Best Knowledge Graph Software for Enterprise AI in 2026

The best knowledge graph software for enterprise AI depends on one question: do you want a graph built for you from your company's work data, or a graph database you model, load and maintain yourself? For the first, look at Coworker (OM2), Glean and Zep. For the second, look at Neo4j, Amazon Neptune, TigerGraph, FalkorDB and Memgraph, plus Stardog and Graphwise GraphDB if your team works with ontologies and RDF.

Most "best knowledge graph tools" lists mix these together, which is why they are hard to use. A graph database is a place to store a graph. It does not decide what goes in it. A knowledge graph platform for enterprise AI does that part too: it reads Slack, tickets, docs and CRM records, pulls out the entities and facts, keeps them current, and checks who is allowed to see what. Those are very different purchases.

I work at Coworker, which makes OM2, so I list it first and say plainly where it does not fit. I read every product page, price and doc linked below on November 9, 2026. I did not run hands-on tests of each tool. If you want the concept first, start with my explainer on what an enterprise knowledge graph is and how a context graph differs from a knowledge graph.

What is the best knowledge graph software in 2026?

Here is the short version. Prices are what each vendor publishes; "quote" means the vendor sells through sales.

What are the four kinds of knowledge graph software?

Before comparing features, sort the market. Almost every tool on this list falls into one of four groups, and the group matters more than any single feature.

The first two groups answer "how do I give AI the context of my company?" The last two answer "where do I store and query a graph I have designed?" If you are buying for enterprise AI, most of the work is in deciding which question you are actually asking. My post on the enterprise AI stack shows where each layer sits.

How did I pick these knowledge graph tools?

I started from what enterprise buyers ask AI assistants and search engines about knowledge graphs, then kept tools that meet three tests:

1. Real and current. The product is sold or maintained today, with public docs. Where a product is in maintenance mode or has been renamed, I say so.
2. Relevant to enterprise AI. It either builds a knowledge graph for AI agents or is a common place to store one for GraphRAG.
3. Checkable claims. Every price and capability below links to the vendor's own page or a dated third-party source.

I left out note-taking apps with graph views, such as personal knowledge tools. They are useful, but they are not knowledge graph software for a company.

The 11 best knowledge graph tools for enterprise AI

1. Coworker (OM2): a company knowledge graph built for AI agents

What it is. OM2 is Coworker's organizational memory: Coworker's second-generation organizational memory, which builds a permission-aware knowledge graph from your connected tools and serves that context to Coworker, to MCP clients such as Claude and ChatGPT, and to your own agents. It connects to 50+ work tools such as Slack, Google Drive, Jira, GitHub, Salesforce, HubSpot and meeting transcripts, and builds the graph without you modeling it.

How it builds the graph. The OM2 page describes five stages: it connects to 50+ tools and keeps up as work happens, turns every message, doc and meeting into small, sourced facts linked to the people, accounts and projects they mention, marks a changed fact as current and keeps the old one as history, answers with exact graph queries within each person's access, and learns faster routes to frequently asked questions.

How you use it. Inside Coworker's own apps and agents, or from Claude, ChatGPT, Cursor and other clients through Coworker MCP. For a domain beyond everyday work data, OM2 Custom Graph drafts an ontology from your data with evidence for each type, or takes yours, and a person approves every change. Coworker lists it as available hosted or on your own servers.

Best for. Companies that want their AI assistants and agents to answer questions about customers, projects and decisions without building a retrieval pipeline.

Pricing. Not published. Book a demo for a quote.

Strength. The graph is built and maintained for you, and it is portable: the same company context works in Coworker or in the AI tools your teams already use. On Coworker's own benchmark of 100 tasks (Claude through Coworker MCP versus Claude with its native connectors), with Coworker Learning on, the full set ran 75.5% cheaper and context-heavy retrieval tasks ran about 9x cheaper. Reviewers preferred Coworker's answers 84.5% of the time in blind comparisons. These are Coworker's own statistically significant benchmarks comparing Coworker MCP with Claude's native tooling.

Honest limitation. OM2 is not a general-purpose graph database. You do not write Cypher or SPARQL against your own schema, and it is not built for graph analytics such as fraud rings or network algorithms. Coworker lists 50+ connectors, far fewer than the 275+ Glean lists. There is no public price.

2. Glean: a knowledge graph behind enterprise search

What it is. Glean is an enterprise search and AI assistant platform. Its Knowledge Graph organizes indexed company information in real time around three pillars: content (documents, messages, tickets), people (identities, roles, teams) and activity (interactions and document history). Glean's site lists more than 275 connectors (Glean Enterprise Graph).

