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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.
OpsinTech is an AI-Native Operations Platform for production environments. v1.0 is built on DeerFlow's Agent runtime (ByteDance, GitHub Trending #1), with multi-tenancy, RBAC, audit logging, visual admin dashboards, and database-driven configuration layered on top.
Our mission: turn AI Agents from impressive demos into governed, enterprise-ready platforms.
OpsinTech transforms AI Agents into a governed production platform with the following capabilities:
A unified workspace with three-tier resource isolation (user → tenant → global). Each user gets a personal tenant on registration, and tenant admins manage members, models, tools, and skills independently.
Modules:
make up to get everything runningmake dev for local developmentDeerFlow 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.
DeerFlow 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:
We'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.
Short-term (v1.x): Keep DeerFlow's runtime, add the governance layer — multi-tenancy, RBAC, audit, admin dashboards, database-backed config, model provider templates.
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.
user → tenant → global, strict data separationplatform_admin / tenant_admin / tenant_memberdocker compose up -dOpsinTech is not "DeerFlow with a dashboard." We're building a secure, governed, AI-Native Operations Platform for production environments.
On 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:
Discover → Understand → Decide → Execute → Audit
This space is almost entirely vacant in open source. That's why we need community voices.
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.
✅ v1.0 (Current) — Governable AI Agent Platform
├─ Multi-tenancy + RBAC + Audit
├─ 20+ model provider templates, database-backed
├─ Visual admin dashboards (platform + tenant)
├─ Terminal Agent with security governance
├─ Skill System with sandbox testing
└─ Docker one-command deploy
🔜 v1.1 — Smart Alerting
├─ Webhook / Alertmanager ingestion
├─ Raw Alert → Signal → Incident pipeline
├─ AI-powered summaries and context assembly
└─ Incident-to-workspace context linking
🔜 v1.2 — Workflows + Terminal
├─ Event-driven workflow engine
├─ SOP templates (service restart, log collection, health checks)
├─ Governed terminal execution + audit trail
└─ Full closed loop: Alert → Incident → Workflow → Terminal → Audit
📋 v2.0 — Digital Operators & Ecosystem
├─ Custom Agents: SOUL.md + tool_groups whitelist + Skill/Workflow binding
├─ Auto-route alerts to agent operators
├─ Skill marketplace: community contributions + official scenarios
├─ AI Analysis Workbench: data analysis, reports, PPT generation
└─ Gradual replacement of DeerFlow internals with our own architecture
Clone and configure
git clone https://github.com/opsintech/opsintech-platform.git
cd opsintech-platform
make config
Generates config.yaml with platform-level settings (database, sandbox, tool groups). No model configuration needed.
Start and log in
make up
Open http://localhost:2026. On first visit, you'll be redirected to the Setup Wizard to set your admin email and password.
You can also use
docker compose up -d, butmake uphandles config initialization and secret generation automatically.
Add models via admin UI
Log in as platform admin, go to Settings → Models, pick from 20+ provider templates, enter your API key.
Models are stored in the database. Do not configure models in
config.yamlfor production.
Docker production:
make up # Build images and start all services (recommended for first deploy)
make up-nobuild # Start services without rebuilding images
make down # Stop and remove containers
make rebuild-images # Remove all OpsinTech images and rebuild from scratch
Local development:
make check # Verify prerequisites (Node.js 22+, pnpm, uv, nginx)
make install # Install dependencies
make dev # Start dev servers (with hot-reload)
make stop # Stop all services
Docker development:
make docker-init # Pull sandbox image
make docker-start # Start Docker dev services
make docker-stop # Stop Docker dev services
make docker-logs # View all logs
Access: http://localhost:2026
In China, pulling base images during Docker builds may fail due to network issues. Pre-pull before building:
# Required images
docker pull python:3.12-slim
docker pull node:22-alpine
docker pull nginx:alpine
docker pull docker:cli
# Build-stage dependency
docker pull ghcr.io/astral-sh/uv:0.9.26
# Optional images
docker pull postgres:16-alpine # PostgreSQL mode
docker pull enterprise-public-cn-beijing.cr.volces.com/vefaas-public/all-in-one-sandbox:latest # Sandbox
For mirror accelerator setup, see the Quick Start Guide.
Three execution modes: Local / Docker containers / Docker + Kubernetes. See Sandbox Guide.
Configurable MCP servers (managed per-tenant via admin UI). Supports stdio/SSE/HTTP transports with OAuth token flows.
We welcome contributions — code, documentation, scenario suggestions, or real-world pain points. Open an Issue or PR.
MIT License. See LICENSE.
v1.0 is built on DeerFlow (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.
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.
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 mo
ภาพรวม
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.
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Opsintech Platform ทำงานแบบโลคัลผ่าน stdio
ตรวจสอบล่าสุดเมื่อ 2026-10-27 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.