This repository has been split into focused, product-specific repositories.
See the new repositories:
codeflow-engine- Core engine (Python)codeflow-desktop- Desktop application (Tauri/React)codeflow-vscode-extension- VS Code extensioncodeflow-infrastructure- Production infrastructure (Bicep/Terraform)codeflow-azure-setup- Azure bootstrap scriptscodeflow-website- Marketing websiteOriginal monorepo history preserved at tag:
legacy-autopr-monorepo-final
AutoPR Engine is a comprehensive AI-powered automation platform that transforms GitHub pull request workflows through intelligent analysis, issue creation, and multi-agent collaboration.
📢 Repository Structure Update: The repository has been reorganized for better maintainability and developer experience. See Reorganization Summary for details on the improvements.
The AutoPR Engine repository is organized for optimal developer experience and maintainability:
.
├── docs/ # 📚 All documentation
│ ├── getting-started/ # Setup and quick start guides
│ ├── development/ # Development guides and tools
│ ├── architecture/ # System architecture documents
│ ├── plans/ # Project planning documents
│ └── README.md # Documentation index
├── configs/ # ⚙️ Configuration files
│ ├── platforms/ # Platform configurations
│ ├── workflows/ # Workflow configurations
│ ├── phases/ # Phase configurations
│ └── tasks/ # Task configurations
├── templates/ # 🎨 Template system
│ ├── platforms/ # Platform-specific templates
│ ├── use-cases/ # Use case templates
│ ├── deployment/ # Deployment templates
│ └── integrations/ # Integration templates
├── tools/ # 🔧 Development tools
│ ├── development/ # Development workflow tools
│ └── quality/ # Code quality tools
└── autopr/ # 🚀 Main source code
- Multi-Agent Review: CodeRabbit, GitHub Copilot, AI TypeScript Check integration
- Platform Detection: Detects 25+ development platforms (Replit, Bolt, Lovable, etc.)
- Intelligent Issue Classification: Security, performance, bugs, features
- Quality Gates: Automated validation before merge
- Communication: Slack (Axolo), Microsoft Teams, Discord, Notion
- Project Management: Linear, GitHub Issues, Jira
- AI Tools: AutoGen multi-agent, configurable LLM providers
- Monitoring: Sentry, DataDog, Prometheus metrics
- Issue Auto-Creation: GitHub Issues and Linear tickets
- AI Tool Assignment: Route issues to specialized AI tools
- Workflow Orchestration: 20+ pre-built workflows
- Memory System: Learn from past interactions and patterns
# Install from PyPI
pip install codeflow-engine
# Or install with all features
pip install "codeflow-engine[full]"
# Docker deployment (build locally)
docker build -t codeflow-engine:latest .
docker run -d \
-e GITHUB_TOKEN=your_token \
-e OPENAI_API_KEY=your_key \
-p 8080:8080 \
YOUR_DOCKERHUB_USERNAME/codeflow-engine:latest
# Docker Compose deployment (recommended for full setup)
# Create a .env file with your environment variables:
# GITHUB_TOKEN=your_token
# OPENAI_API_KEY=your_key
# Then run:
docker-compose up -d
# Publishing to GitHub Container Registry (GHCR)
# 1. Build the image:
docker build -t codeflow-engine:latest .
# 2. Tag for GHCR (replace YOUR_USERNAME with your GitHub username):
docker tag codeflow-engine:latest ghcr.io/YOUR_USERNAME/codeflow-engine:latest
# 3. Authenticate to GHCR:
docker login ghcr.io -u YOUR_USERNAME -p YOUR_GITHUB_TOKEN
# 4. Push to GHCR:
docker push ghcr.io/YOUR_USERNAME/codeflow-engine:latest
# 5. Run the published image:
docker run -d \
-e GITHUB_TOKEN=your_token \
-e OPENAI_API_KEY=your_key \
-p 8080:8080 \
ghcr.io/YOUR_USERNAME/codeflow-engine:latest
# Publishing to Docker Hub (alternative)
# 1. Build and tag for Docker Hub:
docker build -t YOUR_DOCKERHUB_USERNAME/codeflow-engine:latest .
