OpenDiscourse MCP

# AI Code Review Suite This document describes the comprehensive AI-powered code review system integrated into the OpenDiscourse MCP project. ## Overview The AI Code Review Suite provides automated, multi-agent code analysis using CrewAI and specialized AI agents. Each agent focuses on specific aspects of code quality, security, and maintainability. ## Architecture ### Multi-Agent System The system uses CrewAI to coordinate specialized AI agents: - **Security Reviewer**: Vulnerability assessment and security analysis - **Performance Reviewer**: Bottleneck identification and optimization - **Style Reviewer**: Code style and maintainability standards - **Testing Reviewer**: Test coverage and quality assessment - **Architecture Reviewer**: Design patterns and structural analysis - **Dependency Reviewer**: Package management and license compliance - **Accessibility Reviewer**: WCAG compliance and user experience - **Documentation Reviewer**: Documentation quality and completeness ### Observability Integration All AI agents are integrated with Langfuse for: - **Tracing**: Complete execution trace visibility - **Monitoring**: Performance metrics and success rates - **Debugging**: Detailed error analysis and troubleshooting - **Analytics**: Usage patterns and improvement insights ## Available Review Scripts ### Individual Review Scripts ```bash # Security-focused review python3 scripts/security_review_crew.py # Performance optimization review python3 scripts/performance_review_crew.py # Comprehensive quality review (5 agents) python3 scripts/quality_review_crew.py # Documentation quality review python3 scripts/documentation_review_crew.py # Original comprehensive review (4 agents) python3 scripts/code_review_crew.py ``` ### GitHub Actions Integration The AI Code Review Suite is integrated into GitHub Actions with configurable review types: ```yaml # Trigger specific review types - security: Security vulnerability assessment - performance: Performance bottleneck analysis - quality: Code style, testing, architecture, dependencies, accessibility - documentation: Documentation quality and completeness - all: Complete comprehensive review ``` ## Configuration ### Environment Variables Required for AI agents: ```bash OPENAI_API_KEY=your_openai_api_key LANGFUSE_PUBLIC_KEY=your_langfuse_public_key LANGFUSE_SECRET_KEY=your_langfuse_secret_key LANGFUSE_HOST=your_langfuse_host ``` ### Agent Configuration Each agent can be customized in their respective script: ```python # Example agent configuration agent = Agent( role="Security Code Reviewer", goal="Identify potential security vulnerabilities", backstory="Expert cybersecurity analyst...", llm=ChatOpenAI(model="gpt-4", temperature=0.1), tools=[code_search, dir_search, file_read], verbose=True ) ``` ## Review Focus Areas ### Security Review - **Vulnerability Assessment**: SQL injection, XSS, authentication issues - **Security Best Practices**: Input validation, encryption, access control - **Dependency Security**: Known vulnerabilities in third-party packages - **Data Protection**: Sensitive data handling and storage ### Performance Review - **Algorithmic Complexity**: Big O analysis and optimization opportunities - **Memory Management**: Memory leaks and inefficient usage patterns - **Database Optimization**: Query performance and indexing - **Concurrency Issues**: Race conditions and threading problems ### Quality Review - **Code Style**: PEP 8 compliance and naming conventions - **Testing Coverage**: Unit tests, integration tests, edge cases - **Architecture**: Design patterns, separation of concerns, scalability - **Dependencies**: Package versions, license compatibility, security updates - **Accessibility**: WCAG compliance and inclusive design ### Documentation Review - **API Documentation**: Complete and accurate API references - **Code Comments**: Clear inline documentation and explanations - **README Quality**: Comprehensive setup and usage instructions - **Examples**: Working code examples and use cases ## Output Format Each review generates comprehensive reports including: ### Structure ```python { "vulnerabilities": [ { "severity": "high|medium|low", "location": "file:line", "description": "Issue description", "recommendation": "Fix suggestion" } ], "metrics": { "total_issues": 10, "critical_issues": 2, "performance_score": 85 }, "summary": "Overall assessment and priorities" } ``` ### Report Types - **Security Report**: Vulnerability findings with CVSS scores - **Performance Report**: Bottleneck analysis with optimization impact - **Quality Report**: Style violations and maintainability metrics - **Documentation Report**: Coverage gaps and quality assessments ## Best Practices ### Running Reviews 1. **Run Individual Reviews First**: Start with specific review types 2. **Review Output Carefully**: AI suggestions require human validation 3. **Iterative Improvement**: Address issues incrementally 4. **Track Progress**: Monitor improvement over time ### Integration Workflow 1. **Pre-commit Hooks**: Run style and basic security checks 2. **PR Reviews**: Automated comprehensive reviews 3. **Release Checks**: Full security and performance validation 4. **Periodic Audits**: Regular comprehensive assessments ### Customization - **Agent Prompts**: Tailor to project-specific requirements - **Review Criteria**: Adjust focus areas based on project needs - **Output Format**: Customize reporting for team workflows - **Integration**: Adapt to CI/CD pipeline requirements ## Troubleshooting ### Common Issues **API Rate Limits** ```bash # Add retry logic and exponential backoff # Monitor usage in Langfuse dashboard ``` **Agent Performance** ```bash # Adjust temperature settings for consistency # Refine prompts for better focus # Monitor token usage and costs ``` **Integration Problems** ```bash # Verify environment variables # Check Langfuse connectivity # Review GitHub Actions permissions ``` ### Monitoring Use Langfuse dashboard to monitor: - **Success Rates**: Agent completion percentages - **Performance Metrics**: Execution time and token usage - **Error Patterns**: Common failure modes and fixes - **Cost Analysis**: API usage and optimization opportunities ## Future Enhancements ### Planned Features - **Custom Agents**: Project-specific specialized reviewers - **Integration**: More CI/CD platform support - **Metrics**: Advanced analytics and trend analysis - **Automation**: Self-improving agent capabilities ### Extensibility The system is designed for easy extension: - Add new specialized agents - Customize existing agent behaviors - Integrate additional tools and data sources - Create custom review workflows ## Support For issues with the AI Code Review Suite: 1. Check Langfuse dashboard for detailed error information 2. Review agent logs and configuration 3. Verify API keys and permissions 4. Consult the troubleshooting guide above 5. Open an issue with detailed error information and logs