> 🚧 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
• Benchmark
• 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 and synthetic incident simulations for realistic production failures

We do that by:

• building easy-to-deploy, customizable AI SRE agents for production incident investigation and response
• running scored synthetic RCA suites that check root-cause accuracy, required evidence, and adversarial red herrings (tests/synthetic)
• running real-world end-to-end tests across cloud-backed scenarios including Kubernetes, EC2, CloudWatch, Lambda, ECS Fargate, and Flink (tests/e2e)
• keeping semantic test-catalog naming so e2e vs synthetic 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.

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 <code class="ra0-md-code">main.py</code> for the process entrypoint map.

Configure once, then pick how you want to run investigations:

opensre onboard

Interactive shell — with no subcommand, opensre starts a REPL (TTY required). Describe incidents in plain language, stream investigations, and use slash commands for session control (/help, /status, /cost, /sessions, /resume, /compact, /new, /exit), integrations (/integrations list, /integrations verify), local agent fleet monitoring (/agents), and reasoning depth (/effort for OpenAI and Codex — low through max). Ctrl+C cancels an in-flight investigation without losing session state. See interactive shell commands for the full reference.

opensre

One-shot investigation — run the agent once against an alert file:

opensre investigate -i tests/e2e/kubernetes/fixtures/datadog_k8s_alert.json

Remote runtime investigation — investigate a deployed service by name (live health, logs, and deployment status):

opensre investigate --service api-backend

Hermes log watch — tail a Hermes errors.log, classify incidents, and optionally alert on Telegram:

opensre hermes watch

From Python — drive the agent in-process from your own code (source checkout required):

from core.agent_harness import AgentHarness

harness = AgentHarness.start()
result = harness.dispatch_message("why is checkout-api slow?")
if result.answered:
    print(result.primary_response_text)

See Python API for sessions, conversations, and custom output sinks.

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 <code class="ra0-md-code">.env.example</code>), plus DATABASE_URI and REDIS_URI if persistence is needed.

Full deployment steps and prerequisites → DEPLOYMENT.md

How OpenSRE Works

When an alert fires, OpenSRE automatically:

1. Fetches the alert 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. Generates a structured investigation report with probable root cause and linked evidence
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.

Benchmark

Regenerate numbers with make benchmark; refresh this table from cached results via make benchmark-update-readme. See docs/DEVELOPMENT.md for details.

_No benchmark results yet._

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.

Category	Integrations	Roadmap
AI / LLM Providers	Anthropic · OpenAI · OpenAI Codex · Ollama · Google Gemini · OpenRouter · 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 · Hermes	New Relic
Infrastructure	Kubernetes · AWS (S3 · Lambda · EKS · EC2 · CloudTrail · Bedrock) · GCP · Azure · 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	Notion · Teams · Confluence
Agent Deployment	Vercel · EC2 · ECS · Railway
Protocols	MCP · ACP · OpenClaw

OpenSRE is community-built. Looking for a safe first contribution? Browse <code class="ra0-md-code">good first issue</code> tickets or see the Good First Issues guide. See CONTRIBUTING.md for the full workflow.

Local environment: SETUP.md (all platforms, Windows, MCP/OpenClaw).

Developing in this repo: docs/DEVELOPMENT.md (install from source, CI parity checks, dev container, benchmark, deployment detail, telemetry reference).

Thanks goes to these amazing people:

Security

OpenSRE is designed with production environments in mind: structured and auditable LLM prompts, local transcript handling by default, and no silent bulk export of raw logs. See SECURITY.md for responsible disclosure.

Telemetry

PostHog (product analytics) and Sentry (errors) are opt-out. Quick disable:

export OPENSRE_NO_TELEMETRY=1

Full matrix, DSN override, and local event logging → docs/DEVELOPMENT.md

License

Apache 2.0 — see LICENSE.

Citations

¹ https://arxiv.org/abs/2310.06770