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Build your own AI SRE agents. The open source toolkit for the AI era.
这个仓库详情页已经具备足够的原创分析、来源证据和同类对比，可以作为 Git-Stars 的可索引评测页。
100
评测分
解决的问题
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.
部署现实
当前可用的安装信号是：curl -fsSL https://install.opensre.com | bash。这只能作为起点，正式使用前建议让 Codex 或 Claude 检查 README、环境变量、运行时版本和部署目标。
商业可用性
当前记录的许可证是 Apache-2.0。这是重要的商业使用信号，但仍应核验许可证全文、依赖许可证、模型/API 条款和商标限制。
能力上限
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 是独立产品，不隶属于 GitHub 或该项目。 分析可能由 AI 辅助生成，依据公开仓库元数据和 README 的短摘要。 我们不镜像完整 README、文档、Issue 或社媒评论。
Tracer-Cloud/opensre 被追踪为 Python 项目，主要属于 AI Agent, Developer Tool, Data Tool, Observability, UI Framework 方向。这个评估结合公开 GitHub 元数据、分类信号、短来源摘要和 Git-Stars 编辑规则，而不是复制项目文档。
增长检查：该仓库目前有 9.9k Star，今日 +0，本周 +537，本月 +0。这些窗口用于区分持续采用信号和短期曝光峰值。
维护检查：当前活跃度为 一般；最近一次推送距今 71 天，未关闭 Issue 为 156，约占总 Star 的 1.58%。这只是采用信号，不替代工程尽调。
采用检查：1.4k Fork 和 9.9k Watcher 反映项目被复用和关注的程度。许可证信号：Apache-2.0。商业或内部使用前请核验许可证兼容性。
适用判断：当你需要「Python 技术栈团队评估生态原生工具」时，这个项目更值得评估；如果「需要法律审查、安全审计或生产 SLA 保证」，则需要谨慎。
来源检查：Git-Stars 当前为这份报告保留了 2 个明确来源引用，近期增长信号为 537。最终安装、安全和版本信息仍应以原始 GitHub 仓库为准。
原创分析
充分已经具备问题、能力和结论层面的原创判断。
来源
充分已附带仓库元数据和 README/来源引用。
许可证
充分已记录 Apache-2.0，可作为评估依据。
维护
充分仓库元数据中可以看到近期活跃信号。
同类对比
充分已有足够相近项目用于对比。
热度
9.9k 星标
复用
1.4k 复刻
关注
9.9k 关注者
维护
moderate
许可证
Apache-2.0
未解决 Issue
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.
核心特性
- 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 | bash63
健康评分
一般
提交活跃度
Jan 13, 2026
创建于
Aug 3, 2026
最近提交
每日同步积累数据后，Star 历史将在此显示。
复刻
未解决
关注者
Tracer-Cloud
GitHub 主页codecrafters-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
✓
许可证
✓
Fork 情况
~ 一般
维护情况
解决的问题
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.
能力范围
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.
结论
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.