The Graph Database for AI Builders

FalkorDB is an enterprise ready graph database built for AI agents, GraphRAG and multi-tenant workloads.

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What are you building?

AI applications

I’m building with LLMs and agents.

AI applications

I’m building with LLMs and agents.

Graph workloads

I’m querying connected data.

Graph workloads

I’m querying connected data.

For AI teams that need answers grounded in how things connect, and memory that lasts past one session.

GraphRAG

Ground LLM answers in connected context, not just similar text.

Question

"Which open claims are covered by the policy we changed last week?" # graph query generated MATCH (p:Policy)-[:COVERS]->(c:Claim)-[:FILED_BY]->(u:Customer) WHERE c.status = 'open' AND p.updated_at >= date() - duration('P7D') RETURN c.id, u.name

Answer grounded in 3 connected facts, each with its source

GraphRAG

Ground LLM answers in connected context, not just similar text.

Agentic AI flows

Give agents a shared, queryable model of the world they act in.

Agent tool call 3 ms

MATCH (t:Ticket {id: 4821})-[:ABOUT]->(s:Service) -[:DEPENDS_ON]->(d:Service) RETURN d

2 upstream services returned to the agent as its next step

Agentic AI flows

Give agents a shared, queryable model of the world they act in.

Agentic memory

Long-term memory that remembers relationships, not only facts.

Cypher 2 ms

MATCH (u:User {id: 'u_482'})-[:IN]->(s:Session) -[:MENTIONED]->(t:Topic) WHERE s.id <> $session_id RETURN DISTINCT t

2 topics recalled from 2 earlier sessions

Agentic memory

Long-term memory that remembers relationships, not only facts.

Question

Answer grounded in 3 connected facts, each with its source

Agent tool call 3 ms

MATCH (t:Ticket {id: 4821})-[:ABOUT]->(s:Service) -[:DEPENDS_ON]->(d:Service) RETURN d

2 upstream services returned to the agent as its next step

Cypher 2 ms

MATCH (u:User {id: 'u_482'})-[:IN]->(s:Session) -[:MENTIONED]->(t:Topic) WHERE s.id <> $session_id RETURN DISTINCT t

2 topics recalled from 2 earlier sessions

AI frameworks integrate out of the box, from LangChain and LlamaIndex to Graphiti and Mem0

GraphRAG-SDK on GitHub → Read the docs →

7 AI frameworks integrate out of the box, from LangChain and LlamaIndex to Graphiti and Mem0

GraphRAG-SDK on GitHub → Read the docs →

For fraud, security and supply-chain teams that need answers across many hops, in real time.

Fraud & risk detection

Follow the money across accounts, devices and identities in real time.

Cypher 3 ms

MATCH (a:Account {flagged: true})-[:USES]->(:Device) <-[:USES]-(b:Account) WHERE a <> b AND NOT b.flagged RETURN DISTINCT b

2 accounts share a device with a flagged account

Fraud & risk detection

Follow the money across accounts, devices and identities in real time.

Cybersecurity

Map who can reach what: identities, permissions, assets and the paths between them.

Cypher 4 ms

MATCH p = (u:User {role: 'contractor'}) -[:MEMBER_OF|CAN_ACCESS*1..4]->(a:Asset {tier: 'critical'}) RETURN p

1 path from a contractor account to a critical asset

Cybersecurity

Map who can reach what: identities, permissions, assets and the paths between them.

Supply chain

Trace dependencies upstream and downstream before a disruption becomes an outage.

Cypher 4 ms

MATCH (p:Port {closed: true})<-[:SHIPS_VIA]-(:Carrier) <-[:USES]-(s:Supplier) RETURN DISTINCT s

3 suppliers ship through the closed port

Supply chain

Trace dependencies upstream and downstream before a disruption becomes an outage.

Cypher 3 ms

MATCH (a:Account {flagged: true})-[:USES]->(:Device) <-[:USES]-(b:Account) WHERE a <> b AND NOT b.flagged RETURN DISTINCT b

2 accounts share a device with a flagged account

Cypher 4 ms

MATCH p = (u:User {role: 'contractor'}) -[:MEMBER_OF|CAN_ACCESS*1..4]->(a:Asset {tier: 'critical'}) RETURN p

1 path from a contractor account to a critical asset

Cypher 4 ms

MATCH (p:Port {closed: true})<-[:SHIPS_VIA]-(:Carrier) <-[:USES]-(s:Supplier) RETURN DISTINCT s

3 suppliers ship through the closed port

496×

faster P99 latency than Neo4j, on an open benchmark

See the live benchmark → Read the docs →

496× faster P99 latency than Neo4j, on an open benchmark

See the live benchmark → Read the docs →

Connect your data.
Unlock what’s possible.

