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This system takes high-level natural language requirements and orchestrates a collaborative workflow between specialized AI agents to generate system architecture, functional backend code, interactive UIs, and comprehensive unit tests.\n\n## Architecture\n\nThe project leverages a multi-agent system where each agent is assigned a specific role and equipped with an optimized LLM to handle discrete phases of software development.\n\n```mermaid\ngraph TD\n    User[\"User (Provides Requirements)\"]\n    \n    subgraph \"🤖 Multi-Agent CrewAI System\"\n        direction TB\n        Lead[\"Engineering Lead<br/>(GPT-4o-mini)\"]\n        Backend[\"Backend Engineer<br/>(Llama-3.1)\"]\n        Frontend[\"Frontend Engineer<br/>(Gemini Flash)\"]\n        QA[\"Test Engineer<br/>(GPT-4o-mini)\"]\n    end\n    \n    subgraph \"📂 Output Directory\"\n        DesignDoc[\"System Design Document<br/>(*_design.md)\"]\n        BackendCode[\"Backend Logic<br/>(*.py)\"]\n        GradioUI[\"Interactive UI<br/>(app.py)\"]\n        Tests[\"Unit Tests<br/>(test_*.py)\"]\n    end\n\n    User -->|Main.py Requirements| Lead\n    \n    Lead -->|Designs Architecture| DesignDoc\n    Lead -->|Passes Design Context| Backend\n    \n    Backend -->|Writes Python Code| BackendCode\n    Backend -->|Passes Code Context| Frontend\n    Backend -->|Passes Code Context| QA\n    \n    Frontend -->|Builds Gradio Web App| GradioUI\n    QA -->|Generates Testing Suite| Tests\n```\n\n### Agent Roles\n\n1. **Engineering Lead (`gpt-4o-mini`)**: Analyzes the high-level requirements and produces a detailed technical design document (`*_design.md`).\n2. **Backend Engineer (`llama-3.1-8b-instant` via Groq)**: Implements the technical design into a complete, self-contained Python module.\n3. **Frontend Engineer (`gemini-2.5-flash-lite`)**: Builds an interactive Gradio web application (`app.py`) to demonstrate and interact with the backend logic.\n4. **Test Engineer (`gpt-4o-mini`)**: Writes comprehensive unit tests (`test_*.py`) to ensure the reliability and correctness of the generated backend module.\n\nAll generated artifacts are automatically saved to the `output/` directory.\n\n## Tech Stack\n\n- **Framework**: CrewAI\n- **LLMs**: OpenAI (GPT-4o-mini), Groq (Llama-3.1), Google GenAI (Gemini)\n- **UI & Tools**: Gradio, Python, uv (dependency management)\n\n## Installation & Setup\n\nEnsure you have Python >=3.10 and <3.14 installed on your system. This project uses [uv](https://docs.astral.sh/uv/) for fast dependency management.\n\n1. **Clone the repository:**\n   ```bash\n   git clone <your-repo-url>\n   cd team\n   ```\n\n2. **Install `uv` (if not already installed):**\n   ```bash\n   pip install uv\n   ```\n\n3. **Install dependencies:**\n   ```bash\n   crewai install\n   ```\n\n4. **Environment Variables:**\n   Create a `.env` file in the root directory and add the required API keys for the respective LLM providers:\n   ```env\n   OPENAI_API_KEY=your_openai_key\n   GROQ_API_KEY=your_groq_key\n   GEMINI_API_KEY=your_gemini_key\n   ```\n\n## Running the Project\n\nTo execute the multi-agent crew, run the following command from the root directory:\n\n```bash\ncrewai run\n```\n\nOr alternatively:\n\n```bash\npython -m team.main\n```\n\nBy default, the agents will read the requirements defined in `src/team/main.py` (e.g., building an inventory management system) and output the resulting architecture document, python classes, UI, and test files into the `output/` directory.\n\n## Customizing Requirements\n\nYou can change the software being built by updating the `requirements` variable inside `src/team/main.py`. 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