Best Enterprise Knowledge Graph Software in 2026

The best enterprise knowledge graph software depends on the job. Neo4j leads for developer graph applications, Memgraph for real-time workloads, Stardog for querying relational sources in place, Graphwise for packaged semantic search and GraphRAG, eccenca for ontology-first governance, AWS Neptune for AWS-native teams, and Fluree for governed, auditable data that AI agents query.

Updated October 7, 2026: every vendor profile was re-checked against the vendor’s own documentation. Disclosure: Fluree publishes this guide, and our own profile follows the same six criteria and names its trade-offs.

If you’ve been asked to evaluate enterprise knowledge graph platforms, you’ve probably noticed that every vendor calls its product a “knowledge graph.” The label is doing a lot of heavy lifting. Neo4j is a property graph database. Memgraph is an in-memory property graph engine built for real-time workloads. Stardog federates queries across relational sources without moving data. Fluree is a verifiable knowledge graph database with an immutable ledger and triple-level access control. Graphwise pairs ontology management with a mature RDF engine. eccenca focuses on governed corporate memory. AWS Neptune is a managed service that speaks three query languages across two graph models.

These are not interchangeable products. Picking the wrong one for your use case, then discovering the mismatch twelve months into a build, is expensive. This guide is structured to help you avoid that.

What is the difference between a graph database and a knowledge graph?

A graph database stores nodes and relationships. A knowledge graph adds shared meaning: an ontology that defines what each thing is and how it relates, in a standard format other systems can read. In 2026, with AI agents consuming enterprise data directly, that difference has real production consequences.

Property graph databases (Neo4j, Memgraph, AWS Neptune in property graph mode) store nodes and relationships with key-value properties attached. Their native query languages are Cypher and Gremlin. They’re optimized for traversal performance: shortest paths, recommendations, fraud networks. They have excellent developer tooling and large communities. What they don’t do natively: formal ontologies, W3C standards interoperability, or reasoning across shared type definitions.

Semantic knowledge graphs (Stardog, Fluree, Graphwise, eccenca, and AWS Neptune in RDF mode) represent data using the RDF vs. LPG model: every fact is a subject–predicate–object triple, and those triples conform to a shared ontology that encodes domain meaning. The standard query language is SPARQL. The payoff: entities carry explicit meaning that is machine-readable and standards-compliant across systems, not just within one database.

Why the distinction matters for AI

Reasoning. An LLM querying a property graph gets data back. An LLM querying a semantic graph gets data with meaning back: the graph can infer relationships the data model implies, because the ontology defines what “supplier” means and how it relates to “contract” and “compliance requirement.” This is the foundation of accurate GraphRAG on complex, multi-domain enterprise data.
Standards interoperability. When enterprise data spans SAP, Salesforce, a data warehouse, and a document repository, the semantic layer needs to unify these sources around a shared vocabulary. RDF and JSON-LD are W3C standards designed for exactly this. Property graphs now have an ISO query standard in GQL, but no shared standard for meaning or data exchange, so every cross-system integration stays bespoke.
Governance at the data layer. AI agents making autonomous decisions need access rules that travel with the data, not just with the application. Most graph databases, property and semantic alike, grant access by role at the database, graph, or label level. Fewer evaluate policy on each individual fact at query time, and the finer the grain, the safer it is to let several agents with different clearances share one graph.

If your primary use case is developer-facing graph applications (recommendations, social graphs, routing, fraud detection), a property graph is likely the right choice. If your use case is governed enterprise intelligence with AI agents in the loop, cross-silo reasoning, or audit requirements, the semantic graph category is where to spend your evaluation time.

What types of knowledge graph software are there?

There are five kinds: RDF and semantic platforms, property graph databases, metadata and governance catalogs, managed multi-model services, and operational data platforms. Knowing which category a vendor actually belongs to is the fastest way to shorten an evaluation.

