cursorrules

Project Overview This project, named GPT-Researcher, LLM based autonomous agent that conducts local and web research on any topic and generates a comprehensive report with citations, is built using Next.js and TypeScript. It integrates various libraries for their strenghts. Your primary goal is to help with Next.js app router patterns, TypeScript type safety, Tailwind CSS best practices, code quality standards, and Python/FastAPI backend optimizations. # Key URLs - Project Home Page: https://gptr.dev/ - GitHub Repository: https://github.com/assafelovic/gpt-researcher - Documentation: https://docs.gptr.dev/ # Project Structure - Frontend user interface built with Next.js, TypeScript, and Tailwind CSS in /frontend - Static FastAPI version for lightweight deployments - Next.js version for production use with enhanced features - Multi-agent research system using LangChain and LangGraph in /backend/multi_agents - Browser, Editor, Researcher, Reviewer, Revisor, Writer, and Publisher agents - Task configuration and agent coordination - Document processing using Unstructured and PyMuPDF in /backend/document_processing - PDF, DOCX, and web content parsing - Text extraction and preprocessing - Report generation using LangChain and Jinja2 templates in /backend/report_generation - Template-based report structuring - Dynamic content formatting - Multiple output formats in /backend/output_formats - PDF via md2pdf - Markdown via mistune - DOCX via python-docx - Format conversion utilities - Export functionality - GPT Researcher core functionality in /gpt_researcher - Web scraping and content aggregation - Research planning and execution - Source validation and tracking - Query processing and response generation - Testing infrastructure in /tests - Unit tests for individual components - Integration tests for agent interactions - End-to-end research workflow tests - Mock data and fixtures for testing # Language Model Configuration - Default model: gpt-4-turbo - Alternative models: gpt-3.5-turbo, claude-3-opus - Temperature settings for different tasks - Context window management - Token limit handling - Cost optimization strategies # Error Handling - Research failure recovery - API rate limiting - Network timeout handling - Invalid input management - Source validation errors - Report generation failures # Performance - Parallel processing strategies - Caching mechanisms - Memory management - Response streaming - Resource allocation - Query optimization # Development Workflow - Branch naming conventions - Commit message format - PR review process - Testing requirements - Documentation updates - Version control guidelines # API Documentation - REST endpoints - WebSocket events - Request/Response formats - Authentication methods - Rate limits - Error codes # Monitoring - Performance metrics - Error tracking - Usage statistics - Cost monitoring - Research quality metrics - User feedback tracking # Frontend Components - Static FastAPI version for lightweight deployments - Next.js version for production use with enhanced features # Backend Components - Multi-agent system architecture - Document processing pipeline - Report generation system - Output format handlers # Core Research Components - Web scraping and aggregation - Research planning and execution - Source validation - Query processing # Testing - Unit tests - Integration tests - End-to-end tests - Performance testing # Rule Violation Monitoring - Alert developer when changes conflict with project structure - Warn about deviations from coding standards - Flag unauthorized framework or library additions - Monitor for security and performance anti-patterns - Track API usage patterns that may violate guidelines - Report TypeScript strict mode violations - Identify accessibility compliance issues # Development Guidelines - Use TypeScript with strict mode enabled - Follow ESLint and Prettier configurations - Ensure components are responsive and accessible - Use Tailwind CSS for styling, following the project's design system - Minimize AI-generated comments, prefer self-documenting code - Follow React best practices and hooks guidelines - Validate all user inputs and API responses - Use existing components as reference implementations # Important Scripts - npm run dev: Start development server - npm run build: Build for production - npm run test: Run test suite - python -m pytest: Run Python tests - docker-compose up: Start all services - docker-compose run gpt-researcher-tests: Run test suite in container - python -m uvicorn backend.server.server:app --host=0.0.0.0 --port=8000: Start FastAPI server - python -m uvicorn backend.server.server:app --reload: Start FastAPI server with auto-reload for development - python main.py: Run the main application directly # AI Integration Guidelines - Prioritize type safety in all AI interactions - Follow LangChain and LangGraph best practices - Implement proper error handling for AI responses - Maintain context window limits - Handle rate limiting and API quotas - Validate AI outputs before processing - Log AI interactions for debugging # Lexicon - GPT Researcher: Autonomous research agent system - Multi-Agent System: Coordinated AI agents for research tasks - Research Pipeline: End-to-end research workflow - Agent Roles: Browser, Editor, Researcher, Reviewer, Revisor, Writer, Publisher - Source Validation: Verification of research sources - Report Generation: Process of creating final research output # Additional Resources - Next.js Documentation - TypeScript Handbook - Tailwind CSS Documentation - LangChain Documentation - FastAPI Documentation - Project Documentation End all your comments with a :-) symbol.