OpenSRE v0.1: Build Your Own AI SRE Agents
The open-source framework for AI SRE agents, and the training and evaluation environment they need to improve. Connect the 60+ tools you already run, define your own workflows, and answer production questions on your own infrastructure.
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🚧 Public Alpha: Core workflows are usable for early exploration, though not yet fully stable. The project is in active development, and APIs and integrations may evolve
Table of Contents
Why OpenSRE?
When something breaks in production, the evidence is scattered across logs, metrics, traces, runbooks, and Slack threads. OpenSRE is an open-source framework for AI SRE agents that resolve production incidents, built to run on your own infrastructure.
We do that because SWE-bench1 gave coding agents scalable training data and clear feedback. Production incident response still lacks an equivalent.
Distributed failures are slower, noisier, and harder to simulate and evaluate than local code tasks, which is why AI SRE, and AI for production debugging more broadly, remains unsolved.
OpenSRE is building that missing layer:
an open reinforcement learning environment for agentic infrastructure incident response, with end-to-end tests for realistic production failures
We do that by:
- building easy-to-deploy, customizable AI SRE agents for production operations and incident response
- running real-world end-to-end tests across cloud-backed scenarios (tests/e2e)
- keeping semantic test-catalog naming so e2e vs unit and local vs cloud boundaries stay obvious (tests/README.md)
Our mission is to build AI SRE agents on top of this, scale it to thousands of realistic infrastructure failure scenarios, and establish OpenSRE as the benchmark and training ground for AI SRE.
1 https://arxiv.org/abs/2310.06770
Install
The root installer URL auto-detects Unix shell vs PowerShell and installs the latest build from main. OpenSRE moves quickly, so main is the latest stable version for normal installs.
Before installing, check the supported platforms and architectures table for release availability and CI coverage.
macOS / Linux:
curl -fsSL https://install.opensre.com | bash
The macOS/Linux installer does not require sudo. If no writable bin directory is already on PATH, it installs to ~/.local/bin and prints the shell command to apply the PATH update.
Equivalent explicit main-channel form:
curl -fsSL https://install.opensre.com | bash -s -- --main
Homebrew:
brew tap tracer-cloud/tap
brew install tracer-cloud/tap/opensre
Windows (PowerShell):
irm https://install.opensre.com | iex
Quick Start
Contributors: start at main.py for the process entrypoint map.
Create or sign in to your OpenSRE account once. Setup activates the hosted model:
opensre setup
Webapp contributors can run opensre setup --dev to authenticate through http://localhost:3000.
Interactive shell — with no subcommand, opensre validates your account and starts a REPL (TTY required). You can exit and stay signed out, but the shell only opens for an active account. Describe incidents in plain language, watch the agent work, and use slash commands for session control (/help, /status, /cost, /sessions, /resume, /compact, /new, /exit), integrations (/integrations list, /integrations verify), and local agent fleet monitoring (/agents). Ctrl+C cancels an in-flight turn without losing session state. See interactive shell commands for the full reference.
opensre
Headless CLI — run one agent turn non-interactively from a terminal, script, or CI job:
opensre ask "why is checkout-api slow?"
See Headless CLI for stdin prompts, JSON output, and tool approvals.
From Python — drive the agent in-process from your own code (source checkout required):
from core.agent_harness import AgentSession
session = AgentSession.start()
result = session.chat("why is checkout-api slow?")
if result.answered:
print(result.primary_response_text)
See Python API for sessions, conversations, and custom output sinks.
For your team's daily loop: embed OpenSRE in the Python services and automations your teammates already use.
Start with the in-repo Python API guide, then use it every day to make incident response repeatable.
Other useful commands:
opensre integrations setup
opensre agents scan
opensre update
opensre uninstall # remove opensre and all local data
Deployment
Two primary AWS EC2 paths and a general hosted option:
- Gateway (AMI + systemd):
make build-gateway-image then make deploy-gateway — Telegram gateway only, no Docker; the gateway is installed into a server image that new servers start from.
