Instant graph context for AI agents fraud detection compliance systems real-time decisions

When a signal arrives, the context you need is already ready.
No waiting. No slow queries. Just answers.

Your graph is too slow when it matters most

Traditional graph databases do their heavy lifting at read time. That's fine for dashboards. It's not fine when an AI agent needs context in microseconds, or when a compliance system is processing thousands of transactions per second.

krabnet keeps the answers you care about ready before you ask. Your graph changes constantly — krabnet makes sure the context derived from it is always current and always instant to read.

How it works

Tell krabnet what context you need from your graph. It keeps those answers ready and up to date as your data changes, so reads are always instant.

1

Define what you need

Specify the graph context that matters to your application — relationships, neighborhoods, paths. krabnet prepares the results.

2

Feed it your data

Stream changes to your graph in real time. As entities and relationships are added or removed, krabnet keeps every result current.

3

Read instantly

When your application needs context, the answer is already there. Microsecond reads on a graph that never stops changing.

Built for the critical path

Always-Ready Context

Define the graph context you need once. krabnet keeps it current as your data changes — always fresh, always instant to read.

Exact Incremental Updates

When your graph changes, only the affected results are updated. No stale data, no full recomputation. Mathematically exact.

Multi-Level Interpretation

Get a fast binary signal, a structural breakdown, or a full AI-powered analysis of what changed — choose the depth your use case needs.

Automatic Pattern Detection

krabnet spots emerging patterns in your live data and surfaces them before you think to ask. The graph tells you what to watch.

High-Throughput Ingestion

Ingest over 100,000 events per second with sub-microsecond latency. Built in Rust for the workloads where performance is non-negotiable.

Production-Grade Durability

Crash recovery, automatic memory management, and persistent state. Designed to run unsupervised in production environments.

Performance

Written in Rust. Designed for workloads where latency is measured in microseconds.

<1µs Event ingestion (p99)
<500µs Context retrieval (p99)
>100K Events per second
<10µs Change propagation

Two ways in

gRPC

For backend services and enterprise systems. Bidirectional streaming with full crash recovery and production durability.

krabnet-server

MCP

For AI agents. Connect Claude, GPT, or any MCP-compatible agent directly to live graph context as a tool.

krabnet-mcp

Start building with instant graph context

krabnet is open source and written in Rust. Install it, define the context you need, and see what instant graph reads feel like.

cargo install krabnet