ai-workflow-utils
Advanced tools
A comprehensive automation platform that streamlines software development workflows by integrating AI-powered content generation with popular development tools like Jira, Bitbucket, and email systems. Includes startup service management for automatic syst
The Ultimate AI-Powered Development Workflow Automation Platform
Streamline your development process with intelligent Jira ticket creation, AI-powered code reviews & pull request creation with custom template support, featuring a beautiful dark/light theme interface
Create professional Jira tickets (Tasks, Bugs, Stories) using AI with multiple provider support:
Revolutionary AI-powered pull request creation for Atlassian Bitbucket:
Revolutionary AI-powered pull request reviews for Atlassian Bitbucket:
Comprehensive logging and monitoring system for troubleshooting and system insights:
The new API Client module provides a flexible, general-purpose interface for making API requests to any service (Jira, Bitbucket, email, or custom endpoints).
/api/api-client endpoint~/.ai-workflow-utils/environment.jsonComing Soon: AI-powered automation, script generation, and smart workflow integration will be added in future releases.
Beautiful, adaptive interface that automatically adjusts to your preferences:
Advanced Model Context Protocol (MCP) client management for seamless AI tool integration:
# Install Ollama (if you want local AI processing)
# macOS
brew install ollama
# Linux
curl -fsSL https://ollama.com/install.sh | sh
# Windows - Download from https://ollama.com/
# Download the LLaVA model for image analysis
ollama pull llava
# Start Ollama service
ollama serve
Then configure Ollama as your AI provider in the web interface.
npm install -g ai-workflow-utils
# Start the application directly
ai-workflow-utils
The application will start immediately and be available at
http://localhost:3000
For production use or to run automatically on system boot:
ai-workflow-utils startup install
The service will now start automatically on boot. Access at
http://localhost:3000
Startup Service Management:
ai-workflow-utils startup status # Check service status
ai-workflow-utils startup start # Start the service
ai-workflow-utils startup stop # Stop the service
ai-workflow-utils startup uninstall # Remove startup service
Supported Platforms:
For detailed startup service documentation, see STARTUP.md
All configuration is managed through the web-based settings page:
http://localhost:3000/settings/environmentAll changes are saved to ~/.ai-workflow-utils/environment.json and persist
across upgrades.
No manual .env setup required!
AI Workflow Utils is a fully-featured PWA! Install it as a native app for the best experience:
🖥️ Desktop Installation:
http://localhost:3000 in Chrome, Edge✨ PWA Benefits:
What makes it special:
Example Usage:
AI Providers Supported:
Revolutionary PR Generation:
How it works:
AI Features:
Revolutionary Code Review:
How it works:
Comprehensive System Monitoring:
How it works:
Monitoring Features:
AI Workflow Utils follows functional programming principles throughout the codebase:
Benefits:
Comprehensive Jira Mocking Service for development and testing:
# Enable mock mode (no real API calls)
JIRA_MOCK_MODE=true
# Use real Jira API
JIRA_MOCK_MODE=false
Mock Service Features:
Functional Mock Architecture:
// Pure state management
const getMockState = () => ({ ...mockState });
const updateMockState = updates => ({ ...mockState, ...updates });
// Functional API operations
export const createIssue = async issueData => {
/* pure function */
};
export const getIssue = async issueKey => {
/* pure function */
};
export const searchIssues = async jql => {
/* pure function */
};
server/
├── controllers/ # Feature-based controllers
│ ├── jira/ # Jira integration
│ │ ├── services/ # Business logic services
│ │ ├── models/ # Data models
│ │ ├── utils/ # Utility functions
│ │ └── README.md # Module documentation
│ ├── pull-request/ # PR creation & review
│ ├── email/ # Email generation
│ ├── chat/ # AI chat integration
│ └── mcp/ # Model Context Protocol client management
├── mocks/ # Mock services (excluded from npm package)
│ └── jira/ # Comprehensive Jira mocking
└── services/ # Shared services
Each module follows the same structure:
# Interactive setup wizard
ai-workflow-setup
# Check configuration
ai-workflow-utils --config
# Test connections
ai-workflow-utils --test
# Start in development mode
ai-workflow-utils --dev
# Enable debug logging
ai-workflow-utils --debug
# Specify custom port
ai-workflow-utils --port 8080
# View logs in real-time
ai-workflow-utils --logs
# Clear log files
ai-workflow-utils --clear-logs
# Check Ollama status
ai-workflow-utils --ollama-status
# Download recommended models
ai-workflow-utils --setup-ollama
# List available models
ollama list
// Automatic fallback order:
1. OpenAI Compatible API (Primary)
2. Ollama LLaVA (Local fallback)
3. Error handling with user notification
# For different OpenAI-compatible providers:
OPENAI_COMPATIBLE_MODEL=gpt-4-vision-preview # OpenAI
OPENAI_COMPATIBLE_MODEL=claude-3-sonnet-20240229 # Anthropic
OPENAI_COMPATIBLE_MODEL=llama-2-70b-chat # Custom API
# For Ollama local models:
OLLAMA_MODEL=llava:13b # Larger model for better quality
OLLAMA_MODEL=llava:7b # Faster, smaller model
OLLAMA_MODEL=codellama:7b # Code-focused model
# Streaming configuration
STREAM_CHUNK_SIZE=1024
STREAM_TIMEOUT=60000
# Rate limiting
API_RATE_LIMIT=100
API_RATE_WINDOW=900000
# File upload limits
MAX_FILE_SIZE=50MB
ALLOWED_FILE_TYPES=jpg,jpeg,png,gif,mp4,mov,pdf,doc,docx
# Logging configuration
LOG_LEVEL=info # error, warn, info, debug
LOG_MAX_SIZE=10MB # Maximum log file size
LOG_MAX_FILES=5 # Number of rotated log files
LOG_RETENTION_DAYS=30 # Days to keep log files
ENABLE_REQUEST_LOGGING=true # Log all HTTP requests
# Build the application
npm run build
# Create Docker image
docker build -t ai-workflow-utils .
