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Uses multiple AI agents to research destinations, manage travel logistics, and generate personalized itineraries. Demonstrates agentic AI, local LLMs, web search, and automated trip planning.\n# AI Trip Planner Agent (Multi-Agent, CrewAI + Ollama)\n\nA local, API-cost-free multi-agent travel planning system built with\n[CrewAI](https://github.com/crewAIInc/crewAI) and a locally-hosted LLM\n(Ollama). Three specialist AI agents collaborate to research and produce\na complete, personalized travel itinerary.\n\n## What it does\n\nGive it a departure city, a destination, travel dates, and your interests —\nit returns a full markdown itinerary, backed by three specialist agents:\n\n| Agent | Responsibility |\n|---|---|\n| **Travel Logistics Expert** | Visa requirements, transportation, cost of living, weather, local events |\n| **City Local Guide Expert** | Attractions, food, activities tailored to your interests |\n| **Travel Planning Expert** | Merges both reports into one day-by-day itinerary with budget tips |\n\nAll three agents share a live web-search tool (DuckDuckGo) so answers are\ngrounded in current information, not just model knowledge.\n\n## Why it's built this way\n\n- **Runs 100% locally** via Ollama — no OpenAI/Anthropic API key or per-call\n  cost required. Great for clients who want to avoid recurring API bills.\n- **Modular structure** — agents, tasks, tools, and orchestration are cleanly\n  separated, so any part (prompts, models, tools) can be swapped independently.\n- **Multi-agent context passing** — the planner agent receives the other two\n  agents' outputs as context, producing one coherent itinerary instead of\n  three disconnected reports.\n- **Production touches** — input validation, error handling with actionable\n  troubleshooting steps, and markdown reports saved to disk automatically.\n\n## Project structure\n\n```\n.\n├── agents.py         # Agent definitions (LLM, role, goal, backstory, tools)\n├── tasks.py          # Task definitions (prompts + expected output per agent)\n├── tools.py          # Shared web search tool\n├── main.py           # CLI entrypoint — wires agents + tasks into a Crew\n├── requirements.txt\n└── output/           # Generated markdown reports (created on first run)\n```\n\n## Setup\n\n1. **Install Ollama** and pull the model:\n   ```bash\n   ollama pull llama3.2\n   ollama serve\n   ```\n\n2. **Install Python dependencies:**\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n3. **Run it:**\n   ```bash\n   python main.py\n   ```\n\nYou'll be prompted for departure city, destination, dates, and interests.\nThe final itinerary prints to the console and is also saved as:\n\n- `output/city_report.md`\n- `output/guide_report.md`\n- `output/travel_plan.md`\n\n## Customization ideas for clients\n\n- Swap `ollama/llama3.2` for any other Ollama-hosted model in `agents.py`\n- Add a fourth agent (e.g. \"Budget Optimizer\" or \"Restaurant Booking Agent\")\n- Wrap `main.py`'s `run_trip_planner()` in a FastAPI endpoint or Streamlit UI\n- Swap the DuckDuckGo tool for a paid search API (Serper, Tavily) for\n  higher-quality results\n","readmeExcerpt":"AI-Trip-Planner AI-powered travel planner built with Python, CrewAI, Ollama, and Streamlit. 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