VeritasGraph is live · free plan; paid plans on request · last verified 25 Oct 2026 · viability monitor
VeritasGraph
Open-source GraphRAG that traces every LLM answer back to a node in an auditable knowledge graph.
VeritasGraph is worth a serious look when you need to show an auditor the chain of reasoning, not just a confident answer. The ontology-aware retrieval plus on-premise deployment line up with what regulated public-sector and healthcare work actually demands. It's a weaker fit if you want a managed service, a GUI, or a vendor on call when something breaks — this is code you own.
Verdict generatedVerified 5d ago · liveness 65/100 · cite: rightaichoice.com/tools/veritasgraph
- Enterprise AI architects building grounded, auditable GraphRAG systems
- Regulated public-sector and healthcare teams needing on-premise sovereign GenAI
- Researchers and developers working with knowledge graphs and RDF
- Document-heavy workflows: regulatory review, complaints triage, grounded BI copilots
- Buyers who want a no-code GUI for graph construction
- Beginners with no graph modelling or RAG experience
- Teams that need a managed service with a commercial support contract
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Skip VeritasGraph if you need a turnkey product with a support contract, or if your questions are single-document lookups where a plain vector store would do the same job with less setup.
Running it on-premise means you pay for the GPUs, storage and engineering time yourself — the licence is free, the infrastructure is not.
Free and open source, so the comparison is not licence price but total cost of ownership against commercial graph platforms. Neo4j's GraphRAG and Microsoft's GraphRAG come with support contracts and managed infrastructure you pay for; VeritasGraph trades that spend for in-house engineering and GPU capacity.
In short
VeritasGraph — Open-source GraphRAG that traces every LLM answer back to a node in an auditable knowledge graph. Best for Enterprise AI architects building grounded, auditable GraphRAG systems, Regulated public-sector and healthcare teams needing on-premise sovereign GenAI, Researchers and developers working with knowledge graphs and RDF. Free to start; it also has paid plans whose price we could not confirm — see the pricing table.
What is VeritasGraph?
VeritasGraph — Open-source GraphRAG that traces every LLM answer back to a node in an auditable knowledge graph. Best for Enterprise AI architects building grounded, auditable GraphRAG systems, Regulated public-sector and healthcare teams needing on-premise sovereign GenAI, Researchers and developers working with knowledge graphs and RDF. Free to start; it also has paid plans whose price we could not confirm — see the pricing table.
VeritasGraph is an open-source GraphRAG framework from Bibin Prathap, a Microsoft MVP and Abu Dhabi-based AI architect, aimed at regulated sectors that need auditable, on-premise GenAI. You model a domain as ontologies, nodes and edges, then query it through a reasoning layer that walks multi-hop relationships rather than matching a single retrieved chunk. The pipeline is graph-native: entity extraction pulls structure from unstructured documents, ontology-aware retrieval keeps the graph deciding what reaches the model, and RDF plus linked-data support covers teams already working in semantic web formats. Storage and vector search run on Neo4j, pgvector or FAISS, and the framework slots into LangChain and LlamaIndex pipelines instead of forcing a rewrite. An MCP server bridges it to enterprise tooling, so exposable tools reach the LLM with auth, audit and guardrails baked in. Deployment is the other half of the offer. VeritasGraph runs locally or in the cloud, with on-premise options for teams whose records can't leave their own hardware. Sovereign model serving runs through vLLM and Ollama with LLaMA, DeepSeek or Mistral. The honest read: this is infrastructure for people who write ontology and pipeline code, not a product you click through. For a packaged graph database with a support contract, Neo4j or Microsoft's GraphRAG are the conventional picks. Choose it when reasoning transparency and data sovereignty outrank polish.
Key features
- Multi-hop reasoning over knowledge graphs for LLM queries
- Verifiable source attribution tracing every answer to source material
- Ontology-aware retrieval that keeps the graph in charge of context
- Knowledge graph construction with nodes, edges and ontologies
- Entity extraction from unstructured documents
- RDF and linked-data support for semantic web formats
- Retrieval across both structured data and unstructured text
- Graph storage and traversal on Neo4j
- Vector storage and search via pgvector
- FAISS integration for vector search
- LangChain pipeline compatibility
- LlamaIndex pipeline compatibility
- Model Context Protocol (MCP) server integration with auth and audit guardrails
- Local, cloud or on-premise deployment
- On-premise sovereign model serving with LLaMA, DeepSeek and Mistral via vLLM and Ollama
Behind the verdict
Most GraphRAG tooling sells you a smarter retriever. VeritasGraph sells you an audit trail, and that's the difference that matters if your answers end up in a regulator's inbox. The ontology layer isn't decoration either: it constrains what the graph hands the model, which is why multi-hop questions about complaints, contracts or case files return something you can defend line by line. We'd reach for this when the question isn't just 'did the model get it right' but 'can you show me how it got there.' Public-sector document review, complaints triage, grounded BI copilots over linked records — the sort of work where a wrong answer has a paper trail attached. RDF and linked-data support also means semantic-web teams don't have to abandon their existing triple stores to get here. Where it bites is operational. A largely single-developer open-source project carries bus factor, and you're the one who patches it at 2am. If your team doesn't already write ontology code and pipeline glue, the learning curve is real — there's no drag-and-drop graph builder waiting for you. Compared with Neo4j's packaged stack or Microsoft's GraphRAG, VeritasGraph gives up polish and a support contract but keeps your data on your own GPUs and your reasoning trace in your own hands. Sovereign model serving through vLLM and Ollama with LLaMA, DeepSeek or Mistral is the part that closes the loop for UAE-style data residency requirements. The pragmatic play: prototype on the open-source core, then bring the author in for a scoped enterprise engagement if the proof-of-concept holds. Don't buy this expecting a vendor relationship from day one — you're adopting an architecture, and the architecture is sound. Just budget for the engineers who'll own it.