RDF and semantic platforms. Store data as W3C-standard triples conforming to formal ontologies, queried with SPARQL. Built for cross-silo meaning, reasoning, and standards interoperability. Fluree, Stardog, Graphwise, and eccenca live here.
Property graph databases. Engines optimized for traversal performance and developer experience, queried with Cypher or Gremlin. Neo4j and Memgraph are the leading examples.
Metadata and governance catalogs. Tools like Collibra and Alation use graph technology to catalog and govern data assets. They describe your data estate rather than store your operational knowledge. Valuable, but a different purchase.
Managed and multi-model services. Cloud services that bundle graph capabilities into a managed offering, often supporting both property graphs and RDF. AWS Neptune is the canonical example; Azure Cosmos DB’s Gremlin API is another.
Operational data platforms. Broader platforms that embed a knowledge graph as connective tissue for applications and AI, unifying entity resolution, semantics, and governance in one operational layer rather than a standalone database.

The seven vendors below sit in the semantic, property graph, and managed categories, and several reach into operational platforms. That spread is why a feature-by-feature comparison without this framing misleads: a metadata catalog and a semantic ledger both say “knowledge graph” on the label and solve different problems.

How should you evaluate knowledge graph software?

Score each platform on six criteria: query languages and standards, governance and access control, AI and GraphRAG integration, scalability and deployment, time travel and audit, and total cost of ownership. Weight them by your use case.

Query language and standards compliance. SPARQL vs. Cypher vs. Gremlin vs. GQL. If agents and systems need to query across multiple data sources, SPARQL plus W3C standards reduce translation overhead. If your team is developer-centric, Cypher is more approachable.
Data governance and access control. Where is policy enforced: in the application, in database roles, or on individual facts at the data layer? For regulated industries (finance, pharma, defense) and agentic AI systems, fine-grained policy enforced at the data layer is the safest architecture to scale.
AI and GraphRAG integration. Is GraphRAG native to the platform or a third-party integration you build yourself? Native integration saves months. Third-party integration gives flexibility. Know which you’re buying.
Scalability and deployment model. Cloud-managed, on-premises, hybrid, or multi-cloud. Data sovereignty and regulatory requirements often rule out SaaS-only options in regulated industries.
Time-travel and audit capabilities. Can you query the graph as it existed at a past timestamp? Can you replay what an AI agent “saw” at decision time? In regulated industries, this is an audit requirement, not a nice-to-have.
Total cost of ownership and learning curve. Proprietary query languages carry long-term lock-in risk. Ontology modeling requires domain expertise and upfront investment. Factor both into the build cost.

Enterprise knowledge graph platforms compared

How the seven platforms line up on the criteria above, as of October 2026.

Vendor	Graph model	Query languages	AI / GraphRAG	Governance	Best for
Neo4j	Property graph	Cypher (Cypher 25 supports most mandatory GQL features)	Vector search, official MCP server, Aura Agent for GraphRAG agents	RBAC with fine-grained graph privileges; ABAC role mapping; security log	Developer graph apps, recommendations, fraud detection
Memgraph	Property graph, in memory	Cypher (openCypher-compatible) over Bolt	Vector search, Atomic GraphRAG, MCP server	Enterprise RBAC with fine-grained and property-based rules	Real-time, streaming graph workloads at low latency
Stardog	RDF + virtual graphs	SPARQL, GraphQL, SQL	Query-time inference; Voicebox text-to-SPARQL; MCP server	SHACL; RBAC and named-graph security; audit logs	Federated SPARQL across relational and NoSQL sources without ETL
Fluree	RDF / JSON-LD	SPARQL 1.1, openCypher, GraphQL, JSON-LD Query	Graph, BM25, and vector retrieval in one query; MCP server; Fluree Memory	Policy enforced on every triple at query time; immutable history	Governed, auditable data that AI agents query; regulated industries
Graphwise	RDF (GraphDB engine)	SPARQL, basic GraphQL	GraphRAG workflow engine (launched February 2026); MCP server	RBAC; statement- and named-graph rules in Enterprise	Semantic search and GraphRAG with ontology tooling included
eccenca	RDF / OWL	SPARQL, JSON-LD	Companion LLM chat and MCP servers; no GraphRAG-branded product	Access Conditions per named graph; optional audit log	Ontology-first knowledge management in governance-heavy organizations
AWS Neptune	Property graph and RDF (each dataset in one model)	Gremlin, openCypher, SPARQL	Managed GraphRAG via Bedrock Knowledge Bases on Neptune Analytics; MCP server	IAM per cluster, action, and query language; encryption; VPC isolation	AWS-native teams that want a managed graph service

