FalkorDB/FalkorDB

FalkorDB Ultra-fast, Multi-tenant Graph Database Powering Generative AI, Agent Memory, Cloud Security, and Fraud Detection 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. from falkordb import FalkorDB # Connect to FalkorDB db = FalkorDB ( host = 'localhost' , port = 6379 ) # Create the 'MotoGP' graph g = db . select_graph ( 'MotoGP' ) g . query ( """CREATE (:Rider {name:'Valentino Rossi'})-[:rides]->(:Team {name:'Yamaha'}), (:Rider {name:'Dani Pedrosa'})-[:rides]->(:Team {name:'Honda'}), (:Rider {name:'Andrea Dovizioso'})-[:rides]->(:Team {name:'Ducati'})""" ) # Query which riders represents Yamaha? res = g . query ( """MATCH (r:Rider)-[:rides]->(t:Team) WHERE t.name = 'Yamaha' RETURN r.name""" ) for row in res . result_set : print ( row [ 0 ]) # Prints: "Valentino Rossi" # Query how many riders represent team Ducati ? res = g . query ( """MATCH (r:Rider)-[:rides]->(t:Team {name:'Ducati'}) RETURN count(r)""" ) print ( res . result_set [ 0 ][ 0 ]) # Prints: 1 USING FALKORDB You can call FalkorDB's commands from any Redis client. Here are several methods: With redis-cli $ redis-cli 127.0.0.1: 6379> GRAPH.QUERY social " CREATE (:person {name: 'roi', age: 33, gender: 'male', status: 'married'}) " With any other client You can interact with FalkorDB using your client's ability to send raw Redis commands. Note: Depending on your client of choice, the exact method for doing that may vary. Example: Using FalkorDB with a Python client This code snippet shows how to use FalkorDB with from Python using falkordb-py : from falkordb import FalkorDB # Connect to FalkorDB db = FalkorDB ( host = 'localhost' , port = 6379 ) # Select the social graph g = db . select_graph ( 'social' ) reply = g . query ( "CREATE (:person {name:'roi', age:33, gender:'male', status:'married'})" ) CLIENT LIBRARIES Note: Some languages have client libraries that provide support for FalkorDB's commands: Official Clients Project Language License Author Stars Package Comment jfalkordb Java BSD FalkorDB Maven falkordb-py Python MIT FalkorDB pypi falkordb-ts Node.JS MIT FalkorDB npm falkordb-rs Rust MIT FalkorDB Crate falkordb-go Go BSD FalkorDB GitHub NFalkorDB C# Apache-2.0 FalkorDB nuget Additional Clients Project Language License Author Stars Package Comment nredisstack .NET MIT Redis nuget redisgraph-rb Ruby BSD Redis GitHub redgraph Ruby MIT pzac GitHub redisgraph-go Go BSD Redis GitHub rueidis Go Apache 2.0 Rueian GitHub ioredisgraph JavaScript ISC Jonah GitHub @hydre/rgraph JavaScript MIT Sceat GitHub php-redis-graph PHP MIT KJDev GitHub redisgraph_php PHP MIT jpbourbon GitHub redisgraph-ex Elixir MIT crflynn GitHub redisgraph-rs Rust MIT malte-v GitHub redis_graph Rust BSD tompro GitHub rustis Rust MIT Dahomey Technologies Crate Documentation NRedisGraph C# BSD tombatron GitHub RedisGraph.jl Julia MIT xyxel GitHub DOCUMENTATION Official Docs | Clients | Commands | 📊 Latest Performance Benchmarks Community and Support Discussions : Join our community discussions on GitHub Discussions to ask questions, share ideas, and connect with other users. Contributing : We welcome contributions! See the Developer Guide below to build FalkorDB from source and run the test suites. License : This project is licensed under the Server Side Public License v1 (SSPLv1). See the LICENSE file for details. Developer Guide This repository is the Rust implementation of FalkorDB. It builds the falkordb Redis module ( libfalkordb.{so,dylib} ) from Rust, using GraphBLAS sparse matrices for graph storage and traversal. Quick Start with Dev Container (Recommended) The easiest way to get started is using the development container, which includes all dependencies pre-installed: Install Docker and VS Code Install the Dev Containers extension Open this project in VS Code Click "Reopen in Container" when prompted (or press F1 and select "Dev Containers: Reopen in Container") Wait for the container to build (first time takes ~10-15 minutes) Start developing! All dependencies are ready to use. See .devcontainer/README.md for more details. Manual Setup If you prefer to set up the environment manually: Build cargo build Dependencies: GraphBLAS, LAGraph, and RediSearch must be built and installed before building this project. Toolchain prerequisites Host Compiler OpenMP runtime macOS brew install llvm (provides clang with OpenMP support) brew install libomp Linux clang-22 (e.g. from apt.llvm.org ) apt install libomp-22-dev Local builds use whatever OpenMP package is on the system — build/libomp.sh is not required for local development. It is only invoked by the Docker toolchain image ( build/Dockerfile ) to produce /opt/libomp/lib/libomp.a , which lets the published libfalkordb.{so,dylib} embed libomp statically and have no libomp.so.5 / libgomp.so.1 / libomp.dylib runtime dependency. A locally-built artifact will dynamically link the system libomp instead — fine for dev, but CI/Docker is the source of truth for the self-contained image. If you do want a self-contained local artifact (e.g. to mirror the Docker build), run build/libomp.sh with a writable PREFIX and point graph/build.rs at it via LIBOMP_PREFIX : CC= $( brew --prefix llvm ) /bin/clang PREFIX= $HOME /libomp ./build/libomp.sh LIBOMP_PREFIX= $HOME /libomp cargo build The script auto-detects the libomp source release from ${CC:-clang} --version , so it stays ABI-matched to your compiler with no manual version arg. In Docker the same auto-detection runs against clang-${CLANG_MAJOR} , eliminating the prior drift risk between the apt-installed clang and a hand-pinned LLVMORG_VERSION . Building GraphBLAS + LAGraph GraphBLAS and LAGraph are built by a single script: GraphBLAS is installed system-wide, LAGraph is emitted under ./lagraph_lib . On macOS, point the script at homebrew clang first: export CC= $( brew --prefix llvm ) /bin/clang export CXX= $( brew --prefix llvm ) /bin/clang++ ./graphblas.sh On Linux: CC=clang-22 CXX=clang++-22 ./graphblas.sh Building RediSearch RediSearch lives at deps/RediSearch as a git submodule (same layout as the C engine on master ). git owns that checkout — redisearch.sh only builds what is there, so populate it first: git submodule update --init --recursive ./redisearch.sh ( git clone --recurse-submodules does the first step for you. The script exits with instructions if the submodule is empty.) To work on RediSearch itself, edit deps/RediSearch in place and re-run ./redisearch.sh — it never fetches or resets, so local work is never clobbered.