falkordb/falkordb

Security:

• React2Shell (CVE-2025-55182) is fixed starting from version v4.14.9.

FalkorDB

Our objective is to create an outstanding Knowledge Graph specifically for Large Language Models (LLM) that boasts exceptionally low latency, ensuring swift delivery of information through our Graph Database, known as KG-RAG.

FalkorDB is the first queryable https://github.com/opencypher/openCypher/blob/master/docs/property-graph-model.adoc database to use sparse matrices to represent the adjacency matrix in graphs and linear algebra to query the graph.

Primary features:

• Adopting the https://github.com/opencypher/openCypher/blob/master/docs/property-graph-model.adoc
  • Nodes (vertices) and Relationships (edges) that may have attributes
  • Nodes can have multiple labels
  • Relationships have a relationship type
• Graphs represented as sparse adjacency matrices
• OpenCypher with proprietary extensions as a query language
  • Queries are translated into linear algebra expressions

To see FalkorDB in action, visit https://github.com/FalkorDB/FalkorDB/tree/master/demo.

Quickstart

1. Trying FalkorDB
2. Build
3. Start
4. Use from any client
5. Documentation

Give it a try

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

docker run -p 6379:6379 -p 3000:3000 -it --rm falkordb/falkordb:edge

Once loaded you can interact with FalkorDB using redis-cli.

Here we'll quickly 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.

With redis-cli

sh
$ redis-cli
127.0.0.1:6379> GRAPH.QUERY MotoGP "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'})"
1) 1) Labels added: 2
   2) Nodes created: 6
   3) Properties set: 6
   4) Relationships created: 3
   5) "Query internal execution time: 0.399000 milliseconds"

Now that our MotoGP graph is created, we can start asking questions, for example: Who's riding for team Yamaha?

sh
127.0.0.1:6379> GRAPH.QUERY MotoGP "MATCH (r:Rider)-[:rides]->(t:Team) WHERE t.name = 'Yamaha' RETURN r.name, t.name"
1) 1) "r.name"
   2) "t.name"
2) 1) 1) "Valentino Rossi"
      2) "Yamaha"
3) 1) "Query internal execution time: 0.625399 milliseconds"

How many riders represent team Ducati?

sh
127.0.0.1:6379> GRAPH.QUERY MotoGP "MATCH (r:Rider)-[:rides]->(t:Team {name:'Ducati'}) RETURN count(r)"
1) 1) "count(r)"
2) 1) 1) (integer) 1
3) 1) "Query internal execution time: 0.624435 milliseconds"

Using FalkorDB

You can call FalkorDB's commands from any Redis client.

With redis-cli

sh
$ 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.

Depending on your client of choice, the exact method for doing that may vary.

Python example

This code snippet shows how to use FalkorDB with raw Redis commands from Python via https://github.com/andymccurdy/redis-py:

Python
import redis

r = redis.StrictRedis()
reply = r.execute_command('GRAPH.QUERY', 'social', "CREATE (:person {name:'roi', age:33, gender:'male', status:'married'})")

License

Licensed under your choice of the Server Side Public License v1 (SSPLv1). See https://github.com/FalkorDB/FalkorDB/blob/master/LICENSE.txt.