FalkorDB is a high-performance graph database designed for knowledge graphs, particularly for LLM applications like GraphRAG. It targets developers needing low-latency access to interconnected data, offering a unique approach using sparse matrices and linear algebra for querying.

How It Works

FalkorDB represents graph adjacency matrices using sparse matrices, optimizing storage and query performance. It leverages linear algebra for query execution, a novel approach for graph databases that enhances computational efficiency. The database adheres to the Property Graph Model and supports OpenCypher query language with extensions.

Quick Start & Requirements

• Install/Run: docker run -p 6379:6379 -p 3000:3000 -it --rm -v ./data:/var/lib/falkordb/data falkordb/falkordb:edge
• Prerequisites: Redis 6.2 or later. For building from source, requires build-essential, cmake, m4, automake, peg, libtool, autoconf, python3, python3-pip. macOS users may need brew install gcc g++ and libomp.
• Setup: Docker image launch is near-instantaneous. Building from source requires compilation.
• Docs: Official Docs | Demos

Highlighted Details

• First queryable Property Graph database to use sparse matrices and linear algebra for querying.
• Optimized for low-latency knowledge graph operations, crucial for GraphRAG.
• Supports OpenCypher query language.
• Offers official client libraries for Python, Java, Node.js, and Rust.

Maintenance & Community

• Active development by the FalkorDB team.
• Community support via GitHub Discussions.
• Contribution guidelines are available.

Licensing & Compatibility

• Licensed under the Server Side Public License v1 (SSPLv1).
• SSPLv1 is a strong copyleft license; commercial use or linking with closed-source applications may require careful review and potential licensing agreements.

Limitations & Caveats

The SSPLv1 license may impose restrictions on commercial use and cloud offerings. Building from source requires a specific set of development tools and may have platform-specific dependencies (e.g., OpenMP on macOS).