AutoPR Engine 🤖

AI-Powered GitHub PR Automation and Issue Management

[](https://badge.fury.io/py/autopr-engine) [](https://www.python.org/downloads/) [](https://opensource.org/licenses/MIT) [](https://github.com/marketplace/autopr-engine)

AutoPR Engine is a comprehensive AI-powered automation platform that transforms GitHub pull request workflows through intelligent analysis, issue creation, and multi-agent collaboration.

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🌟 Features

🤖 AI-Powered PR Analysis

• 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

🔗 Smart Integrations

• 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

⚡ Advanced Automation

• 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

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🚀 Quick Start

Installation

# Install from PyPI
pip install autopr-engine

# Or install with all features
pip install "autopr-engine[full]"

# Docker deployment
docker run -d \
  -e GITHUB_TOKEN=your_token \
  -e OPENAI_API_KEY=your_key \
  -p 8080:8080 \
  neuralliquid/autopr-engine:latest

Basic Configuration

# 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 Action Integration

# .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: neuralliquid/autopr-engine@v1
        with:
          github-token: ${{ secrets.GITHUB_TOKEN }}
          openai-api-key: ${{ secrets.OPENAI_API_KEY }}
          linear-api-key: ${{ secrets.LINEAR_API_KEY }}

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📖 Documentation

Core Concepts

• Architecture Overview - System design and components
• Workflow Guide - Creating and customizing workflows
• Action Reference - Complete action documentation
• Integration Guide - Setting up external services

Platform-Specific Guides

• Replit Integration - Prototype to production
• Slack/Axolo Setup - PR channel automation
• Linear Workflow - Issue management
• Azure Deployment - Cloud deployment guide

Advanced Topics

• Multi-Agent AI - AutoGen integration
• Memory Systems - Learning and patterns
• Custom Actions - Building your own
• Monitoring - Observability setup

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🏗️ Architecture

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

Loading

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🛠️ Development

Setup Development Environment

# Clone repository
git clone https://github.com/neuralliquid/autopr-engine.git
cd autopr-engine

# 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

Project Structure

autopr-engine/
├── autopr/                 # Core engine package
│   ├── actions/            # 50+ automation actions
│   ├── workflows/          # Pre-built workflow definitions
│   ├── integrations/       # External service integrations
│   ├── ai/                 # AI and LLM components
│   ├── memory/             # Learning and memory systems
│   ├── quality/            # Quality gates and validation
│   └── server/             # Web server and API
├── tests/                  # Comprehensive test suite
├── docs/                   # Documentation
├── docker/                 # Container definitions
├── scripts/                # Utility scripts
├── examples/               # Usage examples
├── requirements.txt        # Production dependencies
├── requirements-dev.txt    # Development dependencies
├── setup.py               # Package configuration
├── pyproject.toml         # Modern Python packaging
├── Dockerfile             # Container build
├── docker-compose.yml     # Local development
└── README.md              # This file

Testing

# 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

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🔧 Configuration

Environment Variables

# 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

Configuration File

# autopr.yml
repositories:
  - owner: neuralliquid
    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"

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🔌 Integrations

Communication Platforms

• Slack - Channel automation with Axolo
• Microsoft Teams - Enterprise communication
• Discord - Community-focused teams
• Notion - Documentation automation

Project Management

• Linear - Modern issue tracking
• GitHub Issues - Native GitHub integration
• Jira - Enterprise project management

AI & Development Tools

• CodeRabbit - AI code review
• GitHub Copilot - AI coding assistant
• AutoGen - Multi-agent collaboration
• Platform Detection - 25+ platforms

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📊 Monitoring & Observability

Built-in Metrics

• PR processing times and success rates
• AI tool performance and accuracy
• Issue creation and resolution tracking
• Integration health monitoring

Supported Monitoring

• Prometheus metrics collection
• Grafana dashboards
• Sentry error tracking
• DataDog APM integration
• Custom webhooks for external systems

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🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Workflow

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Add tests for new functionality
5. Run the test suite (pytest)
6. Commit your changes (git commit -m 'Add amazing feature')
7. Push to the branch (git push origin feature/amazing-feature)
8. Open a Pull Request

Code Standards

• Follow [PEP 8](https://peps.python.org/pep-0008/) style guidelines
• Add type hints for all functions
• Write comprehensive tests for new features
• Update documentation for user-facing changes

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📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🙏 Acknowledgments

• 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

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📞 Support

• Documentation: [https://autopr-engine.readthedocs.io](https://autopr-engine.readthedocs.io)
• GitHub Issues: [Report bugs and request features](https://github.com/neuralliquid/autopr-engine/issues)
• Discussions: [Community discussions](https://github.com/neuralliquid/autopr-engine/discussions)
• Email: support@neuralliquid.com

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Made with ❤️ by the NeuralLiquid team