Connect your lakehouse to a petabyte-scale graph. Zero ETL. AI-powered query discovery. Subsecond latency from day one.
Your vector store answers "what's similar." Your SQL warehouse answers "what happened." Graph answers the question your AI agents need most: "how is everything connected?"
LLMs hallucinate relationships. RAG pipelines miss multi-hop context. Agents can't traverse entity connections across your data lake.
Ground every LLM call in real relationships. Multi-hop queries in milliseconds. Your AI knows not just facts, but how facts connect.
CrabGraph reads directly from your existing lakehouse catalogs. No migration, no duplication, no drift. Your data stays where it is โ CrabGraph brings the graph to it.
Native format support
Direct table access
Upsert-aware reads
Databricks native
Point CrabGraph at your Iceberg catalog. We discover your schema and suggest graph projections automatically using AI.
Built on a distributed graph engine designed for the scale of modern lakehouses โ not bolted on after the fact.
Traditional graph databases charge per query, per consumer. CrabGraph's shared graph layer lets every AI consumer โ agents, pipelines, analysts โ distribute cost across one materialized graph. Query more, pay less per insight.
No demo required. Our AI-powered schema discovery and query visualizer get you to first insight in seconds โ not sprints.
Point CrabGraph at your Iceberg, Delta, or Glue catalog. We connect in seconds โ no credentials stored, no data copied.
Our AI scans your tables and suggests node types, edge relationships, and graph projections. Review and confirm with one click.
Write Cypher or use natural language. Our visual query explorer shows your graph, estimated cost, and live results side by side.
Define node and edge projections using standard SQL. No new language to learn.
Materialize hot subgraphs for subsecond read performance. Configure per-graph policies.
Add node types and edge labels without downtime. Backward compatible by default.
Attach embeddings to any node. Call Python UDFs inside traversal queries.
Incremental graph refresh triggered by upstream table commits. Always fresh.
See projected query cost before you run it. Budget guardrails built into the UI.
Ground your LLM responses in actual entity relationships. Multi-hop traversals retrieve richer context than vector search alone โ reducing hallucinations and improving answer quality.
Detect fraud rings, shared device fingerprints, and anomalous transaction chains with subsecond graph traversals. Connect behavioral signals across your entire customer graph.
Power collaborative filtering and item graphs with traversal queries. CrabGraph materializes your product, user, and interaction graph so recommendations stay fresh at scale.
Build a connected knowledge layer across your org โ people, projects, documents, systems. Give your agents the relational context they need to reason, plan, and act with confidence.
"We replaced a custom graph pipeline with CrabGraph in a weekend. Our GraphRAG accuracy jumped 40% because we finally had real relationship traversal โ not just vector similarity."
"The zero-ETL story is real. We pointed it at our Iceberg tables on day one and had a working fraud detection graph by end of sprint. No migrations, no pain."
"Subsecond latency at petabyte scale is not marketing. We ran our knowledge graph across 800M nodes and p99 stayed under 80ms. Nothing else came close."
From catalog connection to your first graph query โ in under two minutes.
Connect your first catalog in seconds. No demo required, no credit card needed to start.