OpenSRE — Open Source Toolkit for Building AI-Powered SRE Agents

Introduction

OpenSRE provides building blocks for creating AI agents that perform site reliability engineering tasks autonomously. Instead of paging a human at 3 AM, these agents can detect anomalies, correlate signals across your monitoring stack, determine root cause, and execute predefined remediation runbooks.

What OpenSRE Does

• Provides a framework for building AI agents that monitor and respond to production incidents
• Integrates with observability platforms like Datadog, Grafana, Prometheus, and PagerDuty
• Supports root cause analysis by correlating metrics, logs, and traces automatically
• Executes remediation actions through configurable runbook automation
• Sends contextual alerts to Slack or other channels with diagnosis summaries

Architecture Overview

OpenSRE is a Python framework with a plugin-based architecture. Sensor plugins connect to monitoring APIs to ingest signals. An analysis engine powered by LLMs evaluates incoming data against historical patterns and known failure modes. When an incident is detected, the agent constructs a diagnosis chain correlating related signals, then selects and executes remediation actions from a library of approved runbooks. Guard rails ensure destructive actions require explicit approval policies.

Self-Hosting & Configuration

• Install via pip and initialize a project with the CLI scaffolding command
• Configure integrations in YAML: add API keys for your monitoring tools
• Define remediation runbooks as Python functions with safety annotations
• Set approval policies: auto-execute safe actions, require human approval for risky ones
• Deploy as a long-running service or trigger via webhook from your alerting system

Key Features

• LLM-powered root cause analysis correlates signals across metrics, logs, and traces
• Plugin architecture supports any observability tool via a simple adapter interface
• Runbook automation executes predefined fixes with configurable safety boundaries
• Incident timeline reconstruction shows the full causal chain for postmortems
• Human-in-the-loop mode requires approval before executing destructive remediation

Comparison with Similar Tools

• PagerDuty AIOps — proprietary incident intelligence; OpenSRE is open-source and customizable
• Robusta — Kubernetes-focused alert enrichment; OpenSRE is infrastructure-agnostic
• Shoreline — closed-source remediation platform; OpenSRE lets you own the agent logic
• BigPanda — SaaS alert correlation; OpenSRE runs in your environment with your LLM
• Grafana OnCall — routing and scheduling; OpenSRE adds autonomous diagnosis and remediation

FAQ

Q: Which LLM providers does OpenSRE support? A: It works with OpenAI, Anthropic, and any OpenAI-compatible API. You can also use local models via Ollama for air-gapped environments.

Q: Can I trust an AI agent to take action in production? A: OpenSRE enforces safety policies. You define which actions are auto-approved (restart a pod, scale up replicas) versus which require human confirmation (database failover, traffic rerouting).

Q: How does it integrate with my existing alerting? A: OpenSRE consumes webhooks from PagerDuty, Grafana, Datadog, or any alerting tool. It enriches the alert with diagnosis context before notifying your team.

Q: Is this production-ready for large-scale infrastructure? A: OpenSRE is designed for production use with rate limiting, circuit breakers, and audit logging built in. Start with observation-only mode to build confidence before enabling automated remediation.

Sources

• https://github.com/Tracer-Cloud/opensre
• https://opensre.dev/docs