Tracer-Cloud/opensre

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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:

curl -fsSL https://install.opensre.com | bash -s -- -gh

Windows (PowerShell):

& ([scriptblock]::Create((irm https://install.opensre.com/install.ps1))) -gh

Then start OpenSRE:

opensre

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-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

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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.

opensre

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.

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 bootstrap.embedded import start_embedded_session
session = start_embedded_session()
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

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Deployment

An OpenSRE Cloud managed gateway, an AWS EC2 path, and a general self-hosted option:

• OpenSRE Cloud managed gateway: an organization admin provisions it in the web app; signed-in shell users can check, start, or stop it without handling infrastructure credentials locally.
• 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.
• Self-hosted container (Railway / ECS / Vercel): deploy the repo Dockerfile, select MODE, and set LLM_PROVIDER plus its API key. Use DATABASE_URL only when the gateway needs shared Postgres-backed state, such as Slack Events API replicas.

Full deployment steps and prerequisites → DEPLOYMENT.md

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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.

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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.

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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) · Azure · Yandex Cloud · ArgoCD · Helm | | | Database | MongoDB · ClickHouse · PostgreSQL · MySQL · MariaDB · MongoDB Atlas · Azure SQL · Snowflake · Redis · RDS · Supabase | | | Data Platform | Apache Airflow · Apache Kafka · Prefect · RabbitMQ · Dagster · Temporal | | | Dev Tools | GitHub · GitHub MCP · Bitbucket · GitLab · Jenkins | | | Incident Management | PagerDuty · Opsgenie · Jira · Alertmanager · incident.io · ServiceNow · Trello (client helpers only) | Linear | | Communication | Slack · Google Docs · Discord · Telegram · Rocket.Chat · WhatsApp · Buzz | Notion · Teams · Confluence | | Agent Deployment | Vercel · EC2 · ECS · Railway | | | Protocols | MCP | |

OpenSRE is community-built. Looking for a safe first contribution? Browse good first issue tickets or see the Good First Issues guide. See CONTRIBUTING.md for the full workflow.

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

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

Thanks goes to these amazing people:

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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.

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Telemetry

First-party 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

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License

Apache 2.0 — see LICENSE.

Citations

¹ https://arxiv.org/abs/2310.06770