Models under the hood
as of 2026-10-23
Limitations
- Maintained by a single developer, so community size and long-term maintenance are real risks.
- Documentation is thin for a framework this broad — expect to read source to understand behaviour.
- There is no managed hosting, which means you own deployment, scaling, upgrade and monitoring work.
- Ontology design is a prerequisite rather than an optional step, so onboarding is slow for teams new to knowledge graphs.
- The framework presumes structured knowledge representation is the right approach to your problem; for simple single-pass document lookup it is more machinery than you need.
as of 2026-10-04
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Is VeritasGraph still active?
VeritasGraph is live · free plan; paid plans on request · last verified 25 Oct 2026 · viability monitor.
Viability score
How well maintained and how widely used is VeritasGraph? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →What do users say about VeritasGraph?
How we verified this
Every listed fact about VeritasGraph— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 25 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified VeritasGraph 11 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed pricing tiers, description, features
- — changed who it suits, pricing tiers, our verdict, integrations, summary, features, description
- — changed description, who it suits, our verdict, features, integrations, who should skip it, pricing tiers, pricing model, summary
- — changed pricing page, platforms
- — changed switching in, switching out, who it suits, who should skip it, description, use cases, pricing tiers, limitations, summary, setup time, hidden costs, our verdict, pricing fit, features
- — changed SSL certificate
Showing the 6 most recent of 11 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does VeritasGraph cost in 2026?
VeritasGraph: Free to start; it also has paid plans whose price we could not confirm — see the pricing table. Pricing last verified 25 Oct 2026.
Plans compared
Every published VeritasGraph tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Open Source | Free | — | — |
| Enterprise Engagement | Custom | quote | — |
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where VeritasGraph's pricing actually pencils out — and where peers do it cheaper.
Free and open source, so the comparison is not licence price but total cost of ownership against commercial graph platforms. Neo4j's GraphRAG and Microsoft's GraphRAG come with support contracts and managed infrastructure you pay for; VeritasGraph trades that spend for in-house engineering and GPU capacity.
Has VeritasGraph's pricing changed?
No plan price for VeritasGraph has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick VeritasGraph?
Pick VeritasGraph if you are Enterprise AI architects building grounded, auditable GraphRAG systems, Regulated public-sector and healthcare teams needing on-premise sovereign GenAI, Researchers and developers working with knowledge graphs and RDF, Document-heavy workflows: regulatory review, complaints triage, grounded BI copilots.
Look elsewhere if you are Buyers who want a no-code GUI for graph construction, Beginners with no graph modelling or RAG experience, Teams that need a managed service with a commercial support contract, Projects where structured knowledge representation adds no real value.
Skip it if Skip VeritasGraph if you need a turnkey product with a support contract, or if your questions are single-document lookups where a plain vector store would do the same job with less setup.
GeneratedReal-world workflow fit
Concrete scenarios for the personas VeritasGraph actually fits — and what changes day-one when you adopt it.
You load policy documents and an existing taxonomy into an ontology, extract entities, index them in Neo4j with a pgvector store for embeddings, then wire the reasoning layer into your LangChain pipeline behind an internal API.
Outcome: Multi-hop questions like 'which regulation applies to this process and who signed off' return answers with a trace of the nodes and documents used, which your compliance team can review.
You deploy the framework on-premise against clinical records, define an ontology around patients, conditions and treatments, and keep embeddings in a local FAISS index so nothing leaves your network.
Outcome: Clinicians get grounded answers over internal records with data residency intact and no records sent to an external model provider.
You connect a Power BI copilot to the knowledge graph so narrative questions resolve through graph traversal rather than a flat semantic index.
Outcome: Dashboard narratives cite the underlying entities and relationships, which reduces the time analysts spend re-checking numbers before they present them.
