FalkorDB/FalkorDB

A super fast Graph Database uses GraphBLAS under the hood for its sparse adjacency matrix graph representation. Our goal is to provide the best Knowledge Graph for LLM (GraphRAG).

What it solves

FalkorDB is a high-performance, multi-tenant graph database designed to provide exceptionally low latency for information delivery. It is specifically tailored for Large Language Models (LLMs), serving as a Knowledge Graph to power Generative AI, agent memory, and complex data relationship mapping for fraud detection and cloud security.

How it works

Unlike traditional graph databases, FalkorDB uses sparse matrices to represent the adjacency matrix of the graph and employs linear algebra for query execution. This approach optimizes storage and performance. It is compliant with the Property Graph Model and supports the OpenCypher query language, allowing users to define nodes and relationships with attributes.

Who it’s for

It is built for developers and AI engineers who need a fast, scalable graph database to manage structured knowledge for LLMs, AI agents, and security-focused data analysis.

Highlights

  • Sparse Matrix Representation: Uses linear algebra and sparse matrices to optimize graph storage and traversal.
  • OpenCypher Support: Compatible with the OpenCypher query language for flexible querying.
  • Multi-tenant Architecture: Designed for high-performance, multi-tenant environments.
  • Broad Client Support: Provides official client libraries for Java, Python, Node.js, Rust, and Go.

Related

  • Project
    FalkorDB/GraphRAG-SDK

    GraphRAG‑SDK is a Python SDK that builds a knowledge graph in FalkorDB from your documents and then answers questions by traversing that graph. It adds provenance edges, multi‑hop retrieval, and optional abstention to dramatically cut down LLM hallucinations, and it ships with benchmarks showing it outperforms standard vector‑RAG. Install via pip, run a FalkorDB container, ingest texts (or PDFs/markdown), call `finalize()`, and query with `completion()`. The library supports schemas, incremental updates, custom providers, and is Apache‑2.0 licensed.

  • Project
    HelixDB/helix-db

    HelixDB is an open‑source Rust database that natively blends graph and vector storage, exposing a language‑agnostic query API for AI agents. It ships with a CLI that can spin up a local instance, scaffold projects, and even hand off to LLM‑based code generators. SDKs for Rust, TypeScript, Python and Go let you build queries as code, which are sent as JSON ASTs to the server. A managed cloud offering adds HA, auto‑scaling readers and integrated full‑text/vector search, making HelixDB suitable for RAG, agent memory, and knowledge‑graph applications.

  • Project
    FalkorDB/code-graph

    A knowledge graph visualization tool that transforms codebases into searchable graphs to analyze dependencies and enable GraphRAG-powered chat for developers.

  • Project
    HKUDS/LightRAG

    A lightweight, graph-based RAG framework that combines knowledge graphs and vector embeddings to provide efficient, high-quality retrieval and global context understanding.

  • Project
    docker/genai-stack

    A Docker-based starter stack for building GenAI applications, featuring integrated LLMs and Neo4j for RAG and knowledge graph capabilities.