Vendor profiles

Each profile below answers the same six evaluation criteria, in the same order, for every vendor. Where a vendor’s public documentation doesn’t cover a criterion, we say so rather than guessing, and each profile lists the vendor documentation we checked.

Neo4j

Neo4j ranks first among graph databases in the DB-Engines popularity ranking (October 2026) and has the largest developer community in the category. Its AI tooling has grown quickly: vector indexes since 2023, an official Model Context Protocol (MCP) server since late 2025, and Aura Agent, a GraphRAG agent builder inside AuraDB, generally available since February 2026.

Query language and standards: Cypher, which reads like SQL for graphs. Neo4j sat on the ISO committee that wrote GQL, and Cypher 25 supports most mandatory GQL features. It is not RDF-native: RDF and SHACL support come from the neosemantics Labs plugin, which runs only on self-managed deployments.

Governance and access control: Role-based access control with fine-grained graph privileges and a security log, enforced in the database. Attribute-based access control, added in release 2026.03, assigns roles from user attributes and needs Enterprise Edition or AuraDB Business Critical.

AI and GraphRAG: Vector search, the first-party neo4j-graphrag package, an official MCP server, hosted MCP on Aura instances, and Aura Agent for building GraphRAG agents that deploy as MCP or REST endpoints.

Scalability and deployment: Self-managed Community Edition (GPLv3, single instance), Enterprise Edition (autonomous clustering), or Infinigraph (property sharding, sold separately and not on Aura); or fully managed AuraDB.

Time-travel and audit: Not native. Enterprise can restore a backup to a past timestamp, and change data capture streams recent changes, but there is no as-of query on the live graph.

TCO and learning curve: Cypher is widely taught and the talent pool is the largest in the category. Community Edition is free under GPLv3; clustering and advanced security sit in the commercial editions, and AuraDB bills by memory, so memory-heavy workloads drive cost.

Best for: Developer teams building recommendation engines, fraud detection, customer 360, and operational graph applications where property-graph traversal is the priority. Since acquiring GraphAware in 2026, Neo4j also sells packaged investigation and financial-crime applications.

Key trade-off: Property graph first, with no native ontology reasoning or as-of queries. RDF, time travel, and an immutable audit trail need plugins or external tooling.

Sources: Neo4j editions and features · attribute-based access control · Aura Agent launch. Verified: October 2026.

Memgraph

Memgraph is an in-memory property graph engine that positions itself as a Neo4j alternative and, in 2026, as an in-memory graph engine for AI context. It speaks Cypher over the Bolt protocol, so most Neo4j applications and drivers connect unchanged. About 70 common APOC calls map to Memgraph’s own procedures; other APOC calls and Neo4j path syntax such as shortestPath() need rework. Data lives in memory, which Memgraph says delivers sub-millisecond multi-hop traversals, with durability from snapshots and write-ahead logging. That makes it a favorite for streaming and real-time analytics.

Query language and standards: Cypher (openCypher-compatible) over Bolt. Like Neo4j, it is not RDF-native: no SPARQL and no W3C ontology standards. Memgraph Zero, announced in May 2026 and still pre-1.0, federates ISO GQL queries over sources such as Postgres, Snowflake, and Iceberg.

Governance and access control: Role-based access control with fine-grained privileges in the Enterprise edition; release 3.12 added property-based access control.

AI and GraphRAG: Vector search has been stable since Memgraph 3.0 (January 2025). Release 3.8 (February 2026) added Atomic GraphRAG, which runs a retrieval pipeline as one Cypher query, and an open-source MCP server connects Memgraph to agent stacks.

