opensre

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?
• Install
• Quick Start
• Deployment
• How OpenSRE Works
• Capabilities & integrations
• Contributing & development
• Security
• Telemetry
• License
• Citations

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.

Prebuilt Linux binaries require glibc 2.35+ (Ubuntu 22.04+ or a comparable distribution) and do not run on Alpine Linux. On an older or musl-based Linux system, install from source instead.

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 fleet 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:

1. Fetches the relevant context and correlated logs, metrics, traces, and recent deploys
2. Masks sensitive identifiers (optional) before external LLM calls
3. Reasons across your connected systems to test hypotheses in a tool-calling loop
4. Answers with an evidence-linked response in the conversation
5. Suggests next steps and, optionally, executes remediation actions
6. 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

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.