Best for. Large companies that want a mature, permission-aware search and assistant across many SaaS tools.

Pricing. Licensed per user per month through sales; Glean does not publish a price list (Glean Core Suite docs). My Glean pricing breakdown collects the reported figures.

Strength. Breadth of connectors and a strong people graph, built over years of enterprise search work. Glean also offers its context to other AI tools; see my notes on Glean MCP.

Honest limitation. You mostly consume the graph through Glean's own search, assistant and seat-based license. Teams that want the context mainly inside Claude or ChatGPT, or priced by use rather than seats, often look at Glean alternatives. I compare the two directly on Coworker vs Glean.

3. Zep: a temporal context graph for agent memory

What it is. Zep now describes itself as "the enterprise context layer for reliable agents." It builds temporal Context Graphs from text, JSON, messages and documents that record facts, their relationships, where they came from and how they change over time (Zep docs). The graph engine, Graphiti, is open source under Apache 2.0.

Best for. Engineering teams building their own agents that need memory of users, accounts and domain facts.

Pricing. Flex is $125 per month with 50,000 credits, Flex Plus $375 per month with 200,000 credits, and Enterprise is custom (Zep pricing). Credits are used per episode you send; Zep says memory, retrieval, storage and users are not metered.

Strength. Time-aware facts. When something stops being true, the graph knows when, which matters for agents that act on current state. Zep's October 2026 v4 release added entity type hierarchies and facts that link three or more entities (Zep blog).

Honest limitation. Zep is a developer service. Your team decides what to send, wires the ingestion from each source and builds the agent. I compare it with other memory tools in Mem0 vs Zep vs Letta and best MCP memory servers.

4. Neo4j: the most widely used graph database

What it is. Neo4j is a native graph database queried with Cypher, sold as the managed AuraDB service on AWS, Azure and Google Cloud, or self-managed. Neo4j positions it heavily for GraphRAG, where a knowledge graph grounds LLM answers.

Best for. Teams with engineers who want full control over a graph's schema, data and queries.

Pricing. AuraDB Free costs nothing. AuraDB Professional starts at $0.09 per GB per hour (minimum 1GB) and Business Critical at $0.20 per GB per hour (minimum 2GB). Community Edition is free under GPL3 (Neo4j pricing).

Strength. A large ecosystem around Cypher, with drivers, visualization tools, a free tier and a big community, so examples and help are easy to find.

Honest limitation. Neo4j stores the graph; it does not build it from your company's tools. Your team designs the model, writes the ingestion and keeps it current. AuraDB bills compute while an instance exists, including when paused (Neo4j billing docs).

5. Stardog: an enterprise knowledge graph platform with virtual graphs

What it is. Stardog is an enterprise knowledge graph platform built on semantic web standards. Its "virtual graphs" query data where it lives instead of copying it, and Voicebox is its natural language interface over the graph (Stardog Voicebox docs). Stardog 12.1 added multi-step reasoning and planning in Voicebox using frontier models from OpenAI and Anthropic (Stardog 12.1).

Best for. Data and governance teams that need a shared semantic model across many databases. Stardog lists NASA, Bosch and Boehringer Ingelheim among its users (Stardog platform).

Pricing. Stardog Free is available; Enterprise is by quote (Stardog pricing). Atlan's September 2026 review notes the free license is a one-year renewable license that excludes high availability, backups, LDAP and the full connector set (Atlan).

Strength. Virtualization plus reasoning: you can link data across systems without a big migration, and answers carry provenance.

Honest limitation. It asks for semantic modeling skills (RDF, OWL, SPARQL) and a project to build the model. Atlan also points out that named-graph security ships off by default, so plan the access model early.

6. Graphwise GraphDB: semantic graph database plus PoolParty

What it is. Graphwise formed in 2024 when Semantic Web Company (PoolParty) and Ontotext (GraphDB) merged (Graphwise). GraphDB is an RDF graph database with SPARQL and real-time inference; PoolParty, now called Graph Modeling in the Graphwise Platform, handles taxonomies and ontologies. Both are being folded into the unified Graphwise Platform (transition guide).

Best for. Organizations that already manage controlled vocabularies, taxonomies and ontologies.

Pricing. GraphDB Free is limited to two concurrent queries, and since version 11.0 you request the free license from Graphwise; check its license terms before commercial use. Enterprise is by quote (GraphDB licensing).

Strength. Mature standards support and inference that derives new facts from existing ones, which suits strict, explainable models.

Honest limitation. The value depends on taxonomy and ontology work up front, and product names are mid-transition, so check which edition you are buying.

