StoP - AI-Powered SDLC Workflow with LangGraph

2 minute read

A sophisticated AI-powered workflow system that automatically generates user stories, production-ready code, containerization code, and comprehensive unit tests from Swagger/OpenAPI specifications using LangGraph and multiple AI agents. Includes real-time workflow visualization and multi-cloud support.

Features

  • Multi-Agent Architecture: Specialized AI agents for Swagger analysis, user story generation, code, container, and test generation
  • LangGraph Orchestration: Robust workflow management and state handling
  • Swagger/OpenAPI Support: Parse and analyze API specifications
  • User Story Generation: Create detailed user stories with acceptance criteria
  • Code Generation: Generate production-ready code in multiple frameworks
  • Containerization: Generate Docker/container code for cloud deployment
  • Test Generation: Comprehensive unit and integration tests
  • Multi-Cloud Support: Select AWS or Azure for container code
  • Interactive UI: User-friendly Streamlit interface
  • Real-Time Workflow Visualization: Mermaid diagrams show workflow state and errors
  • Export Capabilities: Download complete project as ZIP

Installation

  1. Clone the repository
  2. Install dependencies: pip install -r requirements.txt
  3. Create a .env file and add your Anthropic API key:
     ANTHROPIC_API_KEY=your-anthropic-key-here
  4. Run: streamlit run main.py

Usage

  1. Upload your Swagger/OpenAPI file (JSON or YAML) in the sidebar
  2. Select your preferred code framework, test framework, and public cloud (AWS or Azure)
  3. Click “🚀 Generate Components”
  4. Watch the real-time workflow diagram update as each step completes
  5. Review generated user stories, code, container code, and tests in the results tabs
  6. Download the complete project as a ZIP file

Architecture

Agents

  • SwaggerAnalyzerAgent: Analyzes API specifications and extracts metadata
  • UserStoryAgent: Generates user stories with business value focus
  • CodeGeneratorAgent: Creates production-ready code following best practices
  • ContainerziedCodeGenerationAgent: Generates Docker/container code for cloud deployment
  • TestGeneratorAgent: Generates comprehensive test suites

Workflow

The LangGraph workflow orchestrates agents in sequence:

  1. Swagger Analysis → 2. User Story Generation → 3. Code Generation → 4. Test Generation → 5. Container Code Generation

Supported Frameworks

Code Generation:

  • FastAPI (Python)
  • Flask (Python)
  • Django (Python)
  • Express.js (Node.js)
  • Spring Boot (Java)

Test Frameworks:

  • pytest (Python)
  • unittest (Python)
  • Jest (JavaScript)
  • JUnit (Java)
  • Mocha (JavaScript)

Container/Cloud:

  • AWS (Docker)
  • Azure (Docker)

Configuration

Customize the workflow behavior by modifying config.yaml:

  • Agent timeouts
  • Model parameters (e.g., temperature, max_tokens)
  • Framework preferences
  • Cloud provider
  • Feature toggles

Real-Time Workflow Visualization

The app displays a real-time Mermaid diagram of the workflow, showing:

  • Each step (node) in the workflow
  • Number of generated items per step
  • Current step highlighted
  • Errors highlighted in red

Repository

github.com/uday160386/asr-capstone-project

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