FalkorDB

Overview

FalkorDB is an ultra-fast graph database built on Redis, specifically optimized for AI and machine learning workloads. Originally known as RedisGraph, FalkorDB combines graph database capabilities with vector search, making it uniquely suited for AI applications requiring both relationship modeling and semantic search.

The database leverages Redis's in-memory architecture for exceptional performance, while adding graph-specific optimizations. FalkorDB is particularly powerful for RAG applications and agent memory systems that need to combine vector similarity with graph traversal.

Key Features

**Vector + Graph**: Combined vector search and graph queries

**In-Memory Performance**: Leverages Redis speed

**Cypher Support**: Standard Cypher query language

**AI-Optimized**: Designed for ML/AI workloads

**Open Source**: Free to use and extend

**Low Latency**: Sub-millisecond queries

**Compact Storage**: Efficient memory usage

**Redis Compatible**: Works with Redis ecosystem

When to Use FalkorDB

FalkorDB is ideal for:

RAG applications combining vector and graph

Agent memory systems

Real-time AI applications needing low latency

Knowledge graphs with vector embeddings

Applications already using Redis

High-performance graph requirements

Pros

Extremely fast (in-memory)

Combines vector and graph capabilities

Built for AI workloads

Open-source and free

Cypher compatible

Good for RAG applications

Low latency

Integrates with LangChain/LlamaIndex

Cons

Newer project with smaller community

Limited to in-memory capacity

Less mature than Neo4j

Smaller ecosystem

Limited documentation

Fewer enterprise features

Redis dependency

Less suitable for very large persistent graphs

Pricing

**Open Source**: Free, MIT license

**FalkorDB Cloud**: Managed offering (pricing TBA)

**Self-Hosted**: Free to deploy on Redis