7. Amazon Neptune: managed graphs on AWS

What it is. Neptune is AWS's managed graph database, with a serverless option and Neptune Analytics for in-memory analysis over large graphs. It supports Gremlin, openCypher and SPARQL, and AWS offers fully managed GraphRAG through Amazon Bedrock Knowledge Bases (Neptune features).

Best for. Teams already on AWS that want a managed graph store wired into Bedrock.

Pricing. On-demand, serverless or Database Savings Plans; Neptune Analytics is billed per memory-optimized capacity unit, where each unit is 1GB of memory for an hour (Neptune pricing).

Strength. Three query languages, a serverless option and a managed path to GraphRAG without running your own cluster.

Honest limitation. It ties you to AWS, and Neptune Analytics is not available in the GovCloud regions (Neptune Analytics limits). You still design and load the graph.

8. TigerGraph: graph analytics at large scale

What it is. TigerGraph is a graph database for operational and analytical graph workloads, now sold in the cloud as TigerGraph Savanna. It supports GSQL and OpenCypher, hybrid graph and vector search, and an open-source GraphRAG project.

Best for. Analytics teams running large graph workloads, such as fraud or supply chain analysis, who want a managed service.

Pricing. Savanna bills compute by workspace size and hours used, with storage billed separately (TigerGraph pricing). Cloud Classic on AWS had a scheduled end of life of September 30, 2026, and TigerGraph has been moving customers to Savanna (transition FAQ).

Strength. Storage and compute that scale separately, plus hybrid graph and vector search in one engine.

Honest limitation. GSQL is its own language with a learning curve, and the platform is built for analytics teams rather than for giving an assistant company context.

9. FalkorDB: low-latency graphs for GraphRAG

What it is. FalkorDB is a graph database that speaks the Redis protocol (its docs also list Bolt) and implements a subset of OpenCypher with its own extensions, plus a GraphRAG SDK that builds knowledge graphs from documents (FalkorDB GraphRAG docs).

Best for. Developers building multi-tenant GraphRAG apps where query latency matters.

Pricing. FalkorDB Cloud is free to start; the Pro tier starts at $350 per month with high availability and 24/7 support (FalkorDB Pro tier).

Strength. Speed and multi-tenancy, with a GraphRAG toolkit from the same team.

Honest limitation. It implements a subset of OpenCypher, so Neo4j-specific queries and procedures may not port without changes.

10. Memgraph: an in-memory graph database

What it is. Memgraph is an in-memory graph database compatible with Cypher, with stream connectors, vector search and an algorithm library. Its docs cover GraphRAG and an AI memory feature for coding agents, and in May 2026 it launched Memgraph Zero and MemGQL to query data as a graph where it already lives (Memgraph blog).

Best for. Real-time graph workloads, such as streaming data, fraud checks and agent memory that must answer fast.

Pricing. Community Edition is free and open source; Enterprise pricing scales with memory capacity, with no per-query charges (Memgraph pricing).

Strength. Low latency and simple capacity-based pricing.

Honest limitation. In-memory means sizing the graph to RAM, and as with any database, building and maintaining the knowledge graph is your job.

11. Microsoft GraphRAG: the open-source reference library

What it is. GraphRAG is Microsoft Research's MIT-licensed library. An LLM extracts entities and relationships from your text, groups them into communities and writes summaries, which then power local, global and DRIFT search (GraphRAG query docs).

Best for. Engineers who want to study or prototype GraphRAG on a fixed set of documents.

Pricing. Free to use; you pay for the model calls during indexing and queries.

Strength. It popularized the method and handles "global" questions about a whole corpus that vector search struggles with, as Microsoft's LazyGraphRAG post explains.

Honest limitation. The repository now says the project is "largely in maintenance mode" and will not take new features. Indexing a large, changing corpus with LLM calls is costly, and it does nothing about permissions or freshness. My LangChain vs LlamaIndex post covers frameworks teams use instead.

Other knowledge graph tools worth knowing

• data.world is a data catalog built on a knowledge graph. ServiceNow agreed to acquire it in May 2025 (TechCrunch), and it now sells as part of ServiceNow. It maps your data assets, not the work happening in your company.
• Diffbot offers a knowledge graph of the public web with over 10 billion entities (Diffbot). It has a free plan with 10,000 credits a month and a Startup plan at $299 a month (Diffbot pricing). Useful for company and people data, not for your internal knowledge.

How do you choose knowledge graph software for AI agents?

Five questions sort the options faster than a feature grid.

1. Who builds and maintains the graph? A graph database gives you an empty store. Someone has to write the ingestion from each source, map entities, resolve duplicates and keep it current. If you do not have that team, a built-for-you graph is the realistic option.

