LLM Settings
NO LLM CONFIGURED

Your API key is stored in server RAM only. Never written to disk or any database. Cleared automatically on server restart.

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The decisive moment · Autonomous Incident Intelligence

AI-Native Site Reliability Engineering

Your AI First Responder
for Production Incidents

Kairos watches your logs 24/7, detects anomalies in real-time, and delivers a validated 3-part Root Cause Analysis in seconds — powered by a self-reflective LangGraph Investigator → Critic loop running entirely on your infrastructure.

Human SRE MTTR: ~23 minKairos MTTR: ~8 seconds
Open Live Cockpit
System Status
LangGraph
LLM
ChromaDB
Redis
Neo4j
System Architecture

How Kairos Thinks

A fully automated pipeline from raw logs to validated root cause analysis. Every component runs as an isolated Docker microservice.

MicroservicesLog Sources
POST /ingest
FastAPIAsync Backend
Anomaly Detected
ChromaDBVector RAG
Neo4jGraph Blast Radius
RedisSemantic Cache
LangGraph Loop
InvestigatorDrafts RCA + Tools
Tool BindingsDB / Pod Health
Lead CriticHallucination Check
WebSocket
SRE CockpitReal-time Next.js
Self-Reflective Critic Loop: Investigator drafts → Critic validates → if rejected, Investigator revises → repeat until approved (max 2 cycles). Eliminates hallucinations through adversarial self-critique.
Python 3.11FastAPILangGraphLangChainOllama / GroqChromaDBRedisNeo4jNext.js 16WebSocketsDocker

Live Cockpit

Live

Real-time WebSocket stream. Click “Simulate Production Incident” above to fire a demo incident and watch the AI agent investigate in real-time.

Running on demo data. Connect your production logs via the Integration Hub to analyse real incidents.

0
Logs Ingested
0
Anomalies
0
RCAs Generated
0
Cache Hits
0 min
Time Saved
Incident IntelligenceRAG · mistral:7b
Ask anything about past incidents
>_
Blast RadiusNeo4j GraphRAG
APIAPI GWAuthAuthOrderOrderUserUserPayPaymentInvInventory
Live Firehose
0
Awaiting Logs
WebSocket connected
Agent Reasoning
LangGraph
LLM Idle
Monitoring logs for anomalies
Generated RCAs
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No Active Incidents
All services nominal

Built for Enterprise Scale

This is not a wrapper. Kairos implements the same architectural patterns used by Staff SREs at top-tier engineering organizations.

LangGraph Multi-Agent

A cyclic state machine that forces adversarial self-correction. The Investigator drafts an RCA, but the Critic validates it against hallucinations and missing steps. Max 2 revision cycles.

Dual-Mode LLM Inference

Runs 100% air-gapped on-premise using Ollama (llama3.1), OR cloud-native using the Groq API (llama-3.1-8b-instant) LPU engine at 500 tok/s. Zero code changes required.

ChromaDB Vector RAG

Semantic memory for the SRE agent. Retrieves the top 3 similar historical incidents in under 10ms and injects their root causes into the LLM context to prevent repeating mistakes.

Neo4j Blast Radius

GraphRAG dependency mapping. When a service errors, the system queries Neo4j to instantly identify all downstream consumers affected, feeding blast radius context to the Investigator.

Redis Semantic Cache

Deduplication layer. Identical error patterns hitting simultaneously bypass the LLM layer entirely, serving a validated RCA from memory in ~4ms instead of ~8 seconds.

FastAPI + WebSockets

High-throughput async backend. Ingests logs, runs anomaly detection, and streams real-time state machine transitions to the Next.js frontend without long-polling.