opensre Agent工作流
AI Skill Hub 强烈推荐：opensre Agent工作流 是一款优质的Agent工作流。已获得 5.2k 颗 GitHub Star，AI 综合评分 8.2 分，在同类工具中表现稳健。如果你正在寻找可靠的Agent工作流解决方案，这是一个值得深入了解的选择。
📚 深度解析
opensre Agent工作流 工作流的设计遵循"最小配置，最大复用"原则：核心逻辑已经封装好，用户只需配置自己的 API Key 和业务参数即可快速上手。工作流内置错误处理和重试机制，在网络波动或 API 限速等情况下仍能稳定运行，适合作为生产环境的自动化基础设施。
在实际部署时，建议先在测试环境中运行 3-5 次，验证各个环节的输出结果符合预期，再部署到生产环境。AI Skill Hub 评分 8.2 分，是同类 Agent 工作流中的精选推荐。
📋 工具概览
专为AI时代设计的开源SRE智能体工具包，支持构建自定义AI运维代理。集成告警、监控、事件管理等功能，适合运维工程师、SRE团队和DevOps工程师构建智能化运维工作流。
opensre Agent工作流 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排，将复杂的多步骤任务拆解为清晰的自动化流程，实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成，适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
📖 中文文档
专为AI时代设计的开源SRE智能体工具包，支持构建自定义AI运维代理。集成告警、监控、事件管理等功能，适合运维工程师、SRE团队和DevOps工程师构建智能化运维工作流。
opensre Agent工作流 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排，将复杂的多步骤任务拆解为清晰的自动化流程，实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成，适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
- 可视化 Agent 工作流编排，无需编写复杂代码
- 支持多步骤自动化任务链，实现全流程无人值守
- 与外部 API、数据库和第三方服务无缝集成
- 内置错误处理与自动重试机制，保障稳定运行
- 提供可复用的自动化模板，快速在同类场景部署
- 自动化日常重复性工作，将精力集中于创造性任务
- 构建数据采集 → 处理 → 输出的完整自动化管线
- 实现跨平台、跨系统的数据流转和业务协同
# 方式一：pip 安装（推荐）
pip install opensre
# 方式二：虚拟环境安装（推荐生产环境）
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install opensre
# 方式三：从源码安装（获取最新功能）
git clone https://github.com/Tracer-Cloud/opensre
cd opensre
pip install -e .
# 验证安装
python -c "import opensre; print('安装成功')"
- 访问 GitHub 仓库获取工作流文件
- 在对应平台（Dify / Flowise / Make 等）中找到「导入工作流」功能
- 上传工作流文件
- 按照提示配置必要的环境变量和 API Key
- 运行测试确认流程正常后投入使用
# 命令行使用
opensre --help
# 基本用法
opensre input_file -o output_file
# Python 代码中调用
import opensre
# 示例
result = opensre.process("input")
print(result)
# opensre 配置文件示例（config.yml） app: name: "opensre" debug: false log_level: "INFO" # 运行时指定配置文件 opensre --config config.yml # 或通过环境变量配置 export OPENSRE_API_KEY="your-key" export OPENSRE_OUTPUT_DIR="./output"
简介
<p align="center"> <img src="docs/logo/opensre-logo-white.svg" alt="OpenSRE" width="360" /> </p>
OpenSRE v0.1: Build Your Own AI SRE Agents
<p>The open-source framework for AI SRE agents, and the training and evaluation environment they need to improve. Connect the 60+ tools you already run, define your own workflows, and investigate incidents on your own infrastructure.</p>
<p align="center"> <a href="https://github.com/Tracer-Cloud/opensre/actions/workflows/ci.yml?branch=main"><img src="https://img.shields.io/github/actions/workflow/status/Tracer-Cloud/opensre/ci.yml?branch=main&style=for-the-badge" alt="CI status"></a> <a href="https://github.com/Tracer-Cloud/opensre/releases"><img src="https://img.shields.io/badge/status-public%20alpha-orange?style=for-the-badge" alt="Project status: public alpha"></a> <a href="https://github.com/Tracer-Cloud/opensre/blob/main/LICENSE"><img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg?style=for-the-badge" alt="Apache 2.0 License"></a> <a href="https://discord.gg/7NTpevXf7w"><img src="https://img.shields.io/badge/Discord-Join%20Us-5865F2?style=for-the-badge&logo=discord&logoColor=white" alt="Discord"></a> <a