Scalability and deployment: In-memory first; the on-disk storage mode is still documented as experimental. Self-hosted or Memgraph Cloud on AWS. Memgraph’s own sizing guidance is to provision roughly twice the dataset’s memory footprint, so RAM bounds both scale and cost.

Time-travel and audit: No native time travel; point-in-time reconstruction requires external tooling. Enterprise adds a query audit log, which records queries but not past graph states.

TCO and learning curve: Minimal learning curve for teams coming from Neo4j. The Community edition is free and source-available under BSL 1.1; since January 2026 its production grant covers internal business use only. Enterprise is priced on memory, so RAM drives both infrastructure and license cost.

Best for: Real-time use cases such as streaming analytics, network monitoring, and fraud pipelines, where query latency matters more than dataset size, for teams that want Cypher on an in-memory engine.

Key trade-off: The in-memory architecture that delivers its speed also caps its economics at very large graph sizes, and its ecosystem and community are far smaller than Neo4j’s.

Sources: storage and memory usage · release notes · BSL 1.1 license text. Verified: October 2026.

Stardog

Stardog’s distinguishing capability is its virtual graph engine: it federates SPARQL queries across more than 20 relational databases and NoSQL stores such as MongoDB without ETL, so data stays in place behind a single endpoint. CSV and JSON files use the same mappings but are imported rather than queried virtually. A 2021 benchmark Stardog published with McKnight Consulting reported a 98% cost saving over consolidating all the data in one place. Stardog now markets itself as a semantic AI platform; the current release is 12.1.

Query language and standards: SPARQL and GraphQL over RDF and virtual graphs, plus SQL through Stardog’s BI Server. W3C standards-aligned, with SHACL validation.

Governance and access control: Documented in depth: role-based access control, optional named-graph security, LDAP, Kerberos, and OAuth sign-in, and audit logging. Stardog says its property masking feature isn’t yet production-ready.

AI and GraphRAG: Query-time reasoning plus Voicebox, which turns plain-language questions into SPARQL over the graph and its virtual sources and adds a document RAG pipeline. Voicebox Service 1.0 shipped in August 2026, and an MCP server is documented. Stardog does not brand this as GraphRAG.

Scalability and deployment: Self-hosted, or Stardog Cloud on AWS, also sold through the AWS and Azure marketplaces. Published scale results are vendor-run and predate version 12, so validate against your own workload.

Time-travel and audit: No time-travel queries; versioning was removed in 2019. Audit logs and point-in-time restore are documented.

TCO and learning curve: Commercial licensing. The virtual-graph approach can eliminate significant ETL cost, but it needs a SPARQL-capable team to exploit fully.

Best for: Enterprises with many relational and NoSQL systems that need federated querying without moving data, and a SPARQL-capable team to run it.

Key trade-off: Virtual graphs have documented limits, such as possible duplicate results and no search over virtual sources, and there is no queryable history. Run a proof-of-concept against your actual sources before committing.

Sources: supported virtual graph sources · security model · Voicebox release notes. Verified: October 2026.

Fluree

FlureeDB, Fluree’s verifiable knowledge graph database, makes two choices that are unusual in this category: it stores W3C RDF and JSON-LD natively, and it keeps an append-only, immutable ledger. Nothing is overwritten. Every transaction creates a new commit, so you can query the graph as it stood at any point in time through native time-travel queries rather than log replay. FlureeDB is the engine under Fluree AI, Fluree’s context layer for enterprise AI, which builds an enterprise knowledge graph with three capabilities: creating golden records from tables, turning documents into graph facts, and managing taxonomies and ontologies.

Query language and standards: Four query surfaces over one store: SPARQL 1.1, openCypher 9 for property-graph queries and writes, a GraphQL endpoint on every ledger with the schema derived from the data, and JSON-LD Query, Fluree’s native format. Fluree runs the official W3C SPARQL test suite, and the FlureeDB README reports zero compliance failures.

