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Travel Plan: `<Destination>`\n2. Destination Overview\n3. Budget Breakdown\n4. Day-wise Itinerary\n5. Validation Summary\n\n## Project Structure\n\n```\ntravel-planner-crewai/\n├── src/\n│   └── travel_planner_crewai/\n│       ├── __init__.py\n│       ├── crew.py\n│       ├── main.py\n│       ├── config/\n│       │   ├── agents.yaml\n│       │   └── tasks.yaml\n│       └── tools/\n│           ├── __init__.py\n│           └── custom_tool.py\n├── pyproject.toml\n├── report.md\n└── README.md\n```\n\n## Setup\n\nPython requirement: `>=3.10,<3.14`\n\nClone the repository:\n\n```bash\ngit clone https://github.com/Mubashirul-Islam/travel-planner-crewai.git\ncd travel-planner-crewai\n```\n\nInstall dependencies:\n\n```bash\ncrewai install\n```\n\nSet environment variables:\n\n```bash\nexport GROQ_API_KEY=\"your_groq_api_key\"\nexport SERPER_API_KEY=\"your_serper_api_key\"\nexport GROQ_MODEL=\"llama-3.3-70b-versatile\"  # optional\n```\n\n## Run\n\n```bash\ncrewai run\n```\n\nThe CLI prompts for destination, travel dates, budget, and optional preferences.\n\n## Analysis\n\n**Why multi-agent?** Each agent focuses on a single responsibility (research, budgeting, itinerary design, validation) with its own tools and prompt. This improves accuracy compared to a single LLM call and keeps each context window small and task-specific.\n\n**What if Serper returns incorrect data?** Serper results are not guaranteed to be accurate. The Validation Agent flags unverifiable claims as assumptions, but incorrect data can still propagate; users should verify critical details before travel.\n\n**What if budget is unrealistic?** The Budget Planner explicitly flags any shortfall rather than forcing a plan to fit. The Validation Agent surfaces budget warnings in the final summary.\n\n**Hallucination risks?** LLMs can fabricate plausible details. Grounding via live Serper results and a dedicated Validation Agent reduce this, but cannot eliminate it entirely. Unverified data points are marked as assumptions in the report.\n\n**Token usage?** Per-agent prompts keep each call lightweight, but context accumulates across the workflow. 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