An autonomous AI research scientist for urban science - from topic to publication.
Urban AI Scientist is an end-to-end automated research pipeline designed forthe urban science domain. Given a research topic, it independently conducts thefull research lifecycle: literature-grounded idea generation, open-data discovery and acquisition, experimental design and execution, statistical analysis, figure generation, and Nature-format paper writing with PDF compilation. The system embodies the AI Scientist paradigm, enabling autonomous hypothesis formation, empirical validation, and scholarly communication in urban science.
🌐 Live Demo: https://cocoa-heap-turbulent.ngrok-free.dev
💬 中文文档 →
Authors are listed alphabetically by first name.
| Author | Affiliation |
|---|---|
| Ao Xu | Zhongguancun Academy; Jilin University |
| Jiankun Zhang | Zhongguancun Academy; Jilin University |
| Jingzhi Wang | Zhongguancun Academy; East China Normal University |
| Runwen You | Zhongguancun Academy; Jilin University |
| Tong Xia | Tsinghua University; Zhongguancun Academy |
| Yong Li | Tsinghua University; Zhongguancun Academy |
Research Topic
│
▼
┌──────────────────────────────────────────────┐
│ Phase 1: Idea Generation │
│ Generate novel research idea with literature │
│ grounding and related datasets │
└──────────────────────────────────────────────┘
│ (User review: Continue · Regenerate · Cancel)
▼
┌──────────────────────────────────────────────┐
│ Phase 2: Data Seeking │
│ Search, discover, and download open datasets │
│ for the proposed study │
└──────────────────────────────────────────────┘
▼
┌──────────────────────────────────────────────┐
│ Phase 3: Paper Planning │
│ Build a claim-first paper plan with evidence │
│ graph, task queue, and figure blueprints │
└──────────────────────────────────────────────┘
▼
┌──────────────────────────────────────────────┐
│ Phase 4: Paper Writing │
│ Execute experiments → generate figures → │
│ write LaTeX → compile PDF (Nature format) │
└──────────────────────────────────────────────┘
Four phases run sequentially with real-time progress streaming.
Visit the live demo URL.
Two ways to access:
| Method | Description |
|---|---|
| Passphrase | Enter the shared access key. API Key field will be hidden automatically. |
| Own API Key | Provide your own API Key + Base URL (any OpenAI-compatible endpoint). |
| Method | Description |
|---|---|
| CAMP | Literature-driven: retrieves related papers first, then generates grounded ideas |
| SEMM | Direct generation: creates ideas from the topic without prior retrieval |
| FAST | Lightweight LLM mode: fastest, no backend API dependency |
When accessing via passphrase, you can switch between available models:
claude-sonnet-4-6 · glm-5.1 · qwen3.5-plus · qwen3.7-max · mimo-v2.5-pro · deepseek-v4-pro
Default: claude-sonnet-4-6.
Both English and Chinese are supported. Examples:
Urban flood prediction using satellite imagery基于 BRFSS 数据集量化高温与睡眠不足的关系Impact of ride-hailing on urban commuting efficiency
Click Launch to start the pipeline:
- Real-time progress log via SSE streaming
- Each phase highlights on completion
- Downloadable artifacts after each stage
After Phase 1, the system presents an idea preview (title + abstract):
| Action | Description |
|---|---|
| Continue | Accept and proceed to data seeking |
| Regenerate | Clear current idea and try again |
| Cancel | Stop the entire pipeline |
Auto-continues after 30 seconds of inactivity.
| Phase | Artifact |
|---|---|
| Idea | Research idea document (Markdown) |
| Data | Dataset download report |
| Plan | Paper plan with experiment design |
| Paper | Final compiled PDF (Nature format) |
See the examples/ directory for complete papers generated by the system,
including final PDFs and (where available) full job directories with intermediate outputs
(ideas, data reports, plans, and figures).
┌──────────────┐ SSE ┌──────────────────┐
│ Frontend │ ◄──────────► │ FastAPI Backend │
│ (Vue 3 SPA) │ │ │
└──────────────┘ └────────┬──────────┘
│
4× Sequential AI Agent Runs
(streaming JSON mode)
│
┌────────┼────────┐
▼ ▼ ▼
Idea Data Paper
Generator Seeker Writer
- Frontend: Vue 3 + Vite, single-page application
- Backend: FastAPI with Server-Sent Events for real-time streaming
- Pipeline Engine: Four sequential AI agent runs, each executing a specialized skill
- Paper Compilation: LaTeX (
nature.clstemplate) +latexmk - Public Access: ngrok (fixed domain) / Cloudflare Tunnel
- One job at a time — a concurrency lock ensures only one pipeline runs simultaneously
- Keep the browser tab open — disconnecting cancels the running job
- Estimated duration: 30–60 minutes end-to-end (Idea ~5 min → Data ~10 min → Plan ~10 min → Paper ~20–40 min)
- API cost: ~$5–15 for a full run with your own API key (varies by model and topic complexity)
- Storage: Artifacts are stored server-side by job ID. Download promptly.
Q: The page says "another job is running".
A: Wait for the current job to finish and try again. Only one pipeline runs at a time.
Q: Can I resume a failed job?
A: Yes. The system skips completed phases on retry, so it picks up from where it left off.
Q: Are Chinese topics supported?
A: Yes, both English and Chinese topics work well.
Q: Do I need an Anthropic API key?
A: No. Any OpenAI-compatible API endpoint works. Just provide your API Key and Base URL.
Q: What format is the final paper?
A: A single-file LaTeX document following Nature journal format, plus a compiled PDF.
MIT
Need a passphrase or have questions? Open an Issue.