Diffbot
Auto-built knowledge graph from web crawl. 1T+ facts, 10B+ entities. GraphRAG-fine-tuned model based on open-source Llama 3.3.
At a glance
- Type
- Auto-extracted KG from web crawl
- Tier
- T1
- Section
- Knowledge-graph platforms
- Created
- 2008
- Latest release
- not applicable — not OSS
- License
- not applicable — not OSS
- GitHub
- not applicable — no GitHub repo
- Pricing
- SaaS
- Funding
- $12.5M total (Seed + Series A 2016)
Taxonomy
- storage
- graph
- retrieval
- graph-traversal
- persistence
- long-term
- update
- extraction
- unit
- fact
- governance
- opaque
- conflict
- llm-arbitrate
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
Pre-built knowledge graph of the web (KGoW) — you don't build the graph, you query someone else's; saves enormous extraction cost.
Cons
Quality is bounded by Diffbot's extraction; no schema control; not appropriate for proprietary internal data.
Claims & capabilities
10B+ entities and 1T+ structured facts crawled from public web; 81% accuracy on FreshQA (Google factuality benchmark) — beats ChatGPT and Gemini; 70.36% MMLU-Pro; customers include Cisco, DuckDuckGo, Snapchat, Adobe, AOL, eBay, Microsoft
Technical surface
- API surface
- REST + GraphQL (DQL Knowledge Graph; Enhance, Extract APIs)
- Backend storage
- custom
- Deployment
- Managed-only
- Embedding model
- locked
- Multi-tenancy
- hard-isolation
- MCP
- no first-party MCP adapter published as of 2026-05; community connectors may exist.
- A2A
- no Google A2A (Agent2Agent) integration documented as of 2026-05.
- OpenTelemetry
- no first-party OpenTelemetry exporter documented; standard logs/metrics typically available.
Compare Diffbot with…
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.
- AllegroGraph (Franz) T1
RDF triple/quad store with RDFS++ / OWL reasoning. v8.4 (May 2025) added an NLQ interface.
- Apache AGE T2
openCypher over Postgres. Pairs with pgvector for graph + vector hybrid retrieval. Azure Database for PostgreSQL ships AGE with ai_extension LLM functions for entity extraction directly in SQL.
- ArangoDB T1
HybridGraphRAG combines vector search, graph traversal, full-text in one AQL query. ArangoGraphML for ML pipelines; LangChain integration.
- Dgraph T2
Vector indexing on any node. Google Gen AI Toolbox integration; LangChain agent orchestration.
Related systems
References (1)
- Meta Llama 4 family depends on at runtime — AG-fine-tuned model based on open-source Llama 3.3.