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Give it a destination, dates, budget, and preferences — it returns a complete travel plan with destination research, budget breakdown, day-wise itinerary, and a validation summary.\n\n---\n\n## Project Overview\n\nThe planner uses **4 specialised AI agents** that work sequentially, each passing their output to the next:\n\n| Agent | Role | Tools Used |\n|---|---|---|\n| Destination Researcher | Finds attractions, culture, tips | Serper Web Search |\n| Budget Planner | Estimates costs per category | Serper Web Search |\n| Itinerary Designer | Builds day-by-day plan | LLM only (uses prior context) |\n| Validation Agent | Checks consistency & feasibility | LLM only (reviews all outputs) |\n\n**Input:** Destination, start date, end date, budget (USD), preferences (optional)\n\n**Output:** A structured Markdown file saved to `/output/` containing:\n- Destination overview\n- Budget breakdown (accommodation, food, transport, activities)\n- Day-wise itinerary (morning / afternoon / evening)\n- Validation summary (PASS/WARN/FAIL checks, risks, assumptions)\n\n---\n\n## Run the Project With Only One Command\n**What You Need: An api key from https://serper.dev/api-keys, the LLM Model name and the API_KEY.**\n\n**1. Clone the Project**\n```bash\ngit clone <your-repository-url>\ncd CrewAI-Travel-Planner\n```\n\n**2. Then create a .env file and add the LLM MODEL and API_KEYS**\n\n**Example:**\n```ini\nMODEL=groq/meta-llama/llama-4-scout-17b-16e-instruct\nGROQ_API_KEY=gsk_95PDP7Agkwrsf------b3FYNSrgNtsabEPu8ipKM0hdWbPz\nSERPER_API_KEY=295c2-------87790b833cd6d9f151eea117\n```\n\n**You also need to add your LLM Model in `crew.py` file, `line: 33`**\n```python\ndef _get_llm() -> LLM:\n    \"\"\"\n    CrewAI LLM pointed at Groq via LiteLLM.\n    \"\"\"\n    api_key = os.getenv(\"GROQ_API_KEY\", \"\")\n    if not api_key:\n        log.error(\"GROQ_API_KEY is not set.\")\n        raise EnvironmentError(\n            \"GROQ_API_KEY is missing. Add it to the .env file\"\n        )\n    return LLM(\n        model=\"groq/meta-llama/llama-4-scout-17b-16e-instruct\",\n        api_key=api_key,\n        temperature=0.3,\n    )\n```\n**Add your model name here:**\n```\nmodel=\"groq/meta-llama/llama-4-scout-17b-16e-instruct\",\n```\n**3. Then run the following command in the terminal:**\n```bash\n./run.sh\n```\n**You should see the project running and asking for user input**\n\n<img width=\"731\" height=\"251\" alt=\"run\" src=\"https://github.com/user-attachments/assets/c76da812-3771-47d9-a5ff-3226508cadf3\" />\n\n---\n\n> **You may see an error saying `litellm[proxy]` is not installed, you can ignore this error as the project runs successfully without it.**\n\n---\n\n## Project Installation in the Typical Manner:\n\n## Prerequisites\n\nBefore you begin, make sure you have the following installed on your system.\n\n### 1. Python 3.10 or higher\n\n```bash\npython3 --version\n```\n\nIf not installed, download from [python.org](https://www.python.org/downloads/).\n\n### 2. CrewAI\n\n```bash\npip install crewai\n```\n\nVerify installation:\n\n```bash\ncrewai --version\n```\n\n> **Note:** CrewAI uses `uv` internally to manage the project virtual environment. It will be installed automatically when you run `crewai install`.\n\n\n##  Installation\n\n### Step 1 — Clone the repository\n\n```bash\ngit clone <your-repository-url>\ncd CrewAI-Travel-Planner\n```\n\n### Step 2 - Create a .env file in the project root and add your api keys and llm model name:\n```ini\nMODEL=groq/meta-llama/llama-4-scout-17b-16e-instruct\nGROQ_API_KEY=gsk_95PDP7Agkwrsf------b3FYNSrgNtsabEPu8ipKM0hdWbPz\nSERPER_API_KEY=295c2-------87790b833cd6d9f151eea117\n```\n**Also update the model name in the `crew.py` file:**\n```\nmodel=\"groq/meta-llama/llama-4-scout-17b-16e-instruct\",\n```\n\n### Step 3 — Install dependencies\n\n```bash\ncrewai install\n```\n\n### Step 4 - Run the following command in your terminal\n```\ncrewai run\n```\n**You should see the project running and asking for user input**\n\n---\n\n\n## Running the Project\n\n### Run the planner\n\n```bash\ncrewai run\n```\n\nYou will be prompted to enter your trip details:\n\n```\n═══════════════════════════════════════════════════════\n    AI Travel Planner \n═══════════════════════════════════════════════════════\n\nDestination (city / country): Tokyo, Japan\nStart date (YYYY-MM-DD): 2025-06-10\nEnd date   (YYYY-MM-DD): 2025-06-17\nTotal budget in USD (e.g. 2000): 3000\nPreferences (optional — e.g. vegetarian, no crowds): vegetarian\n```\n\nAfter confirming, the agents will start working. This typically takes **3–8 minutes** depending on the destination and number of days.\n\n```\n┌──────────────────────────────────────────────────┐\n│  Trip Summary                                    │\n│  Destination : Tokyo, Japan                      │\n│  Dates       : 2025-06-10 → 2025-06-17           │\n│  Duration    : 7 days                            │\n│  Budget      : $3,000.00 USD                     │\n│  Preferences : vegetarian                        │\n└──────────────────────────────────────────────────┘\n\n  ▶  Start planning? (y/n): y\n\n  🚀  Starting AI agents... (this may take a few minutes)\n```\n\nWhen complete:\n\n```\n═══════════════════════════════════════════════════════\n  ✅  Travel plan generated successfully!