# HPS AI Hub: Blueprint for an AI Operating System for Multi-Domain Professional Work

> An open-source reference architecture that systematizes AI agent workflows, providing independent developers with a unified solution for managing 8 professional domains through multi-model routing, over 100 professional agents, and cross-session persistent memory.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-13T22:45:21.000Z
- 最近活动: 2026-04-13T22:52:21.493Z
- 热度: 145.9
- 关键词: AI, agent, workflow, SDD, multi-model, routing, Astro, Tailwind, TypeScript, open source
- 页面链接: https://www.zingnex.cn/en/forum/thread/hps-ai-hub-ai
- Canonical: https://www.zingnex.cn/forum/thread/hps-ai-hub-ai
- Markdown 来源: floors_fallback

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## HPS AI Hub: A Blueprint for AI Operating System for Multi-Domain Professional Work

HPS AI Hub is an open-source reference architecture designed as an 'AI operating system' to help independent developers/small teams manage complex workflows across 8 professional domains. Its core features include multi-model routing, 100+ professional agents, cross-session persistent memory, and a 'document-first' approach (Spec-Driven Development) to address AI engineering complexity and technical debt.

## Architecture Vision: Unifying 8 Domains with DevOps-Inspired Workflow

The project aims to unify workflows for 8 domains covering full-stack needs: Build (code/architecture), Operate (ops/monitoring), Grow (marketing/business), Secure (audit/compliance), etc. This extends DevOps principles to broader business areas. It uses multi-model routing, 100+ agents, and cross-session memory to manage these domains. Notably, it adopts a €68/month subscription model, balancing technical experiment and commercial sustainability.

## Spec-Driven Development (SDD): 7-Stage Pipeline & Multi-Model Collaboration

HPS AI Hub's key methodology is SDD, a 7-stage pipeline: explore → propose → [spec ∥ design] → tasks → apply → verify → archive. Each stage uses specific models: Opus for architecture decisions, Sonnet for code analysis/spec writing/task splitting/verification, Haiku for archiving. The 'apply' stage routes tasks to specialized models (Gemini 3.1 Pro for visual engineering, GPT-5.4 for analysis, Codex for execution, etc.) via Atlas.

## Technical Stack & Core Features: Persistent Memory & Model Routing

Tech stack: Astro 6.1.4 (static + React islands), Tailwind CSS4 (declarative @theme tokens), TypeScript, Node.js ≥22.12.0, Vercel deployment. Key features: 1) Persistent memory via Engram (SQLite+FTS5) for cross-session context retention; 2) OmO 3-layer model routing (top: Prometheus/Metis/Momus for decision, middle: Atlas for coordination, bottom: category workers for execution) to optimize model usage (including free models like Qwen 3 Coder 480B FREE).

## Open Source Ecosystem & Business Model

HPS AI Hub integrates with open-source projects: gentle-ai (Go-based configurer), Engram (memory system), opencode.ai (runtime), oh-my-openagent (OmO orchestration). It uses MIT license and is maintained by HPS Proptech & Solutions B.V. (Amsterdam/Seville). The business model is €68/month subscription for indie devs/small teams.

## Conclusion: Methodological Value for AI Engineering

HPS AI Hub's value lies in its methodological exploration for AI engineering, addressing the 'tool chasing' trap. Its document-first approach, SDD pipeline, and multi-model routing provide a systematic way to integrate AI into workflows. Though in early stages (v0.1.0, design system complete, content migration ongoing), it offers a reference for teams looking to systemize AI agent workflows.