Open-Source Observability · Tracer-Cloud

opensre

OpenSRE is an open-source Python framework for building AI agents that automatically investigate and respond to production incidents. It connects to 60+ observability and incident management tools, runs synthetic and real-world tests to validate root-cause analysis, and can be deployed on your own infrastructure.

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Source: GitHub — github.com/Tracer-Cloud/opensre

GitHub stars

1.1k

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	Tracer-Cloud/opensre
Owner	Tracer-Cloud
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	8k
Forks	1.1k
Open issues	167
Latest release	v0.1.2026.7.7 (2026-07-07)
Last updated	2026-07-08
Source	https://github.com/Tracer-Cloud/opensre

What opensre is

Framework for agentic incident response with tool-calling loops across logs, metrics, traces, and runbooks. Includes reinforcement learning environment (synthetic RCA suites, e2e tests across K8s/EC2/Lambda/ECS), semantic test catalogs, and optional remediation execution. Primary language: Python; Apache 2.0 licensed.

Quickstart

Get the opensre source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/Tracer-Cloud/opensre.git2cd opensre3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Self-hosted AI incident investigation

Organizations with strict data residency or sensitive infrastructure seeking to keep incident context (logs, metrics, traces) on-premises while using agentic root-cause analysis.

Training and benchmarking AI for SRE

Teams building or evaluating coding/reasoning agents that need labeled incident datasets, synthetic failure scenarios, and scored evaluation (RCA accuracy, evidence requirements, adversarial red herrings).

Incident workflow automation

SRE teams looking to automate triage, evidence gathering, and hypothesis testing across fragmented observability stacks (Datadog, Grafana, Slack, PagerDuty) with remediation suggestions or auto-execution.

Implementation considerations

Requires LLM API integration (OpenAI, Codex, or other). External LLM calls include optional PII masking but assess data residency and cost implications for high-volume incident investigation.
Onboarding via `opensre onboard` and tool integrations (`/integrations setup`) is command-line driven; no GUI for non-technical users. DevOps/SRE teams should lead configuration.
Deployment options include Docker on EC2, custom AMI with systemd, or hosted (Railway/ECS/Vercel) with `DATABASE_URI` and `REDIS_URI` requirements. Self-hosted cluster deployment not mentioned.
Synthetic and e2e test suites require cloud-backed infrastructure (K8s, EC2, Lambda, ECS, CloudWatch) to validate incident scenarios; local testing is partial and may not cover production failure modes.
Interactive REPL requires TTY; CI/CD automation via one-shot CLI (`opensre investigate`) is available but session management and async execution for long-running incidents need clarification.

When to avoid it — and what to weigh

Need production-grade stability today — Project is in public alpha (v0.1); README states 'Core workflows are usable for early exploration, though not yet fully stable' and 'APIs and integrations may evolve.' 167 open issues as of latest push.
Require vendor SLA and commercial support — No commercial support model or SLA documented. Community support via Discord; enterprise/hosted options unknown. Use-at-your-own-risk for mission-critical incident response.
Limited observability tool footprint — Tight integration dependencies on specific platforms (Datadog, Grafana, Slack, PagerDuty, CloudWatch, etc.). If your stack is fragmented or proprietary, bridging effort may be significant.
No multi-tenancy or compliance framework — Designed for single-org self-hosted deployment. Multi-tenancy, RBAC, audit logging, and compliance certifications (SOC2, FedRAMP, etc.) not documented; assess against regulatory requirements.

License & commercial use

Apache License 2.0 (Apache-2.0). Permissive OSI-approved license allowing commercial use, modification, and distribution with attribution and liability disclaimer.

Apache 2.0 is permissive and allows commercial use without explicit vendor permission. However, no commercial support, SLA, or indemnification from Tracer-Cloud is documented. Assess vendor relationship and support model against your risk tolerance and SLA requirements before production deployment.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	High
DEV.co fit	Good
Assessment confidence	Medium

Security considerations

Project supports optional PII masking before external LLM calls, reducing exposure of sensitive identifiers. No exploitation details provided; assess threat model: external LLM calls, database/Redis persistence, Slack/Telegram webhook outbound communication, and observability tool credential storage. Security audit details and compliance framework claimed on trust.tracer.cloud but not reproduced here; requires independent review. No mention of signed releases, SBOM, or security reporting policy.

Alternatives to consider

PagerDuty Event Intelligence + Runbooks

Vendor-hosted, production-stable, SLA-backed incident correlation and response automation. Simpler onboarding but less transparency, no self-hosted option, higher cost at scale.

Sumo Logic Cloud SOAR

Commercial cloud-native security orchestration and automation. Broader compliance (SOC2, FedRAMP), vendor support, but closed-source and higher pricing. Not open for AI model training.

Home-grown ML pipeline (internal)

Custom agents trained on your incident logs and runbooks using SageMaker, Datadog ML, or LangChain. Full control, proprietary data, but high upfront engineering cost and ongoing maintenance burden.

Software development agency

Build on opensre with DEV.co software developers

OpenSRE is suitable for self-hosted incident investigation and AI training use cases. Public alpha status requires careful evaluation of stability, integrations, and operational readiness. Engage the community via Discord and GitHub to clarify roadmap and production support expectations.

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opensre FAQ

Does OpenSRE execute remediation automatically?

Yes, optionally. README states 'Suggests next steps and, optionally, executes remediation actions.' However, execution scope and guardrails (approvals, rollback, blast radius limits) are not detailed; assess carefully before enabling in production.

Can I run OpenSRE offline or without external LLM APIs?

No. LLM provider and API key are mandatory (OpenAI, Codex, or other). Offline or local-model support is not documented. Assess cost and latency implications for your incident volume.

What is the performance/latency of an investigation?

Not documented. Investigation speed depends on LLM response time, observability API calls, tool-calling loop iterations, and data volume. Benchmark results mentioned in README but table appears incomplete in provided excerpt.

How does OpenSRE handle secrets (API keys, database credentials)?

Method of secret storage, rotation, and isolation during tool execution not documented. Assume plaintext or environment-based storage until clarified. Use secrets management (AWS Secrets Manager, HashiCorp Vault) in production.

Software development & web development with DEV.co

Adopting opensre is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate open-source observability software in production.

Evaluate OpenSRE for Your Incident Response Pipeline

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