Building an Autonomous AI Incident Knowledge Engine with Cognee and Uptime Kuma

In modern infrastructure management, keeping services online is only half the battle. When downtime, degraded performance, or cascading outages occur, engineering teams scramble across alert histories, heartbeat charts, and incident post-mortems to understand what failed, when, and why.

While monitoring tools like Uptime Kuma (with over 65,000+ GitHub stars) excel at real-time alerting, health-checking, and status-page communication, their historical telemetry often remains locked in transient time-series logs and relational tables.

Enter Cognee: an open-source, AI-native memory engine that transforms unstructured text, logs, and relational data into interconnected knowledge graphs and vector databases.

In this guide, we introduce the Uptime Kuma Cognee Community Connector, enabling autonomous LLMs, triage agents, and site reliability engineers (SREs) to query, reason over, and correlate infrastructure incidents using natural language.

Integrating Uptime Kuma telemetry into Cognee's memory graph unlocks several high-leverage capabilities:

The Uptime Kuma connector is designed to be resilient, modular, and privacy-first:

Install Cognee and the community Uptime Kuma connector:

pip install cognee cognee-community-connector-uptime-kuma Enter fullscreen mode Exit fullscreen mode Or using uv:

uv add cognee cognee-community-connector-uptime-kuma Enter fullscreen mode Exit fullscreen mode 2. Basic Usage Run the connector to ingest your monitors and status pages into Cognee memory:

import asyncio import os import cognee from cognee_community_connector_uptime_kuma import ( UptimeKumaConfig, uptime_kuma_connector, ) async def main (): # Configure your Uptime Kuma instance config = UptimeKumaConfig ( api_url = os. getenv ( " UPTIME_KUMA_URL ", " https://uptime.example.com " ), api_key = os. getenv ( " UPTIME_KUMA_API_KEY ", " your-api-key " ), slug = " main-status ", # Optional status page slug heartbeat_limit = 50, ) print ( " Fetching Uptime Kuma telemetry... " ) documents = await uptime_kuma_connector ( config ) print ( f " Generated { len ( documents ) } incident & monitor reports. " ) # Ingest into Cognee Memory print ( " Ingesting into Cognee...

" ) await cognee. add ( documents, dataset_name = " infrastructure_telemetry " ) await cognee. cognify ( dataset_name = " infrastructure_telemetry " ) # Natural Language Incident Reasoning query = " Which services experienced downtime or high latency in the last 24 hours? " print ( f " \n Querying Cognee: ' { query } '" ) results = await cognee. search ( query, dataset_name = " infrastructure_telemetry " ) for result in results: print ( f " \n Memory Result: { result } " ) if __name__ == " __main__ ": asyncio. run ( main ()) Enter fullscreen mode Exit fullscreen mode What Does Ingested Memory Look Like? The connector parses complex JSON telemetry into structured markdown documents tailored for vector similarity and graph extraction:

# Uptime Kuma Status Page: Production Infrastructure (main-status) **Description**: Live public status for core APIs, auth services, and databases. ## Services & Monitors - **Primary API Gateway** (HTTP Monitor) - Target: `https://api.internal.network/health` - Current Status: UP - Interval: 60s - Average Latency: 42ms ## Recent Incident Log - **2026-10-23 14:15 UTC**: Elevated Latency on Auth Cluster - Status: Resolved - Impact: Response times peaked at 1,450ms before auto-scaling engaged. Enter fullscreen mode Exit fullscreen mode When Cognee processes this markdown through cognify(), it constructs knowledge graph nodes representing:

This enables downstream AI agents to uncover hidden failure patterns across distinct services and cloud providers.

The Uptime Kuma connector is an open-source contribution to the Cognee Community ecosystem.

Try it out today to bring intelligent incident memory to your observability stack!