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**CrewAI** que analiza repositorios de GitHub automáticamente y genera un informe PDF completo con hallazgos de seguridad, rendimiento y calidad de código.\n\n---\n\n## ¿Cómo funciona?\n\nEl sistema orquesta cinco agentes especializados que trabajan en secuencia y en paralelo:\n\n```\nExplorador ──► Analista de Seguridad ─┐\n               Ingeniero de Rendimiento ├──► Documentador ──► PDF\n               Inspector de Calidad   ─┘\n```\n\n| Agente | Rol |\n|---|---|\n| **Explorador** | Clona el repo, mapea la estructura y asigna archivos a cada especialista |\n| **Analista de Seguridad** | Detecta vulnerabilidades OWASP, inyecciones, exposición de datos, etc. |\n| **Ingeniero de Rendimiento** | Identifica cuellos de botella, problemas N+1, fugas de memoria, etc. |\n| **Inspector de Calidad** | Revisa convenciones, principios SOLID, DRY, documentación, etc. |\n| **Documentador** | Consolida todos los hallazgos y genera el informe PDF |\n\nEl informe final incluye puntuación global, resumen ejecutivo, hallazgos categorizados con fragmentos de código y correcciones sugeridas, plan de acción priorizado y puntos positivos del proyecto.\n\n---\n\n## Requisitos previos\n\n- Python **3.10+**\n- Git instalado en el sistema\n- Token de GitHub (recomendado para evitar límites de la API)\n- Acceso a un LLM compatible con LiteLLM (OpenAI, Anthropic, Azure, etc.)\n\n---\n\n## Instalación\n\n### 1. Clona este repositorio\n\n```bash\ngit clone https://github.com/tu-usuario/code-review-crew.git\ncd code-review-crew\n```\n\n### 2. Crea un entorno virtual\n\n```bash\npython -m venv .venv\nsource .venv/bin/activate        # Linux / macOS\n.venv\\Scripts\\activate           # Windows\n```\n\n### 3. Instala las dependencias\n\n```bash\npip install -r requirements.txt\n```\n\n### 4. Configura las variables de entorno\n\nCrea un archivo `.env` en la raíz del proyecto:\n\n```env\n# LLM\nAPI_BASE=https://api.openai.com/v1       # o la URL de tu proveedor\nAPI_KEY=sk-...                           # tu clave de API\n\n# GitHub (opcional pero recomendado)\nGITHUB_TOKEN=ghp_...\n```\n\nEl `API_BASE` y `API_KEY` se pasan directamente a LiteLLM, por lo que son compatibles con cualquier proveedor soportado (OpenAI, Azure OpenAI, Anthropic, Ollama, etc.).\n\nSi no configuras `GITHUB_TOKEN`, la herramienta de metadatos funcionará con límites de rate más bajos de la API pública de GitHub.\n\n---\n\n## Uso\n\n```bash\npython main.py <url-del-repositorio>\n```\n\n### Ejemplos\n\n```bash\npython main.py https://github.com/pallets/flask\npython main.py https://github.com/psf/requests\npython main.py https://github.com/tu-usuario/tu-repo\n```\n\nEl informe PDF se guarda automáticamente en:\n\n```\nreports/review_<nombre-del-repo>.pdf\n```\n\n---\n\n## Estructura del proyecto\n\n```\ncode-review-crew/\n├── main.py                  # Punto de entrada\n├── crew.py                  # Orquestación de agentes y tareas\n├── agents.py                # Definición de los agentes\n├── tasks.py                 # Definición de las tareas\n├── requirements.txt\n├── .env                     # Variables de entorno (no subir a git)\n│\n├── config/\n│   ├── agents.yaml          # Roles, objetivos y backstories de los agentes\n│   └── tasks.yaml           # Descripciones y outputs esperados de cada tarea\n│\n├── tools/\n│   ├── gitHub_tools.py      # Herramientas de clonado y lectura de repos\n│   └── pdf_tool.py          # Generador del informe PDF con ReportLab\n│\n└── reports/                 # PDFs generados (se crea automáticamente)\n```\n\n> **Importante:** los archivos `agents.yaml` y `tasks.yaml` deben estar dentro de una carpeta `config/`. Asegúrate de que la estructura de directorios sea correcta antes de ejecutar.\n\n---\n\n## Personalización\n\n### Cambiar el modelo LLM\n\nEn `agents.py`, modifica el bloque `LLM`:\n\n```python\nself.llm = LLM(\n    model=\"openai/gpt-4o\",         # cualquier modelo soportado por LiteLLM\n    base_url=os.getenv(\"API_BASE\"),\n    api_key=os.getenv(\"API_KEY\"),\n    temperature=0.2,\n)\n```\n\n### Ajustar los límites de archivos analizados\n\nEn `config/tasks.yaml`, cada tarea tiene una instrucción `Máximo 15 archivos por especialista`. Puedes aumentarlo o reducirlo según tu presupuesto de tokens.\n\n### Extensiones de código analizadas\n\nEn `tools/gitHub_tools.py`, la variable `CODE_EXTENSIONS` define qué tipos de archivo se incluyen en el árbol. Añade o elimina extensiones según el stack de tu proyecto.\n\n---\n\n## Dependencias principales\n\n| Paquete | Uso |\n|---|---|\n| `crewai` | Framework multi-agente |\n| `litellm` | Capa de abstracción para LLMs |\n| `PyGithub` | API de GitHub (metadatos) |\n| `reportlab` | Generación del PDF |\n| `python-dotenv` | Carga de variables de entorno |\n| `pyyaml` | Lectura de configuración YAML |\n\n---\n\n## Limitaciones conocidas\n\n- El análisis depende de la calidad del LLM configurado. Modelos más potentes producen hallazgos más precisos.\n- Repositorios muy grandes (>500 archivos de código) pueden exceder los límites de contexto. 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