# 2. Authenticate to Docker Hub:
docker login -u YOUR_DOCKERHUB_USERNAME -p YOUR_DOCKERHUB_TOKEN
# 3. Push to Docker Hub:
docker push YOUR_DOCKERHUB_USERNAME/codeflow-engine:latest
# 4. Run the published image:
docker run -d \
-e GITHUB_TOKEN=your_token \
-e OPENAI_API_KEY=your_key \
-p 8080:8080 \
YOUR_DOCKERHUB_USERNAME/codeflow-engine:latest# autopr_config.py
from autopr import AutoPREngine
# Initialize engine
engine = AutoPREngine({
'github_token': 'your_github_token',
'openai_api_key': 'your_openai_key',
'repositories': ['owner/repo'],
'integrations': {
'slack': {
'webhook_url': 'your_slack_webhook',
'channel': '#pr-reviews'
},
'linear': {
'api_key': 'your_linear_key',
'team_id': 'your_team_id'
}
}
})
# Start processing
await engine.start()# .github/workflows/autopr.yml
name: AutoPR Analysis
on:
pull_request:
types: [opened, synchronize]
jobs:
autopr:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: JustAGhosT/[email protected]
with:
github-token: ${{ secrets.GITHUB_TOKEN }}
openai-api-key: ${{ secrets.OPENAI_API_KEY }}
linear-api-key: ${{ secrets.LINEAR_API_KEY }}- Architecture Overview - System design and components
- Enhanced System Architecture - Comprehensive system design
- Quality Engine - AI-powered code analysis
- Development Guide - Development setup and tools
- Platform Detection - Platform identification system
- Windows Development - Windows development setup
- Code Quality - Code quality standards
- Configuration Management - Configuration system
- Python 3.13 Upgrade - Python upgrade guide
- Modular Refactoring - Refactoring documentation
- AutoWeave Integration - Integration guide
- Migration Guide - Migration from previous versions
graph TB
A[GitHub PR Event] --> B[AutoPR Engine]
B --> C[Platform Detector]
B --> D[AI Analysis Engine]
D --> E[CodeRabbit Integration]
D --> F[GitHub Copilot]
D --> G[AI TypeScript Check]
C --> H[Workflow Router]
H --> I[Issue Creator]
H --> J[AI Assignment]
I --> K[GitHub Issues]
I --> L[Linear Tickets]
J --> M[Communication Hub]
M --> N[Slack/Axolo]
M --> O[Teams/Discord]
subgraph "AI Layer"
P[AutoGen Multi-Agent]
Q[Configurable LLM]
R[Memory System]
end
subgraph "Quality Gates"
S[Security Scanning]
T[Performance Analysis]
U[Test Execution]
end
# Clone repository
git clone https://github.com/JustAGhosT/codeflow-engine.git
cd codeflow-engine # or 'cd autopr' if repository is cloned as 'autopr'
# Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install in development mode
pip install -e ".[dev]"
# Run tests
pytest
# Run with live reload
python -m autopr.server --reload.
├── autopr/ # Core engine package
│ ├── actions/ # Automation actions
│ ├── agents/ # Agent system
│ ├── ai/ # AI and LLM components
│ ├── cli/ # Command-line interface
│ ├── clients/ # External service clients
│ ├── config/ # Configuration management
│ ├── integrations/ # External service integrations
│ ├── quality/ # Quality gates and validation
│ ├── security/ # Security framework
│ ├── workflows/ # Pre-built workflow definitions
│ └── engine.py # Core engine
├── tests/ # Comprehensive test suite
├── docs/ # Documentation
│ ├── architecture/ # System architecture
│ ├── development/ # Development guides
│ ├── getting-started/ # Setup guides
│ └── plans/ # Project planning
├── configs/ # Configuration files
│ ├── platforms/ # Platform configurations
│ └── workflows/ # Workflow configurations
├── templates/ # Template system
│ ├── platforms/ # Platform-specific templates
│ ├── deployment/ # Deployment templates
│ └── use-cases/ # Use case templates
├── tools/ # Development tools
│ ├── development/ # Development workflow tools
│ └── quality/ # Code quality tools
├── scripts/ # Utility scripts
├── examples/ # Usage examples
├── setup.py # Package configuration
├── pyproject.toml # Modern Python packaging
├── Dockerfile # Container build
├── docker-compose.yml # Local development
└── README.md # This file
# Run all tests
pytest
# Run with coverage
pytest --cov=autopr --cov-report=html
# Run specific test categories
pytest tests/actions/ # Action tests
pytest tests/workflows/ # Workflow tests
pytest tests/integration/ # Integration tests
