Orqen: An AI Workflow OS for Multi-Agent Orchestration

Orqen is an open-source AI workflow orchestration platform positioned as an AI Workflow OS for Multi-Agent Orchestration, aiming to help developers build production-grade multi-agent applications. Its core features include:

• Visual workflow canvas
• Asynchronous execution engine
• Human approval loop
• Full observability The tech stack includes Next.js, FastAPI, Celery, PostgreSQL, etc. It also serves as an alternative to LangChain, providing better support for complex scenarios in production environments.

Project Background

With the evolution of Large Language Model (LLM) capabilities, developers need to combine multiple AI services, tools, and agents into complex workflows. However, existing solutions lack distributed execution capabilities, state persistence, and human intervention mechanisms.

Project Positioning

Orqen was born in this context, positioned as an AI Workflow OS with core concepts: n8n-style visual orchestration + multi-agent collaboration + enterprise-level observability. Unlike simple chain-based tools, it provides a complete execution engine, state management, and human approval mechanisms, adapting to complex scenarios in production environments.

Orqen uses a front-end and back-end separation architecture, with tech stack selected for both efficiency and performance:

• Front-end layer: Next.js14 + TypeScript + Tailwind CSS + React Flow + Zustand
• Back-end layer: FastAPI + Python3.12 + SQLAlchemy2 + Pydanticv2
• Task queue: Celery5.4 + Redis (Upstash)
• Data persistence: PostgreSQL16 + pgvector extension
• AI integration layer: Anthropic Claude API (Sonnet3.5)

Visual Canvas

A drag-and-drop canvas based on React Flow, supporting node placement and connection to define execution order.

Rich Node Types

Built-in various node types:

• LLM Node (purple): Calls Claude, supports prompt templates and JSON structured output
• HTTP Node (blue): REST API requests, supports multiple authentication methods
• Condition Node (amber): Branching workflows
• Approval Node (cyan): Pauses for human review and resumes after submission
• Slack/Email Node: Message notifications
• Code Node (orange): Sandboxed execution of Python snippets

Asynchronous Execution and State Management

ExecutionContext serializes node outputs and states into JSONB, supporting cross-process recovery to improve reliability.

Human Approval Loop

Pauses at key steps to wait for human review, automatically resumes after results are submitted—suitable for high-risk scenarios.

Real-Time Observability

Pushes node-level status via SSE; monitor panels display inputs, outputs, and execution durations.

Deployment Methods

• Local development: Requires Python3.12+, Node.js20+, PostgreSQL, Redis; start FastAPI, Celery Worker, and Next.js front-end.
• Docker Compose: One-click startup of all services (FastAPI:8000, Next.js:3000, Flower:5555).
• Production deployment: Front-end on Vercel, back-end/Worker on Railway, database using Neon Serverless PostgreSQL.

Typical Scenarios

Example workflow "Research & Notify":

1. Trigger receives topic and email
2. LLM researches materials
3. Code node counts word count
4. LLM generates summary
5. Approval node for human review
6. After approval, send Slack notification and email in parallel

Current Status

Core features completed: Execution engine, REST API, React Flow canvas, real-time SSE monitoring, multi-type nodes, approval mechanism, example workflows, Docker/Railway deployment.

Future Roadmap

• Observability dashboard (token cost, execution history charts)
• pgvector long-term memory node
• Clerk JWT authentication
• AgentNode (multi-step Claude tool loop)
• AI evaluation layer

Orqen represents the development direction of AI workflow orchestration tools: shifting from simple chain calls to complete distributed systems. Its design trade-offs reflect an understanding of production environments:

• Building a custom orchestration engine instead of relying on LangGraph
• Using BFS instead of recursive traversal
• Delayed template parsing to support dynamic upstream outputs For developers, if you need to build complex AI applications, Orqen provides a more robust option than scripts and a more flexible one than commercial platforms. As multi-agent systems become popular, the value of such open-source infrastructure will become increasingly prominent.