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| title: LifeFlow AI - Intelligent Trip Planning System | |
| emoji: πΊοΈ | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 5.49.1 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| short_description: Don't just plan the trip. Design the flow. | |
| thumbnail: https://cdn-uploads.huggingface.co/production/uploads/660ffb10d1eab2dd6da8a060/0AFXUPGhKtD3K7RHYT-Ek.png | |
| tags: | |
| - mcp-in-action-track-enterprise | |
| - mcp-in-action-track-consumer | |
| - mcp-in-action-track-creative | |
| # β¨ LifeFlow AI: Intelligent Trip Planning System | |
| <div align="center"> | |
| <img src="https://cdn-uploads.huggingface.co/production/uploads/660ffb10d1eab2dd6da8a060/0AFXUPGhKtD3K7RHYT-Ek.png" alt="LifeFlow AI Banner" width="55%" /> | |
| </div> | |
| <br> | |
| [](https://gradio.app/) | |
| [](https://github.com/agno-agi/agno) | |
| [](https://www.python.org/) | |
| [](https://www.docker.com/) | |
| [](LICENSE) | |
| > **Your journey, in perfect rhythm.** | |
| > An enterprise-grade, multi-agent system that orchestrates your daily schedule using real-world data, hybrid AI architecture, and mathematical optimization. | |
| --- | |
| ## π₯ Team | |
| ### Team Information: | |
| - **Man-Ho Li** - [@Marco310](https://huggingface.co/Marco310) - Lead Developer & AI Architect | |
| - **Chen-Yang Yu** - [@LittleFish-Coder](https://huggingface.co/LittleFish-Coder) - Technical Consultant & Marketing Director | |
| ## πΊ Demo & Submission | |
| ### π οΈ Technical Walkthrough | |
| > **See the "Dual-Brain" architecture in action.** Watch how LifeFlow transforms a vague prompt into a fully optimized itinerary. | |
| <div align="center"> | |
| <a href="https://www.youtube.com/watch?v=2P0q4jzUuvE" target="_blank"> | |
| <img src="https://img.youtube.com/vi/2P0q4jzUuvE/maxresdefault.jpg" alt="Watch LifeFlow AI Demo" width="55%" /> | |
| </a> | |
| <br> | |
| <p style="color: #888; font-size: 0.9em; margin-top: 10px; margin-bottom: 0px;"> | |
| <i>Click the image or the button below to watch the full demo</i> | |
| </p> | |
| <a href="https://www.youtube.com/watch?v=2P0q4jzUuvE"> | |
| <img src="https://img.shields.io/badge/βΆ_Watch_on_YouTube-FF0000?style=for-the-badge&logo=youtube&logoColor=white" alt="Watch on YouTube"> | |
| </a> | |
| </div> | |
| ### π¬ Product Trailer | |
| > *Experience the vision.* Watch our cinematic trailer showing the "One Man Army" journey. | |
| <br> | |
| π [**Watch Product Trailer**](https://youtu.be/VWtx70UgI5A?si=RryKCfptJGAHm_DY) | |
| ### π Submission Details | |
| * **Submission Date:** November 2025 | |
| * **Social Media Post:** [π View on LinkedIn](https://www.linkedin.com/posts/chen-yang-yu_huggingface-hackathon-activity-7400902296886239232-76vQ?utm_source=social_share_send&utm_medium=member_desktop_web&rcm=ACoAAEff6RgBH-oFavqAmJ2ym_c-fD-cGJqszig) | |
| ## π Overview | |
| **LifeFlow AI** is not just a chatbot; it's a **State Machine for Real-World Operations**. It solves the complexity of daily travel planningβconsidering traffic, weather, opening hours, and route optimizationβby coordinating a team of specialized AI agents. | |
| Unlike traditional AI planners that hallucinate locations, LifeFlow grounds every decision in **Real-Time Data** (Google Maps & OpenWeather) and uses **Mathematical Optimization** (TSP/OR-Tools) for routing. | |
| ### π Special Access for Reviewers | |
| To ensure a smooth testing experience during the **15-day review period**, we have **pre-configured default API keys** in the environment. | |
| * **Zero Setup:** You do NOT need to provide your own API keys for Google Maps, OpenWeather, or LLMs (Gemini-free tier, Groq-free). | |
| * **Ready to Run:** Simply interact with the UI; the backend is fully authenticated. | |
