[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-79925":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":15,"subscribersCount":15,"size":15,"stars1d":15,"stars7d":15,"stars30d":16,"stars90d":15,"forks30d":15,"starsTrendScore":15,"compositeScore":17,"rankGlobal":10,"rankLanguage":10,"license":18,"archived":19,"fork":19,"defaultBranch":20,"hasWiki":21,"hasPages":19,"topics":22,"createdAt":10,"pushedAt":10,"updatedAt":23,"readmeContent":24,"aiSummary":25,"trendingCount":15,"starSnapshotCount":15,"syncStatus":26,"lastSyncTime":27,"discoverSource":28},79925,"opsintech-platform","OpsinTech\u002Fopsintech-platform","OpsinTech","OpsinTech Platform — An enterprise-grade AI agent platform built on LangGraph, featuring multi-tenant architecture, role-based access control, sandboxed tool execution, and an admin UI for managing models, MCP servers, skills, and tools. Designed for production deployment with Docker, PostgreSQL support, and comprehensive governance capabilities.","http:\u002F\u002Fwww.opsintech.net",null,"Python",87,16,6,0,1,3.69,"MIT License",false,"main",true,[],"2026-06-12 02:03:55","# OpsinTech Platform\n\n[中文](.\u002FREADME_zh.md) | English\n\n[![Python](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPython-3.12%2B-3776AB?logo=python&logoColor=white)](.\u002Fbackend\u002Fpyproject.toml)\n[![Node.js](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNode.js-22%2B-339933?logo=node.js&logoColor=white)](.\u002FMakefile)\n[![License: MIT](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLicense-MIT-yellow.svg)](.\u002FLICENSE)\n[![Built on DeerFlow](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FBuilt_on-DeerFlow-8A2BE2)](https:\u002F\u002Fgithub.com\u002Fbytedance\u002Fdeer-flow)\n\nOpsinTech is an **AI-Native Operations Platform for production environments**. v1.0 is built on [DeerFlow](https:\u002F\u002Fgithub.com\u002Fbytedance\u002Fdeer-flow)'s Agent runtime (ByteDance, GitHub Trending #1), with multi-tenancy, RBAC, audit logging, visual admin dashboards, and database-driven configuration layered on top.\n\n> **Our mission: turn AI Agents from impressive demos into governed, enterprise-ready platforms.**\n\n## Table of Contents\n\n- [Why OpsinTech Exists](#why-opsintech-exists)\n- [v1.0 — What We Built](#v10--what-we-built)\n- [Our Vision](#our-vision)\n- [Roadmap](#roadmap)\n- [Quick Start](#quick-start)\n- [Supported Models](#supported-models)\n- [Internationalization](#internationalization)\n- [Documentation](#documentation)\n- [Contributing](#contributing)\n- [License](#license)\n- [Acknowledgments](#acknowledgments)\n\n## Why OpsinTech Exists\n\n### DeerFlow: A Proven Agent Runtime\n\nDeerFlow proved that AI Agents can go beyond chat — they can plan, decompose tasks, execute code in isolated sandboxes, and produce real files and deliverables. Sub-agent parallelism, persistent memory, sandbox isolation — these capabilities earned it 37K+ GitHub stars, and deservedly so.\n\n### From Agent Runtime to Production Platform\n\nDeerFlow excels as an Agent runtime and developer toolkit. OpsinTech builds on top of it to address what teams need when moving from evaluation to production:\n\n| Scenario | DeerFlow | OpsinTech Enhancement |\n|---|---|---|\n| **Team Collaboration** | Single-user mode | Multi-tenancy with `user → tenant → global` isolation |\n| **Access Control** | No role-based permissions | RBAC with `platform_admin` \u002F `tenant_admin` \u002F `tenant_member` |\n| **Model Configuration** | Hand-edited YAML files | 20+ provider templates in admin UI, database-backed, per-tenant assignment |\n| **Administration** | Command-line scripts only | Visual admin dashboards for both platform and tenant levels |\n| **Audit & Compliance** | No operation traceability | Full audit trail for all operations, per-tenant isolation |\n| **Skills & MCP** | Functional runtime, manual setup | Per-tenant management via admin UI, lifecycle tracking |\n| **Security Governance** | Basic sandbox isolation | User status management, mandatory password rotation, operation audit |\n| **Onboarding** | Configuration-heavy, steep learning curve | Docker one-command deploy, admin UI-driven setup |\n\n### Our Approach\n\nWe're not starting from scratch. DeerFlow's Agent runtime is one of the most mature in open source, and for v1.0 we chose to inherit it — then layer on what teams need to use it in production.