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✈️ AI-Powered Trip Planner\n\nAn intelligent travel planning app built with **CrewAI**, **Streamlit**, and **Ollama LLM**, designed to generate personalized travel itineraries based on user preferences.\n\n---\n\n## 🚀 Features\n\n- 🧠 **AI-Powered Planning**: Multi-agent system (Location, Guide, Planner Experts) for holistic travel insights  \n- 🗺️ **Comprehensive Itineraries**: Covers accommodations, transport, food, events, and budgeting  \n- 🌐 **Live Web Search**: DuckDuckGo integration for real-time travel data  \n- 🖥️ **Streamlit Interface**: Intuitive UI for entering travel details  \n- 📄 **Downloadable Plans**: Export itineraries as text files  \n- 🌍 **Multi-language Support**: French responses for Francophone destinations  \n\n---\n\n## 🧩 Architecture\n\nThree specialized AI agents collaborate to deliver a complete travel plan:\n\n| Agent            | Role                                                                 |\n|------------------|----------------------------------------------------------------------|\n| 🏨 Location Expert | Manages logistics: visas, weather, transport, accommodation, costs |\n| 🎯 Guide Expert    | Recommends attractions, food, and activities based on interests     |\n| 📅 Planner Expert  | Compiles all data into a structured, day-by-day itinerary           |\n\n---\n\n## ⚙️ Installation\n\n### 📋 Prerequisites\n\n- Python 3.8+  \n- Ollama installed and running locally  \n- Llama 3.2 model pulled via:  \n  ```bash\n  ollama pull llama3.2\n\n### Setup\n\n- Clone the repo\n```\n   git clone https://github.com/BrijeshRakhasiya/Trip-Planner-Agent.git\n```\n-  Install dependencies\n```\n   pip install -r requirements.txt\n```\n-  Start Ollama\n```\n   ollama serve\n   ollama pull llama3.2\n```\n## Usage\n\n1. Run the Streamlit application:\n   ```bash\n   streamlit run app.py\n   ```\n\n2. Open your browser to the provided local URL (typically http://localhost:8501)\n\n3. Fill in the travel details:\n   - From City\n   - Destination City\n   - Departure Date\n   - Return Date\n   - Interests (e.g., sightseeing, food, adventure)\n\n4. Click \"Generate Travel Plan\" and wait for the AI to create your personalized itinerary\n\n5. Download the travel plan as a text file\n\n## Dependencies\n\n- `crewai`: Multi-agent AI framework\n- `crewai_tools`: Additional tools for CrewAI\n- `langchain`: LLM framework integration\n- `langchain_community`: Community tools for LangChain\n- `langchain-ollama`: Ollama integration for LangChain\n- `duckduckgo-search`: Web search functionality\n- `langchain-google-genai`: Google Generative AI integration (optional)\n- `streamlit`: Web application framework\n\n## Project Structure\n\n```\n├── app.py                 # Main Streamlit application\n├── TravelAgents.py        # AI agent definitions\n├── TravelTasks.py         # Task definitions for agents\n├── TravelTools.py         # Custom tools (web search)\n├── requirements.txt       # Python dependencies\n├── output/                # Generated travel plans\n│   └── Travel_Plan_Rome.txt  # Sample output\n├── git_assets/            # UI screenshots\n│   ├── 1.png\n│   ├── 2.png\n│   └── 3.png\n└── __pycache__/           # Python bytecode cache\n```\n\n## Screenshots\n\n### Main Interface\n![Main Interface](git_assests/1.png)\n\n### Travel Plan Generation\n![Plan Generation](git_assests/2.png)\n\n### Sample Output\n![Sample Output](git_assests/3.png)\n\n## Sample Output\n\nSee `output/Travel_Plan_Rome.txt` for a sample travel plan generated for Rome, focusing on accommodation recommendations.\n\n## Technical Details\n\n- **LLM**: Uses Ollama with Llama 3.2 model running locally\n- **Process**: Sequential agent execution for comprehensive planning\n- **Tools**: DuckDuckGo web search for real-time information\n- **Output Format**: Markdown-structured travel itineraries\n- **Language**: Python 3.x with async capabilities\n\n## Configuration\n\nThe application uses the following configurations:\n- Max iterations per agent: 5\n- Verbose logging: Enabled\n- Full output: Enabled\n- Delegation: Disabled (agents work independently)\n\n## 🧯 Troubleshooting\n\n- Ensure Ollama is running before starting the application\n- Check that the Llama 3.2 model is downloaded\n- Verify all dependencies are installed\n- 🌐 For web search issues, ensure internet connectivity\n\n## 🌟 Future Enhancements\n\n- Support for multiple LLMs\n- Integration with booking APIs\n- Multi-language interface\n- Real-time flight/hotel pricing\n\n## 📄 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## 🙋‍♂️ Author\n\n**Brijesh Rakhasiya**  \nAI/ML Engineer · Data Scientist · Problem Solver\n\n---\n\n**👨‍💻 Developed by Brijesh Rakhasiya**\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbrijeshrakhasiya%2Ftrip-planner-agent","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbrijeshrakhasiya%2Ftrip-planner-agent","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbrijeshrakhasiya%2Ftrip-planner-agent/lists"}