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Build your own AI SRE agents. The open source toolkit for the AI era.
This repository page has enough original analysis, source evidence, and comparison context to be treated as an indexable Git-Stars review.
100
review score
Problem solved
OpenSRE addresses the lack of scalable training data and clear feedback loops for AI agents in production incident response, which is slower, noisier, and harder to simulate than code tasks. It provides a structured environment with scored synthetic RCA suites and real-world tests to train and evaluate AI SRE agents effectively.
Deployment reality
The available setup signal starts with: curl -fsSL https://install.opensre.com | bash. Treat this as a starting point, then ask Codex or Claude to inspect the README, environment variables, runtime version, and deployment target before production use.
Commercial use
The recorded license is Apache-2.0. This is a useful commercial-use signal, but teams should still verify license text, dependencies, model/API terms, and trademark constraints.
Capability ceiling
Developers can build AI SRE agents that autonomously investigate and resolve production incidents by connecting to 60+ tools (logs, metrics, traces, runbooks, Slack). Real-world use cases include automated root cause analysis, incident response, and remediation across Kubernetes, EC2, CloudWatch, Lambda, ECS Fargate, and Flink. The framework supports custom workflows and adversarial testing, enabling agents to handle complex, distributed failures.
Git-Stars is independent and not affiliated with GitHub or this project. Analysis may be AI-assisted and based on public repository metadata plus short README-derived summaries. We do not mirror full README files, docs, issues, or social comments.
Tracer-Cloud/opensre is tracked as a Python project in the AI Agent, Developer Tool, Data Tool, Observability, UI Framework area. This evaluation combines public GitHub metadata, category signals, short source summaries, and Git-Stars editorial rules rather than copying project documentation.
Momentum check: the repository has 9.9k total stars, with +0 today, +537 this week, and +0 this month. These growth windows help distinguish durable adoption from short-lived visibility spikes.
Maintenance check: current activity is Active; the latest push was 19 days ago, and the open issue queue is 156, about 1.58% of total stars. Treat this as an adoption signal, not a substitute for engineering due diligence.
Adoption check: 1.4k forks and 9.9k watchers suggest how often the project is reused or followed. License signal: Apache-2.0. Always verify license compatibility before commercial or internal use.
Practical fit: this project is most relevant when you need Python teams evaluating ecosystem-native tooling. Be cautious when you need a legal review, security audit, or production SLA.
Source check: Git-Stars currently has 2 explicit source reference(s) for this report and a recent growth signal of 537. Follow the original GitHub repository for final install, security, and release information.
Analysis
ReadyOriginal problem, capability, and verdict guidance are available.
Sources
ReadyRepository metadata and README/source references are attached.
License
ReadyApache-2.0 is recorded for review.
Maintenance
ReadyRecent activity is visible in repository metadata.
Alternatives
ReadyEnough nearby projects exist for comparison.
Momentum
9.9k Stars
Reuse
1.4k Forks
Attention
9.9k Watchers
Maintenance
active
License
Apache-2.0
Open issues
156
OpenSRE is an open-source framework for building AI SRE agents that resolve production incidents. It connects 60+ tools, defines custom workflows, and provides a training and evaluation environment for agentic infrastructure incident response.
Key Features
- Connects 60+ tools (logs, metrics, traces, runbooks, Slack) for unified incident investigation - Provides interactive shell, one-shot investigation, and remote runtime investigation modes - Includes synthetic RCA suites and real-world end-to-end tests for benchmarking and training AI SRE agents
AI Agent
Agent frameworks, autonomous workflows, and tool-use systems
Developer Tool
Tools that improve coding, testing, build, and local workflow
Data Tool
Databases, data pipelines, ETL, analytics, and vector search
Observability
Monitoring, tracing, logging, profiling, and reliability tools
UI Framework
Frontend frameworks, design systems, and interface libraries
curl -fsSL https://install.opensre.com | bash73
Health Score
Active
Commit Activity
Jan 13, 2026
Created
Aug 3, 2026
Last push
+79
Today's growth
+533
7-day growth
+533
30-day growth
Forks
Open
Watchers
Tracer-Cloud
GitHub profilecodecrafters-io/build-your-own-x
Master programming by recreating your favorite technologies from scratch.
sindresorhus/awesome
😎 Awesome lists about all kinds of interesting topics
public-apis/public-apis
A collective list of free APIs
freeCodeCamp/freeCodeCamp
freeCodeCamp.org's open-source codebase and curriculum. Learn math, programming, and computer science for free.
EbookFoundation/free-programming-books
:books: Freely available programming books
✓
License
✓
Forked
✓ Active
Maintained
Problem Solved
OpenSRE addresses the lack of scalable training data and clear feedback loops for AI agents in production incident response, which is slower, noisier, and harder to simulate than code tasks. It provides a structured environment with scored synthetic RCA suites and real-world tests to train and evaluate AI SRE agents effectively.
Capabilities
Developers can build AI SRE agents that autonomously investigate and resolve production incidents by connecting to 60+ tools (logs, metrics, traces, runbooks, Slack). Real-world use cases include automated root cause analysis, incident response, and remediation across Kubernetes, EC2, CloudWatch, Lambda, ECS Fargate, and Flink. The framework supports custom workflows and adversarial testing, enabling agents to handle complex, distributed failures.
Bottom Line
OpenSRE is ideal for SRE teams and AI researchers looking to automate incident response with AI agents. It is not suitable for those seeking a fully stable, production-ready solution (public alpha). The key trade-off is between the potential for advanced AI-driven SRE and the current instability and evolving APIs.