# Run performance tests
pytest tests/performance/ -v# Core Configuration
GITHUB_TOKEN=ghp_xxxxxxxxxxxx # GitHub API token
OPENAI_API_KEY=sk-xxxxxxxx # OpenAI API key
AUTOPR_LOG_LEVEL=INFO # Logging level
# Optional AI Providers
ANTHROPIC_API_KEY=sk-ant-xxxxxxxx # Claude API key
MISTRAL_API_KEY=xxxxxxxx # Mistral API key
GROQ_API_KEY=gsk_xxxxxxxx # Groq API key
# Integrations
SLACK_WEBHOOK_URL=https://hooks.slack.com/... # Slack notifications
LINEAR_API_KEY=lin_api_xxxxxxxx # Linear integration
AXOLO_WORKSPACE_URL=https://... # Axolo integration
# Infrastructure
REDIS_URL=redis://localhost:6379 # Caching and state
DATABASE_URL=postgresql://... # PostgreSQL database
SENTRY_DSN=https://... # Error tracking# autopr.yml
repositories:
- owner: JustAGhosT
repos: ["vv-landing", "vv-backend"]
workflows:
- name: "pr_review_analysis"
triggers: ["pull_request.opened", "pull_request.synchronize"]
actions:
- platform_detector
- ai_analysis
- issue_creator
- team_notification
integrations:
slack:
enabled: true
channels:
pr_reviews: "#pr-reviews"
security_alerts: "#security"
linear:
enabled: true
team_id: "VV"
project_templates:
security: "Security Issues"
performance: "Performance Optimization"
ai_providers:
default: "openai"
fallback_order: ["openai", "anthropic", "mistral"]
models:
openai: "gpt-4"
anthropic: "claude-3-sonnet-20240229"- Slack - Channel automation with Axolo
- Microsoft Teams - Enterprise communication
- Discord - Community-focused teams
- Notion - Documentation automation
- Linear - Modern issue tracking
- GitHub Issues - Native GitHub integration
- Jira - Enterprise project management
- CodeRabbit - AI code review
- GitHub Copilot - AI coding assistant
- AutoGen - Multi-agent collaboration
- Platform Detection - Supports 25+ development platforms including Replit, Bolt, Lovable, and more
- PR processing times and success rates
- AI tool performance and accuracy
- Issue creation and resolution tracking
- Integration health monitoring
- Prometheus metrics collection
- Grafana dashboards
- Sentry error tracking
- DataDog APM integration
- Custom webhooks for external systems
AutoPR Engine provides extensive documentation covering all aspects of the project:
- Comprehensive Project Analysis - Complete production-readiness assessment
- Business context and strategic goals
- Technology stack inventory (Python 3.12+, FastAPI, React/Tauri)
- 9 bugs, 9 UI/UX improvements, 9 performance enhancements identified
- 3 high-value feature proposals
- 5-phase implementation roadmap (15 weeks)
- Design System - Complete visual identity and component library
- Color palette with WCAG 2.1 AA compliance
- Typography hierarchy and spacing system
- Accessible component patterns
- Dark mode implementation guide
-
Troubleshooting Guide - Comprehensive problem-solving resource
- 9 common error scenarios with solutions
- Installation, configuration, and runtime issues
- Performance debugging and optimization
- Health check scripts and emergency contacts
-
Security Best Practices - Enterprise security guide
- Authentication and authorization
- Secret management with vault integration
- Secure deployment configurations
- API security and rate limiting
- Compliance (GDPR, SOC 2)
-
Implementation Summary - POC implementation details
- Critical security fixes (race conditions, input validation)
- Test coverage metrics (29 tests, 100% passing)
- Production hardening checklist
-
Completion Summary - Full project status
- All deliverables completed
- Success criteria met
- Next steps and recommendations
The following critical security improvements have been implemented:
- Issue: Concurrent access to workflow metrics without lock protection
- Solution: Async lock implementation for all metrics operations
- Testing: 8 comprehensive async tests validating thread safety
- Impact: Eliminated data corruption risk in production
- Issue: Missing validation allowed injection attacks
- Solution: Comprehensive validation module with Pydantic
- Security: Prevents SQL injection, XSS, command injection, path traversal
- Testing: 21 security tests covering attack scenarios
- Patterns: 12 suspicious patterns detected (script tags, eval, etc.)
