opensre README

The project's README from github.com/Tracer-Cloud/opensre. As one markdown file: README.md.

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.

CI status Project status: public alpha Apache 2.0 License Discord Sponsored by Greptile

Tracer-Cloud%2Fopensre | Trendshift

Quickstart · Docs · FAQ · Security

🚧 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:

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:

Full deployment steps and prerequisites → DEPLOYMENT.md

How OpenSRE Works

opensre-how-it-works-github

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
🔍 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

opensre (Tracer-Cloud/opensre)

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12K ·GitHub
Opensre
What is this?An open-source framework for building AI SRE agents that resolve production incidents on your own infrastructure — and a training and evaluation environment for those agents.
So is it a hosted dashboard, or something I install and run myself?You install a local 'opensre' CLI that runs an interactive shell, a headless mode for scripts and CI, and a Python API — it works against your own cloud, with deployment recipes for EC2, Railway, ECS or Vercel.
How do I install it and sign in?Run the root installer — it auto-detects Unix shell vs PowerShell (Homebrew also available on macOS) — then create or sign in to an OpenSRE account; the interactive shell only opens for an active account.
Do I need my own LLM API key to run it?You need an OpenSRE account, and setup activates a hosted model. You can also bring your own: Anthropic, OpenAI, Ollama, Gemini, OpenRouter, NVIDIA NIM, Bedrock and more.
How stable is it right now?It's a public alpha: core workflows are usable for early exploration but not fully stable, and APIs and integrations may still change as development continues.
Can I run it on Windows, or is it macOS and Linux only?macOS and Linux are first-class — the installer needs no sudo and falls back to ~/.local/bin — and Windows is covered by a PowerShell installer. The interactive shell requires a TTY.
My production data is sensitive — what actually gets sent to the LLM?It runs on your own infrastructure and can mask sensitive identifiers such as pods, clusters and account IDs before external LLM calls, then restore them in the output.
What does OpenSRE actually do when an alert fires or I ask it a question?It fetches and correlates logs, metrics, traces and recent deploys, reasons across your connected systems in a tool-calling loop, and replies with evidence-linked conclusions. It suggests next steps, can execute remediation actions, and posts a summary to Slack, PagerDuty or Telegram.
Which tools and services can it connect to?Over 60 integrations: Grafana, Datadog, Honeycomb and Sentry for observability; Kubernetes, AWS, GCP and Azure for infrastructure; PostgreSQL, MongoDB and ClickHouse for databases; plus PagerDuty, Jira, GitHub, and the MCP and ACP protocols.
Can I run it from a script or CI instead of the interactive shell?Yes — the headless CLI runs one agent turn non-interactively from a terminal, script or CI job, with stdin prompts, JSON output and tool approvals. A Python API also lets you drive the agent in-process from your own code (source checkout required).
How do I deploy it for my team?Two AWS EC2 paths — a gateway baked into an AMI with systemd (Telegram gateway only, no Docker) — or a general hosted option on Railway, ECS or Vercel via the repo Dockerfile. Set LLMPROVIDER with its API key, plus DATABASEURI and REDISURI if you need persistence.
What are similar projects?evenfire, keep, ruflo, openobserve, LangBot, hive, agentscope, Raven
Which topics is it in?sre, alerting, grafana, gcp, tool-calling, slack, training, aws, agent-framework, reinforcement-learning, evaluation, observability
Creator
Tracer-Cloud
Size
191.2 MB
Stars
12K
Updated
CommitOct 16Releasev0.1.2026.10.16Oct 16
Age
9mo
Commits · 90 Days