Your data

Tables

Documents

Code

Events

One graph FalkorDB

Built on it

QueryWeaver Text2SQL

GraphRAG Retrieval

Multi-tenant graphs

Thousands of isolated graphs within a single deployment. Each customer, agent, or session runs in its own graph with no impact on performance.

Easy migration

Native Cypher support means migrating to FalkorDB takes days, not months.

Deploy anywhere

Fully managed on FalkorDB Cloud, or self-hosted with an enterprise console.

Company Brain

Build a knowledge graph from company data and ground LLM answers in it.

ROI that shows up fast

Lower latency and a smaller footprint add up to real savings on graph TCO.

Performance

An engine built for enterprise-grade workloads that doesn’t slow down as graphs grow.

Multi-tenant graphs

Thousands of isolated graphs within a single deployment. Each customer, agent, or session runs in its own graph with no impact on performance.

Easy migration

Native Cypher support means migrating to FalkorDB takes days, not months.

Deploy anywhere

Fully managed on FalkorDB Cloud, or self-hosted with an enterprise console.

Company Brain

Build a knowledge graph from company data and ground LLM answers in it.

ROI that shows up fast

Lower latency and a smaller footprint add up to real savings on graph TCO.

Performance

An engine built for enterprise-grade workloads that doesn’t slow down as graphs grow.

Built on the graph.
Solutions you can use today.

Query SQL data in plain language or ground LLM answers in connected context - all on one graph database.

solutions

QueryWeaver GraphRAG

QueryWeaver

Text2SQL

QueryWeaver maps your schema as a graph, so the model understands how tables relate before it writes a single join. Accurate on the complex enterprise schemas where standard text-to-SQL falls apart.

Learn more →

QueryWeaver turning a plain-language question into a SQL query over a graph of the database schema

GraphRAG

Give LLMs and agents connected context: multi-hop reasoning over your knowledge graph, in milliseconds, with accuracy vector search alone can't reach.

Learn more →

GraphRAG retrieving connected context from a FalkorDB knowledge graph to ground an LLM answer

See what our graph database can do.

An open benchmark against Neo4j. The dataset, the queries and the code are public, so you can run it yourself.

Explore the live benchmark

P99 latency

496

faster

FalkorDB

83 ms

Neo4j

41,157 ms

Memory

6.3

leaner

FalkorDB

95 MB

Neo4j

600 MB

Throughput

11.4

FalkorDB

2,500 queries/sec

Neo4j

220 queries/sec

Lower latency, lower memory and higher throughput are better.

P99 latency

496 ×

faster

FalkorDB 83 ms

Neo4j 41,157 ms

Memory

6.3 ×

leaner

FalkorDB 95 MB

Neo4j 600 MB

Throughput

11.4 ×

FalkorDB 2,500 queries/sec

Neo4j 220 queries/sec

Lower latency, lower memory and higher throughput are better.

Open source

free to use, free to fork

Redis based

runs on infrastructure you already trust

OpenCypher

no new query language to learn

Rust powered

stable by design, no surprises

Run it locally. Ship it anywhere.

Running in 30 seconds. No new language to learn.

Quickstart and installation

→

Documentation

→

Client libraries Python · Node · Java · Go · Rust

→

Start building

Bring us your graph.

Talk to an engineer about what you want to build.

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The Graph Database for AI Builders

GraphRAG

GraphRAG

Agentic AI flows

Agentic AI flows

Agentic memory

Agentic memory

Fraud & risk detection

Fraud & risk detection

Cybersecurity

Cybersecurity

Supply chain

Supply chain

Connect your data.Unlock what’s possible.

Multi-tenant graphs

Easy migration

Deploy anywhere

Company Brain

ROI that shows up fast

Performance

Multi-tenant graphs

Easy migration

Deploy anywhere

Company Brain

ROI that shows up fast

Performance

Built on the graph.Solutions you can use today.

QueryWeaver

GraphRAG

See what our graph database can do.

Run it locally. Ship it anywhere.

Bring us your graph.

Connect your data.
Unlock what’s possible.

Built on the graph.
Solutions you can use today.