Governance and access control: Policy is enforced on every individual fact (triple) inside the database, during both queries and transactions. The policies are themselves data in the ledger, so they are versioned and can be queried as of any past point, like everything else. In the words of Fluree’s policy documentation, the database never returns facts the requester isn’t allowed to see. That matters most in regulated industries and wherever several AI agents with different clearances share one graph.

AI and GraphRAG: GraphRAG retrieval is native: graph traversal, BM25 full-text and HNSW vector search compose in one governed query against the same engine. Fluree Memory gives agents persistent, searchable memory, Agent JSON returns token-efficient results, and an MCP server connects agent stacks. Policy applies to agent queries exactly as it applies to people.

Scalability and deployment: Self-hosted as a single Rust binary, or hosted on Fluree AI. In Fluree’s published benchmark results, FlureeDB v4.2.0 posted a 17.5 ms geometric mean on the SPARQLoscope DBLP benchmark (September 7, 2026), 11.5× faster than QLever, the next-fastest engine, and v4.2.1 answered all 850 WGPB queries over the full Wikidata dump of 21.1 billion triples (September 12, 2026). These are benchmark results, not customer deployments.

Time-travel and audit: Native and always on. The append-only ledger makes point-in-time queries a first-class capability, by transaction number, timestamp, or commit ID, including replaying exactly what an agent could see at decision time.

TCO and learning curve: FlureeDB is source-available under the Business Source License 1.1: free to self-host and build on, with one restriction (you may not offer Fluree itself as a hosted database service), and the license converts to Apache 2.0 on its change date. W3C standards reduce long-term lock-in. Like every semantic platform, ontology modeling takes upfront investment and domain expertise.

Best for: Regulated industries (pharma, finance, defense), teams building agentic AI that needs provenance and fine-grained access control at the data layer, and anyone who needs an immutable audit trail as a database capability rather than a bolt-on.

Key trade-off: A younger ecosystem than Neo4j’s, with fewer third-party tools and a smaller community. openCypher covers the common clause set but not the APOC and GDS libraries, and the data model is RDF, so moving from a property graph is a modeling shift, not just a query rewrite. That investment pays back where meaning, governance, and audit are requirements, which is where enterprise AI is taking most of the evaluations on this page.

Sources: query languages · policy enforcement · time travel · benchmark results · license and README. Verified: October 2026.

Graphwise (formerly Ontotext)

Graphwise formed in October 2024 when Semantic Web Company and Ontotext merged, combining ontology management (PoolParty, now being renamed Graph Modeling inside one Graphwise Platform) with a mature RDF engine (GraphDB). Graphwise reports that GraphDB served more than one million SPARQL queries a day for the BBC’s 2010 World Cup site. Oakley Capital took a majority stake in the company in August 2026.

Query language and standards: SPARQL against a W3C-standards RDF engine with deep semantic-web heritage; basic GraphQL ships in every edition.

Governance and access control: Graph Modeling (formerly PoolParty) handles ontology governance. GraphDB documents role-based access control and, in the Enterprise edition, fine-grained rules down to individual statements and named graphs.

AI and GraphRAG: In February 2026, Graphwise launched GraphRAG, a low-code workflow engine that combines graph (SPARQL), vector, and full-text retrieval. It reached version 1.3 in September and runs as a separate Kubernetes service beside GraphDB, which also has a built-in MCP server.

Scalability and deployment: GraphDB has long-running production deployments at high query volumes. Self-hosted, cloud, and SaaS options are available, including through the AWS and Azure marketplaces.

Time-travel and audit: An optional history plugin records each change with a timestamp and user and supports point-in-time SPARQL queries. It is off by default, and history can be trimmed.

TCO and learning curve: Enterprise licensing is per CPU core. GraphDB Free is limited to non-commercial use and small workloads. Semantic-web expertise shortens time to value considerably.

Best for: Organizations that want semantic search and GraphRAG with ontology tooling from one vendor, and teams with semantic-web expertise who value deep RDF heritage.

Key trade-off: Graphwise is still folding PoolParty into one platform, and its GraphRAG product launched in 2026. Independent GraphRAG benchmarks remain scarce, so evaluate performance against your own data before committing.