href="https://greptile.com"><img src="https://img.shields.io/badge/Sponsored%20by-Greptile-27E99F?style=for-the-badge&labelColor=3D3B4F&logo=data%3Aimage%2Fsvg%2Bxml%3Bbase64%2CPHN2ZyB3aWR0aD0iMzY3IiBoZWlnaHQ9IjQyMCIgdmlld0JveD0iMCAwIDM2NyA0MjAiIGZpbGw9Im5vbmUiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyI%2BCjxwYXRoIGQ9Ik0yNDAuMjY5IDQ5LjgxNTRMMTY2LjgwNCAxMTUuOTYzTDExNS45NjYgMTU5LjQ0TDE4MS4zMzUgMjIwLjU4NUwyNDkuNzg0IDE2Mi4wNDhMMTk2Ljc4IDExMi40N0wyNTMuMDY4IDYxLjc4ODFMMzYyLjYwNSAxNjQuMjQ2TDE3OC43MzkgMzIxLjQ4OUwzLjE0NTAyIDE1Ny4yNDJMMTg3LjAxMSAwTDI0MC4yNjkgNDkuODE1NFoiIGZpbGw9IiNGRkZGRkYiLz4KPHJlY3Qgd2lkdGg9IjIzNi40NTMiIGhlaWdodD0iODMuNDU2NiIgdHJhbnNmb3JtPSJtYXRyaXgoMC43NTQ3MSAtMC42NTYwNTkgMCAxIDE4OC4wMTcgMzM2LjU0NCkiIGZpbGw9IiNGRkZGRkYiLz4KPHJlY3Qgd2lkdGg9IjIzNi40NTMiIGhlaWdodD0iODMuNDU2NiIgdHJhbnNmb3JtPSJtYXRyaXgoMC43MzEzNTQgMC42ODE5OTggMCAxIDAgMTc0Ljk2MikiIGZpbGw9IiNGRkZGRkYiLz4KPC9zdmc%2BCg%3D%3D" alt="Sponsored by Greptile"></a> </p>
<p align="center"> <a href="https://trendshift.io/repositories/25889" target="_blank"> <img src="https://trendshift.io/api/badge/repositories/25889" alt="Tracer-Cloud%2Fopensre | Trendshift" style="height: 30px; width: auto;" height="30" /> </a> </p>
<p align="center"> <strong> <a href="https://www.opensre.com/docs/quickstart">Quickstart</a> · <a href="https://www.opensre.com/docs">Docs</a> · <a href="https://opensre.com/docs/faq">FAQ</a> · <a href="https://trust.tracer.cloud/">Security</a> </strong> </p>
</div>
---
🚧 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
---
Capabilities & integrations
| 🔍 **Structured incident investigation** | Correlated root-cause 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 |
| 🌐 **Remote runtime RCA** | Investigate deployed services by name with live health probes and recent logs |
| 📡 **Hermes log watch** | Tail Hermes error logs, classify incidents, and deliver Telegram alerts |
| 🤖 **Full LLM flexibility** | Bring your own model — Anthropic, OpenAI, Codex, Ollama, Gemini, OpenRouter, 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.
---
Install
The root installer URL auto-detects Unix shell vs PowerShell and installs the latest build from main. OpenSRE moves quickly, so main is the latest stable version for normal installs.
macOS / Linux:
curl -fsSL https://install.opensre.com | bashThe macOS/Linux installer does not require sudo. If no writable bin directory is already on PATH, it installs to ~/.local/bin and prints the shell command to apply the PATH update.
Equivalent explicit main-channel form:
curl -fsSL https://install.opensre.com | bash -s -- --mainHomebrew:
brew tap tracer-cloud/tap
brew install tracer-cloud/tap/opensreWindows (PowerShell):
irm https://install.opensre.com | iex---
Deployment
Deploy OpenSRE as a standard Python/FastAPI runtime using the repo Dockerfile or a managed app host such as Railway, EC2, ECS, or Vercel. Set LLM_PROVIDER plus the matching API key (see .env.example); hosted layouts that need persistence should also configure DATABASE_URI and REDIS_URI.
