Tracer-Cloud / opensre

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. 
 Quickstart ·
 Docs ·
 FAQ ·
 Security 
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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.
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## Before you begin
Make sure you have:
- A terminal open in a code project
- A Git repository (recommended for the full workflow demonstration)
## Step 1: Install and start opensre
macOS or Linux:
 [code] 
Windows (PowerShell):
 [code] 
Then start OpenSRE:
 [code] 
Windows details: Windows.
The installer fetches the latest build from `main` without requiring sudo. If `opensre` is not found, follow the PATH instructions printed by the installer or open a new terminal.
For supported platforms and troubleshooting, see Install locally.
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## Table of Contents
- Before you begin
- Install and start opensre
- Why OpenSRE?
- Quick Start
- Deployment
- How OpenSRE Works
- Capabilities & integrations
- Contributing & development
- Security
- Telemetry
- License
- Citations
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## 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-bench 1 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
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## Quick Start
Contributors: start at `main.py` for the process entrypoint map.
Run `opensre` to sign in and get started. The first launch activates the hosted model.
 [code] 
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 req