The Graph platform developers use to achieve accurate GraphRAG for enterprise GenAI
About FalkorDB
FalkorDB delivers an accurate, multi-tenant RAG solution powered by a low-latency, scalable graph database technology.- Purpose-built for development teams working with complex, interconnected data—whether structured or unstructured—in real-time or interactive user environments.
- Supports the OpenCypher query language with proprietary enhancements that streamline interactions with graph data. Its efficient graph traversal and query capabilities make it well-suited for production environments.
Choose Your Path
- Graph Database Path: If you’re interested in using FalkorDB as a property graph database with OpenCypher support, continue with the sections below.
- GraphRAG Path: If you’re aiming to implement advanced graph reasoning and generative AI tasks, explore our GenAI Tools section, starting with the GraphRAG SDK.
Primary Features
- Adopts the Property Graph Model
- Supports OpenCypher query language with proprietary extensions
- Offers Full-Text Search, Vector Similarity, and Range indexing for efficient querying
- Supports both RESP and Bolt protocols for flexible connectivity
- Uses sparse adjacency matrix representation for efficient graph storage
- Provides GraphRAG capabilities through the GraphRAG SDK for advanced graph reasoning and generative AI tasks
Get Started
Launch an instance using Docker, or use FalkorDB CloudPorts Exposed
-
6379 (FalkorDB Server)
Use this port to connect via the CLI or any FalkorDB-compatible client. -
3000 (FalkorDB Browser)
Access the FalkorDB web UI by opening your browser at: http://localhost:3000
📖 New to FalkorDB? Follow the step-by-step Getting Started guide for a complete walkthrough — from setup to modeling, loading, and querying your first graph.Here we’ll use FalkorDB Python client to create a small graph representing a subset of motorcycle riders and teams taking part in the MotoGP league, once created we’ll start querying our data.
Client libraries
Language-specific clients have been written by the community and the FalkorDB team. The full list and links can be found on the Client Libraries page.Data import
When loading large graphs from CSV files, use the falkordb-bulk-loader:GitHub Discussions
Got questions? Please contact us at the FalkorDB forum.License
FalkorDB is licensed under the the Server Side Public License v1 (SSPLv1).Frequently Asked Questions
What is FalkorDB and how does it differ from other graph databases?
What is FalkorDB and how does it differ from other graph databases?
FalkorDB is a high-performance graph database built as a Redis module. Unlike traditional graph databases, it uses a sparse adjacency matrix representation (based on GraphBLAS) for efficient storage and fast traversals. It supports OpenCypher queries, both RESP and Bolt protocols, and provides built-in GraphRAG capabilities for GenAI applications.
What are the main use cases for FalkorDB?
What are the main use cases for FalkorDB?
FalkorDB excels at use cases involving complex, interconnected data: social networks, recommendation engines, knowledge graphs, fraud detection, and GraphRAG for GenAI applications. Its low-latency design makes it ideal for real-time and interactive user environments.
How do I get started with FalkorDB?
How do I get started with FalkorDB?
The quickest way is Docker:
docker run -p 6379:6379 -p 3000:3000 -it --rm falkordb/falkordb:latest. This starts the server and a browser UI at http://localhost:3000. Then install a client library and follow the Getting Started guide.Does FalkorDB support cloud deployment?
Does FalkorDB support cloud deployment?
Yes. FalkorDB Cloud provides a fully managed, multi-tenant graph database service. You can create a free instance and skip local setup entirely.
What query language does FalkorDB use?
What query language does FalkorDB use?
FalkorDB uses OpenCypher with proprietary extensions. It supports full-text search, vector similarity search, and range indexing. See the Cypher documentation for the complete query language reference.