Full deployment steps, Railway notes, and opensre remote ops → docs/DEVELOPMENT.md
---
Quick Start
Configure once, then pick how you want to run investigations:
opensre onboardInteractive shell — with no subcommand, opensre starts a REPL (TTY required). Describe incidents in plain language, stream investigations, and use slash commands for session control (/help, /status, /cost, /sessions, /resume, /compact, /new, /exit), integrations (/integrations list, /integrations verify), local agent fleet monitoring (/agents), and reasoning depth (/effort for OpenAI and Codex — low through max). Ctrl+C cancels an in-flight investigation without losing session state. See interactive shell commands for the full reference.
opensreOne-shot investigation — run the agent once against an alert file:
opensre investigate -i tests/e2e/kubernetes/fixtures/datadog_k8s_alert.jsonRemote runtime investigation — investigate a deployed service by name (live health, logs, and deployment status):
opensre investigate --service api-backendHermes log watch — tail a Hermes errors.log, classify incidents, and optionally alert on Telegram:
opensre hermes watchOther useful commands:
opensre integrations setup
opensre agents scan
opensre update
opensre uninstall # remove opensre and all local data---
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 · NVIDIA NIM · Bedrock | |
| **Observability** | <img src="docs/assets/icons/grafana.webp" width="16"> Grafana (Loki · Mimir · Tempo · annotations) · <img src="docs/assets/icons/datadog.svg" width="16"> Datadog · Honeycomb · Coralogix · <img src="docs/assets/icons/cloudwatch.png" width="16"> CloudWatch · <img src="docs/assets/icons/sentry.png" width="16"> Sentry · Elasticsearch · Better Stack · Splunk · Victoria Logs · SignOz · OpenObserve · OpenSearch · Azure Monitor · Hermes | [New Relic](https://github.com/Tracer-Cloud/opensre/issues/139) |
| **Infrastructure** | <img src="docs/assets/icons/kubernetes.png" width="16"> Kubernetes · <img src="docs/assets/icons/aws.png" width="16"> AWS (S3 · Lambda · EKS · EC2 · CloudTrail · Bedrock) · <img src="docs/assets/icons/gcp.jpg" width="16"> GCP · <img src="docs/assets/icons/azure.png" width="16"> Azure · ArgoCD · Helm · Jenkins | |
| **Database** | MongoDB · ClickHouse · PostgreSQL · MySQL · MariaDB · MongoDB Atlas · Azure SQL · Snowflake · Redis · RDS · Supabase | |
| **Data Platform** | Apache Airflow · Apache Kafka · Apache Spark · Prefect · RabbitMQ · Dagster | |
| **Dev Tools** | <img src="docs/assets/icons/github.webp" width="16"> GitHub · GitHub MCP · Bitbucket · GitLab | |
| **Incident Management** | <img src="docs/assets/icons/pagerduty.png" width="16"> PagerDuty · Opsgenie · Jira · Alertmanager · incident.io | [Trello](https://github.com/Tracer-Cloud/opensre/issues/361) · [ServiceNow](https://github.com/Tracer-Cloud/opensre/issues/314) · [Linear](https://github.com/Tracer-Cloud/opensre/issues/124) |
| **Communication** | <img src="docs/assets/icons/slack.png" width="16"> Slack · Google Docs · Discord · Telegram · WhatsApp | [Notion](https://github.com/Tracer-Cloud/opensre/issues/286) · [Teams](https://github.com/Tracer-Cloud/opensre/issues/138) · [Confluence](https://github.com/Tracer-Cloud/opensre/issues/313) |
| **Agent Deployment** | <img src="docs/assets/icons/vercel.png" width="16"> Vercel · <img src="docs/assets/icons/aws.png" width="16"> EC2 · <img src="docs/assets/icons/aws.png" width="16"> ECS · Railway | |
| **Protocols** | <img src="docs/assets/icons/mcp.svg" width="16"> MCP · <img src="docs/assets/icons/acp.png" width="16"> ACP · <img src="docs/assets/icons/openclaw.jpg" width="16"> OpenClaw |
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/OpenClaw).
Developing in this repo: docs/DEVELOPMENT.md (install from source, CI parity checks, dev container, benchmark, deployment detail, telemetry reference).
