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Note: This is a readable copy of the .cursorrules file maintained for legibility. The actual rules are implemented from the .cursorrules file in the root directory.

GPT-Researcher Cursor Rules

Project Overview

This project, named GPT-Researcher, is an LLM-based autonomous agent that conducts local and web research on any topic and generates a comprehensive report with citations. It is built using Next.js and TypeScript, integrating various libraries for their strengths.

Your primary goal is to help with:

  • Next.js app router patterns
  • TypeScript type safety
  • Tailwind CSS best practices
  • Code quality standards
  • Python/FastAPI backend optimizations

Key URLs

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
  • 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
  • docker-compose up: Start all services
  • docker-compose run gpt-researcher-tests: Run test suite in container

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

Note: End all your comments with a :-) symbol.