2. Does it respect existing permissions? An assistant that answers from a company graph must only show each person what they can already see in the source tools. Company work graphs such as Coworker and Glean are built around this. With a database, you build the access model yourself.

3. How fresh is it? Company facts change daily. Ask how updates flow in: continuous sync, scheduled batch jobs or manual reloads.

4. How will your AI reach it? Check whether your assistants can use the graph directly, for example through the Model Context Protocol, or whether you must build a retrieval layer first. My post on RAG vs MCP explains the difference.

5. How is it priced, and what will tokens cost? Seats, memory, credits and compute all scale differently. Also count the model cost of using the graph: a graph that hands an agent a few relevant facts costs fewer tokens than one that returns whole documents. I break this down in AI token cost.

Should you build a company knowledge graph or buy one?

Build when the graph is the product: a fraud engine, a drug discovery model or a supply chain map with a schema only your team understands. A graph database plus your own modeling is the right call there.

Buy when the graph is infrastructure for AI: you want assistants and agents to know your customers, projects and decisions, and the data lives in Slack, docs, tickets and the CRM. In that case the hard part is not storage. It is extraction, entity resolution, permissions and freshness across dozens of tools, and it never finishes. I walk through the trade-offs in build vs buy an AI context layer and the related idea of context engineering.

A common middle path: buy a company work graph for everyday context, and keep a graph database for the one domain you model yourself.

Where does Coworker fit, and where doesn't it?

Coworker fits when you want company context for AI without building a pipeline. It suits teams that want one graph shared across Coworker, Claude, ChatGPT and Cursor, rather than separate connectors in each tool. It is also built for teams that care about the token bill, since agents get distilled facts instead of whole documents.

Coworker does not fit if you need a general-purpose graph database to model your own domain with Cypher or SPARQL, run graph algorithms over transaction data, or publish linked open data. Use Neo4j, TigerGraph, Stardog or GraphDB for that. If your priority is the widest possible connector list for enterprise search, Glean lists more connectors than Coworker's 50+.

How does Coworker build a company knowledge graph?

Coworker connects to your tools, turns every message, doc and meeting into small, sourced facts linked to the people, accounts and projects they mention, and keeps the graph current as work happens, keeping old facts as history when something changes. Each answer is scoped to what the person asking can see in the source tools. You can read more about the idea in what organizational memory is, the AI memory layer and context layers.

Because the graph is reachable over MCP, the same context works in Coworker's own agents and in the AI tools your teams already use. If you want to see it on your own data, book a demo.

Frequently asked questions

What is knowledge graph software?

Knowledge graph software stores and manages information as entities (people, products, accounts) and the relationships between them, so software and AI can follow connections instead of matching keywords. Some tools are databases you model yourself, such as Neo4j. Others build the graph for you from your data, such as Coworker and Glean.

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

A graph database is the storage engine for nodes and relationships. A knowledge graph is the content: the entities, facts and meaning you put into it. You can build a knowledge graph on a graph database, but the database alone does not create one. My enterprise knowledge graph guide covers this in more depth.

Which knowledge graph tools are free?

Neo4j AuraDB Free and Community Edition, Memgraph Community Edition, GraphDB Free (two concurrent queries, license by request; check commercial-use terms), Stardog Free (a renewable one-year license), FalkorDB Cloud's free start and Microsoft GraphRAG (MIT) all cost nothing to start. Diffbot's free plan includes 10,000 credits a month. Model and hosting costs still apply where you run the pipeline.

What is the best knowledge graph software for AI agents?

If you want agents to know your company without building ingestion, use a company work graph such as Coworker (OM2) or Glean. If you are building your own agents and want to control what goes into memory, use an agent memory graph such as Zep. If you need a store for a graph you design, use Neo4j or another graph database.

Is GraphRAG better than vector RAG?

GraphRAG does better on questions that need connections across many documents or a summary of a whole dataset, while vector search does well on questions whose answer sits in one passage. Microsoft Research describes this split in its LazyGraphRAG post. Many production systems use both.

How long does it take to build a company knowledge graph?

With a graph database, plan for a project: schema design, ingestion from each source, entity resolution, permissions and ongoing maintenance, often running for months. With a built-for-you company graph, setup is mostly connecting tools and waiting for the first sync.

Does Coworker replace a graph database like Neo4j?

No. Coworker builds and serves a company knowledge graph for AI agents. It is not a general-purpose database for modeling your own domain or running graph analytics. Many teams would use both: Coworker for company context, a graph database for a specific data product.

Related reading

• What is an enterprise knowledge graph?
• What is a context graph?
• Build vs buy an AI context layer
• Best enterprise AI knowledge management tools
• Best enterprise search software
• Best MCP servers for databases
• AI agent memory