# Run container
docker run -p 3000:3000 --env-file .env ai-workflow-utils
# Install PM2
npm install -g pm2
# Start application
pm2 start ecosystem.config.js
# Monitor
pm2 monit
# View logs
pm2 logs ai-workflow-utils
server {
listen 80;
server_name your-domain.com;
location / {
proxy_pass http://localhost:3000;
proxy_http_version 1.1;
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection 'upgrade';
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_set_header X-Forwarded-Proto $scheme;
proxy_cache_bypass $http_upgrade;
}
}
/health endpoint for monitoring# Logging levels: error, warn, info, debug
LOG_LEVEL=info
# Log file rotation
LOG_MAX_SIZE=10MB
LOG_MAX_FILES=5
# Enable request logging
LOG_REQUESTS=true
We welcome contributions! Here's how to get started:
# Clone the repository
git clone https://github.com/anuragarwalkar/ai-workflow-utils.git
cd ai-workflow-utils
# Install dependencies
npm install
# Set up environment
cp .env.example .env
cp ui/.env.example ui/.env
# Start development server
npm run dev
ai-workflow-utils/
├── bin/ # CLI scripts
├── server/ # Backend (Node.js + Express)
├── ui/ # Frontend (React + Redux)
├── dist/ # Built files
└── docs/ # Documentation
Jira Ticket Creation:
POST /api/jira/preview
Content-Type: application/json
{
"prompt": "Login button not working",
"images": ["base64-encoded-image"],
"issueType": "Bug"
}
Create Pull Request Preview (Streaming):
POST /api/pr/stream-preview
Content-Type: application/json
{
"projectKey": "PROJ",
"repoSlug": "my-repo",
"ticketNumber": "PROJ-123",
"branchName": "feature/my-branch"
}
# Returns Server-Sent Events stream with:
# - status updates
# - title_chunk events (streaming title generation)
# - title_complete event (final title)
# - description_chunk events (streaming description generation)
# - description_complete event (final description)
# - complete event (final preview data)
Create Pull Request:
POST /api/pr/create
Content-Type: application/json
{
"projectKey": "PROJ",
"repoSlug": "my-repo",
"ticketNumber": "PROJ-123",
"branchName": "feature/my-branch",
"customTitle": "feat(PROJ-123): Add user authentication",
"customDescription": "## Summary\nAdded user authentication feature\n\n## Changes Made\n- Added login component\n- Implemented JWT tokens"
}
GitStash PR Review:
POST /api/pr/review
Content-Type: application/json
{
"repoUrl": "https://bitbucket.company.com/projects/PROJ/repos/repo",
"pullRequestId": "123",
"reviewType": "security"
}
File Upload:
POST /api/jira/upload
Content-Type: multipart/form-data
file: [binary-data]
issueKey: "PROJ-123"
MCP Client Management:
# Get all MCP clients
GET /api/mcp/clients
# Create new MCP client
POST /api/mcp/clients
Content-Type: application/json
{
"name": "My MCP Server",
"url": "http://localhost:8080/mcp",
"token": "optional-auth-token",
"description": "Local MCP server for custom tools",
"enabled": true
}
# Update MCP client
PUT /api/mcp/clients/:id
Content-Type: application/json
{
"name": "Updated MCP Server",
"enabled": false
}
# Delete MCP client
DELETE /api/mcp/clients/:id
# Test MCP client connection
POST /api/mcp/clients/:id/test
Ollama Connection Failed:
# Check if Ollama is running
ollama list
# Start Ollama service
ollama serve
# Pull required model
ollama pull llava
Jira Authentication Error:
# Test Jira connection
curl -H "Authorization: Bearer YOUR_TOKEN" \
https://your-company.atlassian.net/rest/api/2/myself
Port Already in Use:
# Use different port
ai-workflow-utils --port 8080
# Or kill existing process
lsof -ti:3000 | xargs kill -9
# Enable detailed logging
ai-workflow-utils --debug
# Check logs
tail -f logs/app.log
This project is licensed under the MIT License - see the LICENSE file for details.
Special thanks to the amazing open-source community and the following technologies that make this project possible:
npm install -g ai-workflow-utils
⭐ Star us on GitHub if this tool helps you!
📢 Share with your team and boost everyone's productivity!
Made with ❤️ by Anurag Arwalkar
Empowering developers worldwide with AI-powered workflow automation
FAQs
A comprehensive automation platform that streamlines software development workflows by integrating AI-powered content generation with popular development tools like Jira, Bitbucket, and email systems. Includes startup service management for automatic syst
The npm package ai-workflow-utils receives a total of 23 weekly downloads. As such, ai-workflow-utils popularity was classified as not popular.
We found that ai-workflow-utils demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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