- Hosted (Railway / ECS / Vercel): deploy with the repo
Dockerfile; set LLM_PROVIDER and the matching API key (see .env.example), plus DATABASE_URI and REDIS_URI if persistence is needed.
Full deployment steps and prerequisites → DEPLOYMENT.md
How OpenSRE Works
When you ask a question or an alert arrives, OpenSRE automatically:
- Fetches the relevant context and correlated logs, metrics, traces, and recent deploys
- Masks sensitive identifiers (optional) before external LLM calls
- Reasons across your connected systems to test hypotheses in a tool-calling loop
- Answers with an evidence-linked response in the conversation
- Suggests next steps and, optionally, executes remediation actions
- Posts a summary directly to Slack, PagerDuty, or Telegram — no context switching needed
For the current code-level agent architecture after removing the old graph and chain
framework layers, see AGENTS.md.
Capabilities & integrations
|
|
| 🔍 Evidence-driven diagnosis |
Correlated analysis across logs, metrics, traces, deploys, and config |
| 📋 Runbook-aware reasoning |
OpenSRE reads your runbooks and applies them automatically |
| 🔗 Evidence-backed root cause |
Every conclusion is linked to the data behind it |
| 🛡️ Reversible identifier masking |
Redact pods, clusters, and account IDs before external LLM calls; restore in output |
| 📊 Session cost & history |
Per-session token tracking (/cost) and resumable REPL sessions (/sessions) |
| 👥 Local agent fleet |
Monitor Claude Code, Cursor, Codex, and other coding agents on your machine |
| 🤖 Full LLM flexibility |
Bring your own model — Anthropic, OpenAI, Codex, Ollama, Gemini, OpenRouter, TrustedRouter, NVIDIA NIM, Bedrock |
OpenSRE connects to 60+ tools across LLMs, observability, cloud infrastructure, data platforms, incident management, and MCP. The full matrix (with roadmap links) lives in the product docs; a detailed catalog is also maintained in-repo as the project grows.
Integrations
OpenSRE connects to 60+ tools and services across the modern cloud stack, from LLM providers and observability platforms to infrastructure, databases, and incident management.
| Category |
Integrations |
Roadmap |
| AI / LLM Providers |
Anthropic · OpenAI · OpenAI Codex · Ollama · Google Gemini · OpenRouter · TrustedRouter · NVIDIA NIM · Bedrock |
|
| Observability |
 Grafana (Loki · Mimir · Tempo · annotations) ·  Datadog · Honeycomb · Coralogix ·  groundcover ·  CloudWatch ·  Sentry · Elasticsearch · Better Stack · Splunk · Victoria Logs · SignOz · OpenObserve · OpenSearch · Azure Monitor · New Relic |
|
| Infrastructure |
 Kubernetes ·  AWS (S3 · Lambda · EKS · EC2 · CloudTrail · Bedrock) ·  GCP ·  Azure · Yandex Cloud · ArgoCD · Helm · Jenkins |
|
| Database |
MongoDB · ClickHouse · PostgreSQL · MySQL · MariaDB · MongoDB Atlas · Azure SQL · Snowflake · Redis · RDS · Supabase |
|
| Data Platform |
Apache Airflow · Apache Kafka · Apache Spark · Prefect · RabbitMQ · Dagster |
|
| Dev Tools |
 GitHub · GitHub MCP · Bitbucket · GitLab |
|
| Incident Management |
 PagerDuty · Opsgenie · Jira · Alertmanager · incident.io · ServiceNow |
Trello · Linear |
| Communication |
 Slack · Google Docs · Discord · Telegram ·  Rocket.Chat · WhatsApp · Buzz |
Notion · Teams · Confluence |
| Agent Deployment |
 Vercel · EC2 · ECS · Railway |
|
| Protocols |
 MCP ·  ACP |
|
一个开源框架，用于构建 AI SRE 智能体，在你自己的基础设施上处理生产环境事故——同时也是一个为这些智能体提供训练和评估的环境。