Sources: GraphRAG launch · GraphDB fine-grained access control · data history and versioning. Verified: October 2026.

eccenca

eccenca Corporate Memory takes an ontology-first approach to enterprise knowledge management. The core workflow: model your ontology in OWL and SHACL, map and link source data to it, and expose the graph via SPARQL, with an LLM chat called Companion that writes queries from plain-language questions. Named references include AstraZeneca in pharma R&D, plus Siemens, Bosch, and Nokia. The current release line is 26.2.

Query language and standards: SPARQL, OWL, SHACL, and JSON-LD, with Companion generating SPARQL from natural language.

Governance and access control: Ontology-driven schemas act as formal business rules, the Graph Insights module supports data quality assessment, and Access Conditions grant read and write rights per named graph to users and groups.

AI and GraphRAG: eccenca does not use the GraphRAG label, but Companion grounds LLM answers in the graph, and MCP servers (added in release 25.3, extended in 26.2) expose graph and workspace tools to outside agents.

Scalability and deployment: Runs on a third-party graph store, Ontotext GraphDB by default, with Virtuoso and other standard SPARQL endpoints supported, deployed with Docker or Helm on Kubernetes. eccenca’s sizing documentation cites customer graphs of up to 300 million triples; there are no public benchmarks.

Time-travel and audit: No as-of queries. An optional audit log records graph reads and writes, and a beta versioning feature records change sets for form edits but not SPARQL updates.

TCO and learning curve: Commercial licensing, plus your own GraphDB license for the default store; a free sandbox supports evaluation. The ontology-first workflow assumes in-house ontology engineering skill, which is both its strength and its adoption cost.

Best for: Organizations where ontology engineering is a core competency and governed corporate knowledge management, not application-layer graph traversal, is the primary use case.

Key trade-off: AI access through Companion and MCP is new (since late 2025), the developer community is smaller than Neo4j’s, and the fit is narrow: best evaluated if you have ontology engineers and a governance-centric mandate.

Sources: Corporate Memory 26.2 release notes · Companion · installation and supported stores. Verified: October 2026.

AWS Neptune

Neptune is Amazon’s fully managed graph database for both property graphs (Gremlin, openCypher) and RDF (SPARQL). On AWS, Neptune handles provisioning, patching, backups, and replication.

Query language and standards: Gremlin and openCypher for property graphs, SPARQL for RDF. One cluster can store both, but each dataset is queried only in its own model’s languages, and Neptune does not support RDFS or OWL inference.

Governance and access control: IAM authentication (off by default), encryption at rest and in transit, and VPC isolation. Permissions apply per cluster, action, and query language, not to individual nodes, edges, or properties.

AI and GraphRAG: Amazon Bedrock Knowledge Bases runs managed GraphRAG on Neptune Analytics, which pairs vector search with openCypher. AWS also offers an open-source Neptune MCP server and agent-memory integrations with Mem0 and Cognee.

Scalability and deployment: Neptune Serverless scales automatically. Multi-AZ high availability and read replicas are included, and cross-Region replication is available through Neptune Global Database. AWS only.

Time-travel and audit: Point-in-time recovery restores the database to a past state, and Neptune Streams keeps a change log for up to 90 days, but there is no point-in-time query against the live graph.

TCO and learning curve: No license fee. You pay for instances or serverless capacity, storage, and I/O, or choose I/O-Optimized storage to drop I/O charges at higher instance and storage rates.

Best for: AWS-native organizations that want a managed graph service without infrastructure overhead, teams that need property-graph and RDF in one service, and teams prototyping GraphRAG inside an existing AWS AI stack.

Key trade-off: No native inference, permissions stop at the cluster, and it runs only on AWS, though data exports in standard formats.

Sources: Neptune FAQs · IAM access control scope · inference support. Verified: October 2026.

Which knowledge graph software should you choose?

Match the platform to the job you need done first. Find the situation that sounds like yours.

