Instant graph context for AI agents fraud detection compliance systems real-time decisions
Compressing latency between signal and action.
Compressing latency between signal and action.
Every hop, join, and path computation happens while your application waits. At human-facing scale, you compensate with caching layers and precomputed views. At machine scale, the traversal cost is disqualifying. krabnet materializes graph context continuously as data changes. Before you query it.
Define the questions your application needs: "Who is this entity connected to?" "What changed around this node?" krabnet precomputes and maintains those answers for you.
Stream updates to your graph as they happen. Nodes appear, edges shift, properties change. krabnet keeps every answer current automatically.
When your application needs an answer, it's already there. No traversal, no computation at read time.
Written in Rust. Persistent state with crash recovery. Designed to run unsupervised on the critical path.
For backend services and enterprise systems. Bidirectional streaming with full crash recovery and production durability.
krabnet-server
For AI agents. Connect Claude, GPT, or any MCP-compatible agent directly to live graph context as a tool.
krabnet-mcp
Install krabnet, define the context you need, and see what instant graph reads feel like.
cargo install krabnet