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Secure AI virtual data roomsopensre
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.
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.
Get the opensre source
Clone the repository and explore it locally.
git clone https://github.com/Tracer-Cloud/opensre.gitcd opensre# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
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 |
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.
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?
Can I run OpenSRE offline or without external LLM APIs?
What is the performance/latency of an investigation?
How does OpenSRE handle secrets (API keys, database credentials)?
Build it with a software development company
Considering opensre for production? DEV.co is a software development company that builds, integrates, and maintains open-source observability software — and takes ownership of the parts your team would rather not run.
Evaluate OpenSRE for Your Incident Response Pipeline
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.