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This project shows the minimal\nuseful shape of that pattern: one agent that owns tools (URL construction +\nheadless browsing) and extracts structured facts, one agent that owns presentation,\nand a local LLM so nothing leaves your machine except the browser session.\n\n## Architecture\n\n```mermaid\nflowchart LR\n    U[Streamlit chat<br/>app.py] --> S[Flight Search Agent]\n    S -->|Kayak tool| K[kayak.py<br/>search URL]\n    S -->|Browserbase tool| B[browser_tool.py<br/>remote headless Chromium]\n    B -->|page text| S\n    S -->|top 5 flights| M[Summarisation Agent]\n    M -->|ranked summary| U\n    L[(Ollama<br/>deepseek-r1)] -.-> S & M\n```\n\n## Code map\n\n| File | Responsibility |\n| --- | --- |\n| `src/kayak.py` | Pure function turning airport codes + dates into a Kayak search URL |\n| `src/browser_tool.py` | CrewAI tool: opens a URL in a Browserbase remote Chromium session via Playwright CDP and returns the rendered page text |\n| `src/crew.py` | Defines the LLM (Ollama/DeepSeek-R1), both agents, both tasks, and the sequential crew |\n| `app.py` | Streamlit chat interface calling `find_flights()` |\n| `tests/test_kayak.py` | Unit tests for the URL builder |\n\n## Setup and run\n\n1. Install dependencies: `pip install -r requirements.txt`\n2. Serve the model locally: `ollama pull deepseek-r1 && ollama serve`\n3. Set browser credentials (placeholders — get them from your\n   [Browserbase](https://www.browserbase.com) dashboard):\n\n   ```bash\n   export BROWSERBASE_API_KEY=your-key-here\n   export BROWSERBASE_PROJECT_ID=your-project-id\n   ```\n\n4. 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