Use cases
- Build a multi-hop question answering system over an internal RDF knowledge base
- Give LLM answers verifiable attribution in regulated document review workflows
- Create ontology-aware retrieval that filters results by domain concepts rather than similarity alone
- Run a GraphRAG pipeline on sensitive records entirely on-premise
- Add knowledge graph reasoning into an existing LangChain or LlamaIndex pipeline
- Automate complaints triage and document review with traceable reasoning
- Ground Power BI copilots in an explainable knowledge graph
Setup time & first value
How long it actually takes to get something useful out of VeritasGraph — broken out by persona, not the marketing-page minute.
For an engineer already comfortable with Python, Neo4j or pgvector and LangChain: a few days to stand up a local pipeline on a small corpus. For a team without graph experience: two to four weeks, most of it spent on ontology design and entity-extraction quality rather than installation.
Switching to or from VeritasGraph
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a plain vector-store RAG stack: keep your embedding pipeline and add a graph layer alongside it for the entities and relationships your chunks were missing.
- →From Neo4j-based RAG: reuse the existing graph and point the reasoning layer at it instead of rebuilding the schema.
- →From LlamaIndex or LangChain retrieval: swap in ontology-aware retrieval while keeping your existing loaders and prompt scaffolding.
- ↗To Neo4j GraphRAG: your Cypher schema usually ports directly, but you give up on-premise reasoning transparency in exchange for commercial support.
- ↗To Microsoft GraphRAG: export extracted entities and relationships into its graph format, useful if you are standardising on Azure.
- ↗To a plain vector store: keep the documents and drop the ontology layer if multi-hop reasoning is no longer a requirement.
Tutorials & Learning
YouTube returned 6 videos for “VeritasGraph”, and we withheld 6: 6 could not be judged, because “VeritasGraph” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about VeritasGraph.
Official links
What are the best VeritasGraph alternatives?
The closest alternatives to VeritasGraph we list are GraphRAG, Dkg Engine and ESEILANE. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| GraphRAG | Free | Free | — |
| Dkg Engine | Freemium | Pricing not published | — |
| ESEILANE | Freemium | Pricing not published | — |
Alternatives to VeritasGraph
View all
GraphRAG
Open-source Microsoft Research RAG that builds a knowledge graph and community summaries from your documents, then queries them in four modes.
Dkg Engine
Self-hosted Decentralized Knowledge Graph node software that gives AI agents shared, verifiable memory across platforms and models.
Tools that pair well with VeritasGraph
Common stack mates teams adopt alongside VeritasGraph, with the specific reason each pairing earns its keep.
GraphRAG
Open-source Microsoft Research RAG that builds a knowledge graph and community summaries from your documents, then queries them in four modes.
Dkg Engine
Self-hosted Decentralized Knowledge Graph node software that gives AI agents shared, verifiable memory across platforms and models.
ESEILANE
AI-native graph database running vector similarity and OpenCypher traversal in one query for GraphRAG pipelines.
VeritasGraph vs its closest rival
VeritasGraph's closest head-to-head is with Spider Cloud — Spider Cloud and VeritasGraph solve opposite ends of the data problem: Spider Cloud excels at fetching fresh, structured web data at scale for AI agents, while VeritasGraph helps you build and query explainable knowledge graphs from existing data. Choose Spider Cloud if you need real-time web content for RAG or AI training; choose VeritasGraph if you need auditable reasoning over structured knowledge in regulated environments. They're complementary — you could use Spider Cloud to feed data into a VeritasGraph knowledge pipeline. Verdict generated
Head-to-head comparisons
Veritasgraph vs Spider Cloud
Spider Cloud and VeritasGraph solve opposite ends of the data problem: Spider Cloud excels at fetching fresh, structured web data at scale for AI agents, while VeritasGraph helps you build and query explainable knowledge graphs from existing data. Choose Spider Cloud if you need real-time web content for RAG or AI training; choose VeritasGraph if you need auditable reasoning over structured knowledge in regulated environments. They're complementary — you could use Spider Cloud to feed data into a VeritasGraph knowledge pipeline.
GeneratedVeritasgraph vs Temporal Ai
Temporal AI and VeritasGraph serve fundamentally different purposes: Temporal ensures fault-tolerant execution of AI agents and workflows, while VeritasGraph focuses on auditable reasoning over knowledge graphs. Choose Temporal if you need reliable, stateful orchestration (e.g., agents that survive crashes). Choose VeritasGraph if you need explainable, grounded LLM outputs with verifiable sources. They are complementary — you could use Temporal to orchestrate a VeritasGraph reasoning pipeline.
GeneratedVeritasgraph vs Screenplayiq
ScreenplayIQ and VeritasGraph are too different to compete directly. ScreenplayIQ is a specialized tool for screenwriters analyzing feature film scripts with box office forecasting, while VeritasGraph is an open-source framework for enterprise GraphRAG applications. Choose ScreenplayIQ if you need narrative analysis and market predictions for movie scripts; choose VeritasGraph if you're building auditable, knowledge-grounded AI systems for regulated or sovereign environments.
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