language, including proprietary extensions for advanced querying capabilities.

Explore FalkorDB in action by visiting the Demos.

GET STARTED

Step 1

To quickly try out FalkorDB, launch an instance using docker:

docker run -p 6379:6379 -p 3000:3000 -it --rm -v ./data:/var/lib/falkordb/data falkordb/falkordb

Step 2

Then, open your browser and navigate to http://localhost:3000.

You can also interact with FalkorDB using any of the supported Client Libraries

MotoGP League Example

In this example, we'll use the FalkorDB Python client to create a small graph representing a subset of motorcycle riders and teams participating in the MotoGP league. After creating the graph, we'll query the data to explore its structure and relationships.

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