| * **Note:** These promotional keys have usage quotas and will be rotated after the review phase. | |
| ## π Key Innovation: Hybrid AI Architecture | |
| We solve the "Trilemma" of AI Agents: **Cost vs. Speed vs. Intelligence**. | |
| ### 1. Dual-Brain System π§ + β‘ | |
| Instead of using one expensive model for everything, LifeFlow uses a tiered approach: | |
| * **Primary Brain (The Leader):** Uses high-reasoning models (e.g., **GPT-5, Gemini 2.5 Pro**) for complex intent understanding, team orchestration, and final report generation. | |
| * **Acceleration Layer (The Muscle):** Uses ultra-fast, low-cost models (e.g., **Groq/Llama-3, Qwen 2.5, Gemini Flash-lite, GPT mini**) for high-volume tool execution (searching POIs, checking weather). | |
| ### 2. Context-Offloading Protocol π | |
| Traditional agents paste massive JSON search results into the chat context, burning thousands of tokens. | |
| * **LifeFlow's Approach:** Agents treat data like "Hot Potatoes." | |
| * **Mechanism:** Raw data (reviews, photos, coordinates) is offloaded to a structured database immediately. Agents only pass **Reference IDs** (e.g., `scout_result_123`) to the next agent. | |
| * **Result:** Token consumption reduced by **75%** (from ~80k to ~20k per run). | |
| --- | |
| ## π€ The Agent Team | |
| LifeFlow orchestrates 6 specialized agents working in a strict pipeline: | |
| 1. **π Planner:** Analyzes vague user requests (e.g., "I need to buy coffee and visit the bank") and converts them into structured JSON tasks. | |
| 2. **π¨ββοΈ Team Leader:** The State Machine orchestrator. Enforces SOPs and handles error recovery. | |
| 3. **πΊοΈ Scout (Fast Mode):** Interacts with Google Places API to verify locations and retrieve coordinates. | |
| 4. **β‘ Optimizer (Fast Mode):** Uses routing algorithms to solve the *Traveling Salesperson Problem (TSP)* with time windows. | |
| 5. **π§ Navigator (Fast Mode):** Calculates precise traffic impacts and generates polyline routes. | |
| 6. **π€οΈ Weatherman (Fast Mode):** Checks hyper-local weather forecasts for specific arrival times. | |
| 7. **π Presenter:** Compiles all data (from the DB) into a human-readable, formatted report. | |
| --- | |
| ## π οΈ Features | |
| * **BYOK (Bring Your Own Key):** Secure client-side key management for Google Maps, OpenWeather, and LLMs. | |
| * **Zero-Cost Validation:** Smart API testing mechanism that checks key validity without incurring charges. | |
| * **Interactive Map:** Visualizes routes, stops, and alternative POIs using Folium. | |
| * **Graceful Cancellation:** Cooperative signal handling to terminate background agents instantly. | |
| * **Reactive UI:** Modern Gradio interface with real-time streaming and responsive layouts. | |
| --- | |
| ## βοΈ Configuration | |
| LifeFlow AI allows deep customization via the **Settings** panel: | |
| ### Supported Providers | |
| * **Google Gemini:** 2.5 Pro, 2.5 Flash, 2.0 Flash. | |
| * **OpenAI:** GPT-5, GPT-5-mini, GPT-4o-mini. | |
| * **Groq:** Llama 3.3 70B, GPT-OSS 120B, Kimi K2. | |
| ### Fast Mode (Hybrid) | |
| Enable **Fast Mode** in settings to offload search and routing tasks to Groq. | |
| This significantly reduces latency and API costs while maintaining high-quality reasoning for the final output. | |
| --- | |
| ## π οΈ Tech Stack & Components | |
| | Component | Technology | Description | | |
| | :--- |:--------------------------------------------------------------| :--- | | |
| | **Frontend** | [Gradio](https://www.gradio.app/) | Reactive UI with custom CSS, Stepper, and Map integration. | | |
| | **Orchestration** | [Agno](https://github.com/agno-agi/agno) | Manages Agent state, memory, and team coordination. | | |