- Issue: Path traversal vulnerability in dashboard
- Solution: Whitelist-based path validation with symlink prevention
- Testing: 17 security tests for common attack vectors
- Status: Verified secure implementation
Comprehensive test suite ensuring code quality and security:
# Run all tests
pytest
# Run security tests
pytest tests/test_workflow_validation.py
pytest tests/test_workflow_engine_critical.py
pytest tests/test_dashboard_security.py
# Check coverage
pytest --cov=autopr --cov-report=htmlCurrent Coverage:
- Security tests: 29 (100% passing)
- Total test files: 115+
- Critical components: Fully covered
Backend:
- Python 3.12+ (targeting 3.13)
- FastAPI (REST API), Flask (Dashboard)
- SQLAlchemy 2.0 (PostgreSQL, SQLite)
- Redis (caching), Alembic (migrations)
- Pydantic 2.9.0+ (validation)
Frontend:
- React 18+ with TypeScript
- Tauri (desktop framework)
- Tailwind CSS 3.x
- shadcn/ui components
- Vite (build tool)
AI/LLM:
- OpenAI (GPT-4)
- Anthropic (Claude)
- Mistral AI, Groq
- Multi-provider fallback system
Infrastructure:
- Docker & Kubernetes
- GitHub Actions (CI/CD)
- OpenTelemetry (monitoring)
- Prometheus & Grafana
- Workflow execution timeout: 300 seconds (configurable)
- Workflow history limit: 1000 entries (prevents memory leaks)
- String parameter limit: 10,000 characters (security)
- Nesting depth limit: 10 levels (DoS prevention)
- Rate limits: Dependent on GitHub/LLM provider limits
Short-term (Weeks 1-4):
- Database connection pooling optimization
- Additional database indexes
- API reference documentation
- Deployment playbooks
Medium-term (Weeks 4-8):
- WCAG 2.1 AA compliance improvements
- Performance benchmarking suite
- Accessibility compliance review
Long-term (Weeks 8-15):
- Real-time collaboration features
- AI-powered code suggestion engine
- Workflow analytics dashboard
See Comprehensive Analysis for complete roadmap.
We welcome contributions! Please see our Contributing Guide for details.
AutoPR Engine uses a volume-aware, multi-stage workflow system for automated quality checks:
-
PR-Checks (Ultra-fast validation)
- Runs immediately on PR creation/update
- Pre-commit hooks on changed files only
- Minimal tests for draft PRs
- 10-minute timeout for quick feedback
-
Quality Feedback (Detailed PR feedback)
- Pre-commit hooks on all files
- Security scanning (Bandit + Safety)
- Detailed PR comments with reports
- Artifact uploads for security reports
-
CI (Comprehensive checks)
- Volume-aware conditional execution
- Full test suite with coverage
- Type checking (MyPy)
- Linting (Ruff) with volume-based rules
- Security checks (volume ≥ 600)
-
Background Fixer (Maintenance)
- Scheduled daily runs
- Automated code fixing
- Volume-aware fix aggressiveness
The workflow system uses a volume-based approach (0-1000) to determine check intensity:
- 0-199: Tests only
- 200-399: Tests + relaxed linting
- 400-599: Tests + linting + type checking
- 600+: All checks including security
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Make your changes
- Add tests for new functionality
- Run the test suite (
pytest) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
Note: The workflow system will automatically run appropriate checks based on your PR and repository volume settings. See Workflow Documentation for detailed information.
- Follow PEP 8 style guidelines
- Add type hints for all functions
- Write comprehensive tests for new features
- Update documentation for user-facing changes
This project is licensed under the MIT License - see the LICENSE file for details.
- AutoGen team for multi-agent AI framework
- CodeRabbit for AI code review integration
- Axolo team for Slack PR automation
- GitHub for the platform and APIs
- OpenAI and Anthropic for AI capabilities
- All our contributors and community members
- Documentation: https://codeflow-engine.readthedocs.io
- GitHub Issues: Report bugs and request features
- Discussions: Community discussions
- Email: [email protected]