\n\n**Short-term (v1.x)**: Keep DeerFlow's runtime, add the governance layer — multi-tenancy, RBAC, audit, admin dashboards, database-backed config, model provider templates.\n\n**Long-term (v2.x+)**: Gradually replace DeerFlow internal dependencies with our own architecture, incorporating real operations scenarios — alert ingestion, incident management, workflow orchestration, governed terminals — to form a true AI-Native Operations Platform.\n\n## v1.0 — What We Built\n\n### Multi-Tenancy & RBAC\n\n- **Three-tier resource isolation**: `user → tenant → global`, strict data separation\n- **Two admin dashboards**: Platform admin (users, tenants, audit, model templates) + Tenant admin (members, models, tools, skills)\n- **Auto-created personal tenant** on registration, zero manual setup\n- Roles: `platform_admin` \u002F `tenant_admin` \u002F `tenant_member`\n\n### Model Management, Done Right\n\n- **20+ provider templates** in the admin UI — dropdown to select, auto-fills provider class, base URL, and capability flags. No LangChain class paths to memorize\n- Models stored in database, assigned per-tenant, with active \u002F deprecated \u002F retired lifecycle\n- Providers: OpenAI · Anthropic · DeepSeek · Azure OpenAI · Gemini · Ollama · OpenRouter · Groq · Together AI · SiliconFlow · DashScope · Zhipu · Moonshot · MiniMax · Baichuan · 01.AI · Volcengine Ark · Novita AI\n\n### Audit & Security\n\n- Full audit trail for all operations, per-tenant isolation\n- User status management (active \u002F suspended)\n- Mandatory password change on first login\n\n### Internationalization\n\n- UI supports 4 languages: English, 中文, 日本語, 한국어\n- Auto-detects browser language preference with manual switcher\n\n### Deployment & Operations\n\n- **Docker one-command deploy**: `docker compose up -d`\n- Artifact management: browse, preview, download all agent-generated files\n- Platform announcement system with tenant\u002Frole targeting\n\n## Our Vision\n\nOpsinTech is not \"DeerFlow with a dashboard.\" We're building a **secure, governed, AI-Native Operations Platform for production environments**.\n\nOn this platform, Agents don't just chat and generate code — they operate within boundaries: permission controls, audit records, execution approvals. Starting from Agent conversations, progressively extending into alerting, incidents, workflows, and terminal execution, ultimately forming a complete chain:\n\n> **Discover → Understand → Decide → Execute → Audit**\n\nThis space is almost entirely vacant in open source. That's why we need community voices.\n\n**Who we're looking for**: If you work in operations, security, compliance, or platform engineering, and you're excited by AI Agents but can't use them in production because of missing governance and security — you're exactly who we're building for. Your pain points and suggestions are OpsinTech's roadmap.\n\n## Roadmap\n\n```\n✅ v1.0 (Current) — Governable AI Agent Platform\n  ├─ Multi-tenancy + RBAC + Audit\n  ├─ 20+ model provider templates, database-backed\n  ├─ Visual admin dashboards (platform + tenant)\n  └─ Docker one-command deploy\n\n🔜 v1.1 — Smart Alerting\n  ├─ Webhook \u002F Alertmanager ingestion\n  ├─ Raw Alert → Signal → Incident pipeline\n  ├─ AI-powered summaries and context assembly\n  └─ Incident-to-workspace context linking\n\n🔜 v1.2 — Workflows + Terminal\n  ├─ Event-driven workflow engine\n  ├─ SOP templates (service restart, log collection, health checks)\n  ├─ Governed terminal execution + audit trail\n  └─ Full closed loop: Alert → Incident → Workflow → Terminal → Audit\n\n📋 v2.0 — Digital Operators & Ecosystem\n  ├─ Custom Agents: SOUL.md + tool_groups whitelist + Skill\u002FWorkflow binding\n  ├─ Auto-route alerts to agent operators\n  ├─ Skill marketplace: community contributions + official scenarios\n  ├─ AI Analysis Workbench: data analysis, reports, PPT generation\n  └─ Gradual replacement of DeerFlow internals with our own architecture\n```\n\n## Quick Start\n\n1. **Clone and configure**\n\n   ```bash\n   git clone https:\u002F\u002Fgithub.com\u002Fopsintech\u002Fopsintech-platform.git\n   cd opsintech-platform\n   make config\n   ```\n\n   Generates `config.yaml` with platform-level settings (database, sandbox, tool groups). **No model configuration needed.**\n\n2. **Start and log in**\n\n   ```bash\n   make up\n   ```\n\n   Open http:\u002F\u002Flocalhost:2026. On first visit, you'll be redirected to the Setup Wizard to set your admin email and password.