CLI
Explore, script, and manage ledgers from the terminal. Single binary, no runtime.
A graph database built for data that matters.
Temporal, verifiable, standards-compliant. RDF triples with complete history, integrated search, and fine-grained access control — in a single binary.
Trusted by
Why Core Exists
Most databases store records. Fluree Core stores knowledge.
Traditional databases store records for one system at a time. Fluree Core stores meaning — relationships between entities, the history of every change, and the policies that govern who sees what.
It is the knowledge foundation your entire organization reasons over.
What Makes Core Different
Ten things most graph databases can’t do.
Every transaction signed. Nothing silently altered.
Every transaction is cryptographically signed and appended to an immutable ledger. Data can never be silently altered or deleted, and the full temporal history of every record remains queryable — forever.
Why it matters
Because you can’t defend a decision you can’t reconstruct.
- Cryptographically signed commits, chained end-to-end
- No destructive writes — update = new commit, not overwrite
- The ledger itself is the audit log
BenchmarksFull results
The fastest SPARQL engine on the benchmark.
SPARQLoscope is a neutral academic benchmark from ad-freiburg. 105 queries across DBLP, measuring read-write performance on commodity hardware. Fluree was the only engine to complete every query — and the fastest overall.
Geometric mean
0.28s
1.7× faster than Virtuoso. 138× faster than Oxigraph.
Successful queries
105/105
Zero failed queries. The only engine in the benchmark to finish them all.
Bulk import
2M+/s
Triples per second on a single node. Billions of triples on commodity hardware.
Head-to-head
SPARQLoscope DBLP · March 2026
Fluree#1
0.28s
Virtuoso
0.48s
GraphDB
5.80s
Blazegraph
13.40s
Jena
15.30s
Oxigraph
38.60s
Geometric mean query time across 105 DBLP queries. Lower is better. Commodity hardware. Full methodology and per-query timings at labs.flur.ee.
Fluree Core vs.
the other graph databases.
Most "graph databases" give you edges without meaning, or lock you into a proprietary query language. Fluree stands apart on the axes enterprise buyers actually evaluate.
Capability | Neo4j · Neptune · others Typical graph DBs | Fluree Core |
|---|---|---|
Data model | Property graph or proprietary | Open RDF / JSON-LD, W3C-native |
Immutability | Not built in | Native ledger — every tx signed |
Time travel | Backups or snapshots only | Query any state by tx, time, or commit |
Access control | Application layer or IAM | Triple-level, enforced at query time |
Built-in search | Plugin or external service | BM25 + HNSW in the engine |
Reasoning | External or limited | RDFS, OWL 2 RL, Datalog rules |
Branching | Not supported | Fork, rebase, merge — like git |
Federated queries | Not built in | Iceberg, R2RML, remote SPARQL |
Standards compliance | Cypher or proprietary model | Full SPARQL 1.1, JSON-LD, OWL, SHACL |
Deployment | Cloud-locked or on-prem only | Single binary — any cloud, embedded |
Open source | Partial or closed | Yes — labs.flur.ee |
For DevelopersUnique
Zero to graph
Install. Query. Ship.
Single binary. No JVM, no Python env, no Zookeeper. Pick a package manager and you’re querying a knowledge graph in under a minute — SPARQL or JSON-LD, same engine.
bash
# 1 — install the single binary
brew install fluree/tap/fluree
# 2 — create a ledger
fluree create movies
# 3 — insert a few triples
fluree insert '@prefix ex: <http://example.org/> .
@prefix schema: <http://schema.org/> .
ex:blade-runner a schema:Movie ;
schema:name "Blade Runner" .
ex:alien a schema:Movie ;
schema:name "Alien" .'
# 4 — query
fluree query 'PREFIX schema: <http://schema.org/>
SELECT ?title
WHERE { ?m a schema:Movie ; schema:name ?title }'HTTP server
Production API with OIDC auth, content negotiation, and OpenTelemetry tracing.
Embedded library
Embed Fluree directly inside your Rust application. No server process, no network hop.
Run it your way
One engine. Three surfaces.
Script from the terminal. Stand up a production API. Or embed Fluree as a Rust library directly inside your application — a surface nobody else in the graph space offers.
Deploy Anywhere
On-prem. Your cloud. Or ours.
Fluree fits the deployment model your security team already approved. Full data sovereignty, major-cloud native, or fully managed — same engine, same guarantees.
On-Premises / Air-Gapped
Deploy on your own infrastructure with Docker images or a single binary. Full data sovereignty for high-control environments.
Your Cloud
Run in AWS, Azure, or GCP. AWS-native storage can use S3 + DynamoDB for HA and durable production deployments.
Fluree SaaS
Fully managed single-tenant SaaS with region selection, managed operations, and failover across availability zones.
Go Deeper
Learn, evaluate, decide.
From first-principles explainers to industry analyst reports — everything an enterprise leader needs to build the case for a knowledge graph.
Webinar replay
Live walkthrough
The Future of RAG — Graph-Native AI with Fluree and MCP
The protocol + governance + graph story end-to-end — what enterprise-ready GenAI actually looks like.
Watch replay
Why it matters
Executive downloadWhy Enterprise AI Hallucinates — and the Science Behind Fixing It
The architecture choices that keep generative AI defensible in regulated environments.
Read the articleSo you think you’re ready for a knowledge graph?
A readiness check for teams considering an enterprise KG — data modeling, stakeholder alignment, pilot selection, and scale.
DownloadStrategy
GraphRAG & Knowledge Graphs — Making Your Data AI-Ready for 2026
The data foundation every enterprise AI program needs — and the path to building it.
Read the articleWebinar replay
Live demo
Unified Intelligence — Ask Anything, Across Every System
A live MCP agent conversation spanning Salesforce, SAP, spreadsheets, and SQL — at 90% less cost than traditional integration.
Watch replayArchitecture
WhitepaperThe Power Trio Reshaping BI: GraphRAG, MCP & LLMs
Why these three together produce agents that actually work in production.
Read the articleSemantic GraphRAG — A Complete Overview
The accuracy foundation for enterprise AI — end-to-end architecture and evaluation criteria.
DownloadThesis
Research reportBuilding Corporate Memory for Enterprise LLMs
Why your data architecture determines whether AI can become a product your clients will pay for.
Read the articleGraphRAG for GenAI Accuracy
Benchmarks showing enterprise retrieval accuracy from ~70% on vector RAG to 95%+ on governed GraphRAG.
DownloadIndustryKnowledge Graphs in Pharma
How life-sciences teams use knowledge graphs to connect trial, compound, and regulatory context.
Download