| | **Tool Protocol** | [FastMCP](https://github.com/jlowin/fastmcp) | Exposes Python functions as standardized AI tools. | | |
| | **Models** | [Google Gemini](https://ai.google.dev/) | **Flash** for Leader/Planner (Reasoning), **Flash-Lite** for Workers (Speed). | | |
| | **Optimization** | [Google OR-Tools](https://developers.google.com/optimization) | Solves TSPTW (Traveling Salesperson Problem with Time Windows). | | |
| | **Data Layer** | [SQLite](https://www.sqlite.org/index.html) | Local file-based repository for high-speed context offloading. | | |
| --- | |
| ## π» Local Installation | |
| To run LifeFlow AI locally, follow these steps: | |
| ### Prerequisites | |
| - Python 3.11+ | |
| - Virtualenv (recommended) | |
| ### Installation Steps | |
| 1. **Clone the repository** | |
| ```bash | |
| git clone [https://huggingface.co/spaces/MCP-1st-Birthday/LifeFlow-AI](https://huggingface.co/spaces/MCP-1st-Birthday/LifeFlow-AI) | |
| cd LifeFlow-AI | |
| ``` | |
| 2. Create and activate a virtual environment | |
| ```bash | |
| # Linux/MacOS | |
| python -m venv venv | |
| source venv/bin/activate | |
| # Windows | |
| python -m venv venv | |
| venv\Scripts\activate | |
| ``` | |
| 3. Install dependencies | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| 4. Set up Environment Variables Create a .env file in the root directory (optional if using UI for keys): | |
| ```env | |
| # Search API | |
| GOOGLE_MAPS_API_KEY=your_key_here | |
| OPENWEATHER_API_KEY=your_key_here | |
| # MODEL API | |
| GOOGLE_API_KEY=your_key_here | |
| OPENAI_API_KEY=your_key_here | |
| GROQ_API_KEY=your_key_here | |
| ``` | |
| 5. Run the application | |
| ```bash | |
| python app.py | |
| ``` | |
| ## π Documentation | |
| Explore the technical details behind LifeFlow AI: | |
| - [**ποΈ Architecture Design**](https://huggingface.co/spaces/MCP-1st-Birthday/LifeFlow-AI/blob/main/doc/ARCHITECTURE.md) | |
| > Deep dive into the **"Dual-Brain" system**, **Context Offloading**, and **Exclusive MCP Channels**. | |
| - [**π€ The Agent Team**](https://huggingface.co/spaces/MCP-1st-Birthday/LifeFlow-AI/blob/main/doc/AGENTS.md) | |
| > Detailed roster of our 6 specialized agents, their models, tools, and prompt strategies. | |
| - [**π§ Troubleshooting Guide**](https://huggingface.co/spaces/MCP-1st-Birthday/LifeFlow-AI/blob/main/doc/TROUBLESHOOTING.md) | |
| > Solutions for common API errors (Google/OpenWeather), JSON parsing issues, and Docker setup. | |
| ## π Acknowledgements | |
| A huge thank you to the tools and frameworks that made LifeFlow AI possible: | |
| * **[Agno](https://github.com/agno-agi/agno)** (formerly Phidata): For providing the robust Agentic framework that powers our Scout, Weatherman, and Optimizer agents. | |
| * **[Google Maps Platform](https://developers.google.com/maps)**: For the comprehensive Places and Routes APIs that ground our agents in real-world data. | |
| * **[Google OR-Tools](https://developers.google.com/optimization)**: For the powerful routing optimization algorithms. | |
| * **[Gradio](https://gradio.app/)**: For enabling the rapid development of our interactive UI. | |
| * **[MCP-1st-Birthday](https://huggingface.co/MCP-1st-Birthday)**: For the inspiration! | |
| ## β οΈ Disclaimer | |
| This application uses the **Google Maps Platform API**, which is a paid service. | |
| * **Cost:** Users are responsible for any costs incurred by their API usage. | |
| * **Optimization:** While LifeFlow AI is designed to be efficient, running multiple agents can consume API quotas quickly. | |
| * **Advice:** We strongly recommend setting up **Budgets & Alerts** and **Quotas** in your Google Cloud Console before running this app extensively. | |
| ## π License | |
| This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. | |