“We need graph infrastructure for developer-facing features: recommendations, fraud detection, customer graph applications.” Neo4j. The Cypher query language, developer ecosystem, and tooling are the strongest in the category for this use case. (Evaluating a move away from Neo4j specifically? See our Neo4j alternatives comparison and Fluree vs. Neo4j.)

“We have hard real-time latency requirements: streaming analytics, network monitoring, live fraud scoring.” Memgraph. The in-memory architecture is built for exactly this, and Cypher compatibility means a Neo4j-experienced team carries over.

“Our data is spread across dozens of relational systems and we can’t consolidate it.” Stardog. Virtual graphs, which federate SPARQL across relational and NoSQL sources without ETL, are the center of its product.

“We’re building AI agent infrastructure in a regulated industry and need provenance, fine-grained access control, and immutable audit trails.” Fluree. Policy enforced on every triple at the data layer, an immutable ledger with native time travel, and W3C standards are designed for exactly this architecture.

“We want semantic search and GraphRAG working out of the box, with ontology tooling included.” Graphwise. GraphDB, the GraphRAG workflow engine, and Graph Modeling put retrieval and ontology tooling under one vendor.

“Ontology-driven governance is the mandate, and we have the semantic engineering depth to own it.” eccenca. Corporate Memory is the most ontology-first product on this list, best when governance-centric knowledge management is the primary use case.

“We’re all-in on AWS and want managed infrastructure, not another platform to operate.” AWS Neptune. Dual-model support and Bedrock-managed GraphRAG inside the ecosystem you already run.

For the wider picture of where Fluree sits against the whole data stack, including parsing pipelines, MDM suites, governance platforms, and enterprise search, see the Fluree comparison hub. And if you’re evaluating whether a governed semantic graph fits your AI agent architecture, book a demo with our team.

Frequently Asked Questions

What are the leading enterprise knowledge graph platforms in 2026?

The platforms most often evaluated are Neo4j and Memgraph (property graph databases), Fluree, Stardog, Graphwise, and eccenca (semantic RDF platforms), and AWS Neptune (a managed service for both models). They differ in graph model, query languages, governance, and AI integration; the comparison table above puts them side by side.

What should you look for in knowledge graph software?

Six criteria cover most evaluations: query languages and standards, governance and access control, AI and GraphRAG integration, scalability and deployment, time travel and audit, and total cost including the learning curve. Weight them by use case: a regulated agentic AI project weights governance and audit, while a developer application weights query performance and ecosystem.

Which knowledge graph platforms are open source?

Few are open source in the strict sense. Neo4j Community Edition is GPLv3. Memgraph Community and FlureeDB are source-available under the Business Source License 1.1, which limits some commercial uses, and FlureeDB’s license converts to Apache 2.0 on its change date. GraphDB Free is free for non-commercial use, Stardog and eccenca are commercial, and AWS Neptune is a managed service with no license fee.

What are the key benefits of an enterprise knowledge graph?

A knowledge graph unifies siloed data around shared meaning, which pays off twice: people get cross-silo answers with context, and AI systems get grounded, verifiable retrieval. Fluree’s 2024 study, GraphRAG for GenAI Accuracy, found that knowledge-graph-backed retrieval reached up to 95% accuracy on complex enterprise questions, versus roughly 20% for zero-shot retrieval against relational data.

How do knowledge graphs support AI agents and GraphRAG?

Three ways: grounding (LLMs retrieve facts with explicit semantics instead of guessing from embeddings alone), reasoning (ontologies let the system infer relationships the raw data implies), and governance (policy enforced in the graph applies to AI agents exactly as it applies to people). That is why knowledge graphs have become a standard foundation for GraphRAG and agentic architectures.

When is an enterprise knowledge graph not the right choice?

When your data is small, single-domain, and well served by one relational schema; when the use case is a single application’s traversal workload with no cross-silo integration, where a plain graph database is simpler; or when no one in the organization can own ontology modeling. A knowledge graph earns its cost when data crosses silos, meaning must be shared across systems, or AI agents need governed access to enterprise context.