Ontotext GraphDB / Graphwise

https://graphwise.ai/

RDF graph database with real-time inference, now a component of the Graphwise Platform. Ontotext and Semantic Web Company (PoolParty) joined forces as Graphwise in 2024; Oakley Capital took a majority stake in 2026.

Acquired. acquired by Oakley Capital Source · Alternatives and migration notes →

Acquired · Verified 2026-10-29 · fresh as of 2026-10-29 · 44 cited facts, 6 with a third-party source · 2 recorded changes · Suggest a correction

At a glance

Tier
T1
Created
2008 third-party fix
Latest release
not applicable — not OSS fix
GitHub
not applicable — no GitHub repo fix
Pricing
Enterprise only third-party fix
Freshness
2026-10-29 fix
Layer
model-infra repo fix
Licensing
commercial vendor fix
Deployment locus
hybrid vendor fix
Platform vs product
platform vendor fix

When to use

Optimised for: relationship modeling + reasoning + governance over pure vector

Anti-fit: not for purely-vector or simple-RAG use cases (graph adds setup cost)

Pros & cons

Pros

Best-in-class RDF / SPARQL engine with native reasoning; long-running European / public-sector adoption.

Cons

Property-graph workloads are not a strength; smaller user base than Neo4j.

Claims & capabilities

First triplestore to pass both LDBC SNB Interactive (1.5B edges, SF30) and Semantic Publishing benchmarks; merged with Semantic Web Company to form Graphwise (October 2024) — combined customer count >200 across PoolParty + GraphDB

Technical surface

API surface
REST + SPARQL 1.1 endpoint + RDF4J Workbench
Backend storage
custom (RDF triple store)
Deployment
Both
Embedding model
BYO
Multi-tenancy
hard-isolation
MCP
yes — GraphDB 11.4 supports custom MCP prompt templates (defined via REST) for AI agents
A2A
no Google A2A (Agent2Agent) integration documented as of 2026-05.
OpenTelemetry
first-class — Prometheus / OTel

Similar systems

Other knowledge-graph platforms in the catalog, ranked by inbound references.

  • Neo4j T1

    Property graph DB with Cypher; native vector search; Aura Agent + MCP server for graph-as-memory. $100M GenAI investment in 2025.

  • Amazon Neptune Analytics T1

    Vector index on graph nodes queryable via openCypher. Mem0 integration GA 2025; Cognee integration for agentic RAG. Combines semantic recall with multi-hop traversal in one managed service.

  • FalkorDB T2

    Native GraphRAG SDK. Graph-memory backend for Mem0; MCP server via Graphiti integration; context graphs for long-term agent memory.

  • ArangoDB T1

    HybridGraphRAG combines vector search, graph traversal, full-text in one AQL query. ArangoGraphML for ML pipelines; LangChain integration. The company now brands itself Arango (Arango Contextual Data Platform unifying graph, vector, document and search), with ArangoDB as its database.

  • Kuzu T2

    Embedded property-graph database (Cypher) with a built-in vector index and full-text search, widely used as an embedded store for agent memory and GraphRAG. The kuzudb/kuzu repository was archived on 2025-10-10 after Apple acquired the company; 0.11.3 is the final release and earlier releases remain usable.

  • Memgraph T2

    In-memory GraphRAG engine. Memgraph 3.0 adds native vector search alongside graph traversal. LangChain toolkit.

Sources (43 cited facts, 6 third-party)

Each fact links to the page it came from; the badge says who published it (how to read sources).

What changed

Changes to this record's lifecycle, licence, release, pricing and hosting facts, from the catalog's git history. Subscribe (Atom)

  1. 2026-10-29
    • License “not applicable — not OSS” → “proprietary; GraphDB Free (free-to-use non-commercial option) and Enterprise Edition; every edition needs a licence file (Free licence emailed on request)” source
    #305
  2. 2026-10-27
    • Funding / ownership “~€41M total (€30M valuation + €11M invested by Integral/PortfoLion/Carpathian/EBRD in 2022-2023)” → “Majority stake acquired by Oakley Capital (2026); terms undisclosed” source
    • MCP support first recorded as “yes — GraphDB 11.4 supports custom MCP prompt templates (defined via REST) for AI agents” source
    #274