Not just a search tool. An autonomous agent that decides the fairest meeting point for everyone.
Most location tools return results near you. MeetSpot calculates the geographic center of all participants and returns AI-ranked venues that minimize everyone's travel time.
| Traditional Tools | MeetSpot |
|---|---|
| Search near your location | Calculate fair center for all |
| Keyword-based ranking | AI-powered multi-factor scoring |
| Static results | Adaptive dual-mode routing |
| No reasoning | Explainable AI with chain-of-thought |
MeetSpot is an AI Agent - it makes autonomous decisions based on request complexity, not just executes searches.
User Request
│
┌──────────────┴──────────────┐
│ Complexity Router │
│ (Autonomous Decision) │
└──────────────┬──────────────┘
│
┌────────────────────┼────────────────────┐
│ │ │
▼ │ ▼
┌─────────────────┐ │ ┌─────────────────┐
│ Rule Mode │ │ │ Agent Mode │
│ (2-4 sec) │ │ │ (8-15 sec) │
│ Deterministic │ │ │ LLM-Enhanced │
└────────┬────────┘ │ └────────┬────────┘
│ │ │
└─────────────────────┼───────────────────┘
│
┌──────────────┴──────────────┐
│ 5-Step Processing │
│ Pipeline │
└──────────────┬──────────────┘
│
┌──────────┬──────────┬────┴────┬──────────┬──────────┐
│ │ │ │ │ │
▼ ▼ ▼ ▼ ▼ ▼
Geocode Center POI Ranking HTML Result
Calc Search Gen
The Agent autonomously decides which processing mode to use:
| Factor | Score | Example |
|---|---|---|
| Location count | +10/location | 4 locations = 40 pts |
| Complex keywords | +15 | "quiet business cafe with private rooms" |
| Special requirements | +10 | "parking, wheelchair accessible, WiFi" |
- Score < 40: Rule Mode (fast, deterministic, pattern-matched)
- Score >= 40: Agent Mode (LLM reasoning, semantic understanding)
Final Score = Rule Score × 0.4 + LLM Score × 0.6
The LLM analyzes semantic fit between venues and requirements, then blends with rule-based scoring. Results include Explainable AI visualization showing the agent's reasoning process.
| Step | Function | Details |
|---|---|---|
| Geocode | Address → Coordinates | 90+ smart mappings (universities, landmarks) |
| Center Calc | Fair point calculation | Spherical geometry for accuracy |
| POI Search | Venue discovery | Concurrent async search, auto-fallback |
| Ranking | Multi-factor scoring | Base(30) + Popularity(20) + Distance(25) + Scenario(15) + Requirements(10) |
| HTML Gen | Interactive map | Amap JS API integration |
# Clone and install
git clone https://github.com/JasonRobertDestiny/MeetSpot.git && cd MeetSpot
pip install -r requirements.txt
# Configure (get key from https://lbs.amap.com/)
cp config/config.toml.example config/config.toml
# Edit config.toml and add your AMAP_API_KEY
# Run
python web_server.pyPOST /api/find_meetspot
{
"locations": ["Peking University", "Tsinghua University", "Renmin University"],
"keywords": "cafe restaurant",
"user_requirements": "parking, quiet environment"
}Response:
{
"success": true,
"html_url": "/workspace/js_src/recommendation_xxx.html",
"center": {"lat": 39.99, "lng": 116.32},
"venues_count": 8
}| Endpoint | Method | Description |
|---|---|---|
/api/find_meetspot_agent |
POST | Force Agent Mode (LLM reasoning) |
/api/ai_chat |
POST | AI customer service chat |
/health |
GET | System health check |
/docs |
GET | Interactive API documentation |
 Agent Chain-of-Thought |
 Interactive Map View |
 Multi-Factor AI Scoring |
 Venue Recommendation Cards |
More Screenshots
 Homepage |
 Meeting Point Finder |
 Results Summary |
 AI Customer Service |
| Layer | Technologies |
|---|---|
| Backend | FastAPI, Pydantic, aiohttp, SQLAlchemy 2.0, asyncio |
| Frontend | HTML5, CSS3, Vanilla JavaScript, Boxicons |
| Maps | Amap (Gaode) - Geocoding, POI Search, JS API |
| AI | DeepSeek / GPT-4o-mini for semantic analysis |
| Deploy | Render, Railway, Docker, Vercel |
MeetSpot/
├── api/
│ └── index.py # FastAPI application entry
├── app/
│ ├── tool/
│ │ └── meetspot_recommender.py # Core recommendation engine
│ ├── config.py # Configuration management
│ └── design_tokens.py # WCAG-compliant color system
├── templates/ # Jinja2 templates
├── public/ # Static assets
└── workspace/js_src/ # Generated result pages
# Development server with hot reload
uvicorn api.index:app --reload
# Run tests
pytest tests/ -v
# Code quality
black . && ruff check . && mypy app/Contributions are welcome! Please:
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
|
Email: Johnrobertdestiny@gmail.com GitHub: Issues Blog: jasonrobert.me |
Personal WeChat |
WeChat Group |
MIT License - see LICENSE for details.
If MeetSpot helps you, please give it a star!