\n  📄  Saved to: output/travel_plan_tokyo_japan_20250610_143022.md\n═══════════════════════════════════════════════════════\n```\n\n---\n\n## 📄 Sample Output\n\nThe generated Markdown file in `/output/` will look like:\n\n```\n#  Travel Plan: Tokyo, Japan\n\n##  Trip Overview\n| Field       | Details       |\n|-------------|---------------|\n| Destination | Tokyo, Japan  |\n| Duration    | 7 days        |\n| Budget      | $3,000.00 USD |\n\n##  Destination Research\nTop attractions, local culture, practical tips, best areas to stay...\n\n##  Budget Breakdown\n| Category      | Cost      |\n|---------------|-----------|\n| Accommodation | $840.00   |\n| Food          | $350.00   |\n| Transport     | $200.00   |\n| Activities    | $300.00   |\n| Total         | $1,690.00 ✅ Within Budget |\n\n## 📅 Day-wise Itinerary\nDay 1 — Arrival & Shinjuku\n- Morning: Arrive at Narita, check in (~$0)\n- Afternoon: Explore Shinjuku Gyoen (~$5)\n- Evening: Dinner at local ramen restaurant (~$15)\n...\n\n## ✅ Validation Summary\n- Budget Alignment:        PASS\n- Scheduling Feasibility:  PASS\n- Consistency Check:       PASS\n- Overall Verdict:         APPROVED ✅\n```\n\n---\n\n##  Architecture\n\n```\nUser Input (CLI)\n      │\n      ▼\n┌─────────────────────────────────────────────────────┐\n│                    Crew Manager                     │\n│                                                     │\n│  Task 1: Destination Researcher  ── Serper API      │\n│       │                                             │\n│  Task 2: Budget Planner          ── Serper API      │\n│       │                                             │\n│  Task 3: Itinerary Designer      ── LLM only        │\n│       │                                             │\n│  Task 4: Validation Agent        ── LLM only        │\n└─────────────────────────────────────────────────────┘\n      │\n      ▼\noutput/travel_plan_<destination>_<timestamp>.md\n```\n\nEach task passes its output as context to the next task — no information is lost between agents.\n\n---\n\n##  Project Structure\n\n```\nCrewAI-Travel-Planner/\n│\n├── pyproject.toml                   # Project metadata and dependencies\n├── uv.lock                          # Locked dependency versions (commit this)\n├── .env                             # Your API keys (never commit this)\n├── .env.example                     # API key template\n├── .gitignore\n├── README.md\n│\n├── knowledge/                       # Reserved for CrewAI knowledge sources\n├── logs/                            # Auto-created — one timestamped .log per run\n├── output/                          # Auto-created — Markdown travel plans saved here\n│\n└── src/\n    └── travel_planner/\n        │\n        ├── __init__.py\n        ├── main.py                  # CLI prompts + calls run_travel_crew()\n        ├── crew.py                  # @agent / @task / @crew decorators + output writer\n        ├── logger.py                # Centralised logging (console + file)\n        │\n        ├── config/\n        │   ├── agents.yaml          # Agent definitions (role, goal, backstory)\n        │   └── tasks.yaml           # Task definitions (description, expected_output)\n        │\n        └── tools/\n            ├── __init__.py\n            ├── serper_tool.py       # Serper Dev API wrapper\n            └── calculator_tool.py   # Budget calculator utility\n```\n\n---\n\n## 📋 Logs\n\nEvery run creates a timestamped log file in `/logs/`:\n\n```bash\n# View the latest log\ncat logs/travel_planner_*.log\n\n# Follow a live run in real time\ntail -f logs/travel_planner_*.log\n```\n\nLog levels:\n- **Console** → INFO and above (clean progress messages)\n- **File** → DEBUG and above (full execution trace including every agent step and tool call)\n\n---\n\n## 🔧 Troubleshooting\n\n### `ModuleNotFoundError: No module named 'travel_planner'`\nYou are running `python3 main.py` directly. Always use `crewai run` instead:\n```bash\ncrewai run\n```\n\n### `Model decommissioned error`\nThe Groq model name is outdated. Open `src/travel_planner/crew.py` and update:\n```python\nmodel=\"groq/llama3-8b-8192\"           # ❌ old\nmodel=\"groq/llama-3.3-70b-versatile\"  # ✅ new\n```\nCheck currently available models at [console.groq.com/docs/models](https://console.groq.com/docs/models).\n\n### `Fallback to LiteLLM is not available`\nLiteLLM is missing from the virtual environment:\n```bash\nsource .venv/bin/activate\nuv pip install litellm --frozen\ncrewai run\n```\n\n### `GROQ_API_KEY is not set`\nYour `.env` file is missing or the key is not filled in:\n```bash\ncat .env\n```\nMake sure both keys have real values and not the placeholder text.\n\n### `ImportError: Missing dependency apscheduler`\nThis is a harmless warning from litellm's proxy module — your project does not use the proxy. The agents will still run correctly. 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