\n\n   > You can also use `docker compose up -d`, but `make up` handles config initialization and secret generation automatically.\n\n3. **Add models via admin UI**\n\n   Log in as platform admin, go to **Settings → Models**, pick from 20+ provider templates, enter your API key.\n\n   > Models are stored in the database. Do **not** configure models in `config.yaml` for production.\n\n### Running Options\n\n**Docker production**:\n```bash\nmake up              # Build images and start all services (recommended for first deploy)\nmake up-nobuild      # Start services without rebuilding images\nmake down            # Stop and remove containers\nmake rebuild-images  # Remove all OpsinTech images and rebuild from scratch\n```\n\n**Local development**:\n```bash\nmake check           # Verify prerequisites (Node.js 22+, pnpm, uv, nginx)\nmake install         # Install dependencies\nmake dev             # Start dev servers (with hot-reload)\nmake stop            # Stop all services\n```\n\n**Docker development**:\n```bash\nmake docker-init     # Pull sandbox image\nmake docker-start    # Start Docker dev services\nmake docker-stop     # Stop Docker dev services\nmake docker-logs     # View all logs\n```\n\nAccess: http:\u002F\u002Flocalhost:2026\n\n### China Users: Preparing Images\n\nIn China, pulling base images during Docker builds may fail due to network issues. Pre-pull before building:\n\n```bash\n# Required images\ndocker pull python:3.12-slim\ndocker pull node:22-alpine\ndocker pull nginx:alpine\ndocker pull docker:cli\n\n# Build-stage dependency\ndocker pull ghcr.io\u002Fastral-sh\u002Fuv:0.9.26\n\n# Optional images\ndocker pull postgres:16-alpine          # PostgreSQL mode\ndocker pull enterprise-public-cn-beijing.cr.volces.com\u002Fvefaas-public\u002Fall-in-one-sandbox:latest  # Sandbox\n```\n\nFor mirror accelerator setup, see the [Quick Start Guide](QUICKSTART.md#china-users-preparing-images-for-local-builds).\n\n### Advanced\n\n#### Sandbox Mode\n\nThree execution modes: Local \u002F Docker containers \u002F Docker + Kubernetes. See [Sandbox Guide](backend\u002Fdocs\u002FCONFIGURATION.md#sandbox).\n\n#### MCP Server\n\nConfigurable MCP servers (managed per-tenant via admin UI). Supports stdio\u002FSSE\u002FHTTP transports with OAuth token flows.\n\n## Supported Models\n\n| Category | Providers |\n|---|---|\n| **Global Leaders** | OpenAI · Anthropic · DeepSeek · Azure OpenAI · Gemini |\n| **Aggregators** | OpenRouter · Groq · Together AI |\n| **Self-Hosted** | Ollama |\n| **China-Based** | DashScope · Zhipu · Moonshot · MiniMax · Baichuan · 01.AI · Volcengine Ark · SiliconFlow |\n| **Other** | Novita AI · Any OpenAI-compatible provider |\n\n\n## Documentation\n\n- [5-Minute Quick Start](QUICKSTART.md)\n- [Configuration Guide](backend\u002Fdocs\u002FCONFIGURATION.md)\n- [Architecture Overview](backend\u002Fdocs\u002FARCHITECTURE.md)\n- [API Reference](backend\u002Fdocs\u002FAPI.md)\n- [Contributing Guide](CONTRIBUTING.md)\n\n## Contributing\n\nWe welcome contributions — code, documentation, scenario suggestions, or real-world pain points. Open an Issue or PR.\n\n## License\n\nMIT License. See [LICENSE](.\u002FLICENSE).\n\n## Acknowledgments\n\n### DeerFlow\n\nv1.0 is built on [DeerFlow](https:\u002F\u002Fgithub.com\u002Fbytedance\u002Fdeer-flow) (by ByteDance). DeerFlow's Agent runtime — LangGraph orchestration, sub-agent parallelism, sandboxed execution, persistent memory — is one of the best Agent infrastructures in open source. Deep gratitude to the DeerFlow team and community.\n\n**Our relationship with DeerFlow**: In v1.x, we inherit DeerFlow's Agent runtime and add governance. As we evolve, we'll gradually replace internal dependencies and form our own architecture. OpsinTech's long-term identity is not a DeerFlow fork — it's an independent, operations-focused AI-Native platform.\n\n### Open-Source Community\n\n- [LangChain](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain)\n- [LangGraph](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph)\n- [Next.js](https:\u002F\u002Fnextjs.org\u002F)\n","OpsinTech Platform 是一个面向生产环境的企业级AI代理平台。基于LangGraph构建，它具备多租户架构、基于角色的访问控制（RBAC）、沙箱工具执行以及用于管理模型、MCP服务器、技能和工具的可视化管理界面等核心功能。技术上采用Python开发，并支持Docker部署与PostgreSQL数据库，确保了全面的治理能力。适用于需要将AI代理从概念验证阶段过渡到企业级应用的场景，特别是在要求高安全性和合规性的环境中。",2,"2026-06-11 03:58:34","CREATED_QUERY"]