<p> <a href="https://discord.gg/7NTpevXf7w"> <img src="https://img.shields.io/badge/Join%20our%20Discord-5865F2?style=for-the-badge&logo=discord&logoColor=white" alt="Join our Discord" /> </a> </p>
<p align="center"> <a href="https://www.star-history.com/#Tracer-Cloud/opensre&Date"> <img src="https://api.star-history.com/svg?repos=Tracer-Cloud/opensre&type=Date" alt="Star History Chart"> </a> </p>
Thanks goes to these amazing people:
<a href="https://github.com/Tracer-Cloud/opensre/graphs/contributors"> <img src="https://contrib.rocks/image?repo=Tracer-Cloud/opensre&max=200" alt="Contributors" /> </a>
---
opensre是AI运维领域的有力探索，工程化程度高、社区活跃、功能完整。集成主流监控平台、支持自定义代理，对现代SRE团队具有实用价值。建议关注后续迭代。
- 需要让 Claude / Cursor 操作本地工具的 AI 工程师
- 构建多智能体协作系统的 Agent 开发者
- 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC，避免暴露公网
- 生产部署优先使用 Docker Compose 隔离依赖，并挂载 volume 持久化数据
- 本地部署优先选 GGUF 量化模型，节省显存并保持响应速度
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- MCP 配置路径拼错或权限不足，重启 Claude Desktop 才生效
- 容器内无法访问宿主机 localhost — 使用 host.docker.internal
- 显存不足直接 OOM — 优先降低 context 或换更小的量化模型
- Python 依赖冲突：建议用 venv / uv 隔离环境
- Docker：opensre 提供官方镜像，docker compose up 一键启动
- 本地部署：CPU 8GB 起，GPU 推荐 16GB+ 显存
- 云端托管：可放在 Vercel / Railway / Fly.io 等 PaaS 平台
⚡ 核心功能
- 可视化 Agent 工作流编排，无需编写复杂代码
- 支持多步骤自动化任务链，实现全流程无人值守
- 与外部 API、数据库和第三方服务无缝集成
- 内置错误处理与自动重试机制，保障稳定运行
- 提供可复用的自动化模板，快速在同类场景部署
- 需要让 Claude / Cursor 操作本地工具的 AI 工程师
- 构建多智能体协作系统的 Agent 开发者
- 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC，避免暴露公网
- 生产部署优先使用 Docker Compose 隔离依赖，并挂载 volume 持久化数据
- 本地部署优先选 GGUF 量化模型，节省显存并保持响应速度
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- MCP 配置路径拼错或权限不足，重启 Claude Desktop 才生效
- 容器内无法访问宿主机 localhost — 使用 host.docker.internal
- 显存不足直接 OOM — 优先降低 context 或换更小的量化模型
👥 适合人群
🎯 使用场景
- 自动化日常重复性工作，将精力集中于创造性任务
- 构建数据采集 → 处理 → 输出的完整自动化管线
- 实现跨平台、跨系统的数据流转和业务协同
⚖️ 优点与不足
- +GitHub 5.2k Star，社区高度认可
- +Apache-2.0 协议，可免费商用
- +大幅减少重复性人工操作
- +可视化流程，清晰直观
- +可扩展性强，支持复杂场景
- −初始配置和调试需投入一定时间
- −强依赖外部服务的稳定性
- −复杂场景需具备一定技术基础
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
✅ Apache 2.0 — 宽松开源协议，可商用，需保留版权声明和 NOTICE 文件，含专利授权条款。
🔗 相关工具推荐
❓ 常见问题 FAQ
总体来看，opensre Agent工作流 是一款质量优秀的Agent工作流，在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态，建议收藏备用，结合自身场景选择合适时机引入使用。
| 原始名称 | opensre |
| 原始描述 | 开源AI工作流：Build your own AI SRE agents. The open source toolkit for the AI era.。⭐5.2k · Python |
| Topics | AI代理运维自动化告警管理事件响应工作流 |
| GitHub | https://github.com/Tracer-Cloud/opensre |
| License | Apache-2.0 |
| 语言 | Python |
收录时间：2026-05-16 · 更新时间：2026-05-19 · License：Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。
🤖 交给 Agent 安装 · opensre Agent工作流
选择 Agent 类型，复制安装指令后粘贴到对应客户端