# API Integration Debugger 🔍 A production-ready multi-agent system built with LangGraph for debugging API integration issues. Uses specialized AI agents to analyze requests, responses, authentication, and schemas to provide actionable solutions. ## 🌟 Features - **Multi-Agent Architecture**: 6 specialized agents working together - **Comprehensive Analysis**: Request structure, response parsing, auth debugging, schema validation - **RESTful API**: FastAPI backend ready for microservice deployment - **Real-time Processing**: Streaming agent progress - **Production Ready**: Type-safe, error handling, CORS enabled - **Easy Deployment**: Optimized for Render and other cloud platforms ## 🏗️ Architecture ``` User Request → Supervisor Agent ↓ ┌───────────────┼───────────────┐ ↓ ↓ ↓ Request Analyzer Response Parser Auth Troubleshooter ↓ ↓ ↓ └───────────────┼───────────────┘ ↓ Schema Validator ↓ Solution Generator ↓ Final Solution ``` ### Agents 1. **Supervisor Agent**: Routes workflow to specialized agents 2. **Request Analyzer**: Validates HTTP method, headers, body structure 3. **Response Parser**: Interprets status codes and error messages 4. **Auth Troubleshooter**: Debugs JWT, OAuth2, API keys 5. **Schema Validator**: Ensures OpenAPI/Swagger compliance 6. **Solution Generator**: Creates actionable fixes with code examples ## 🚀 Quick Start ### Prerequisites - Python 3.11+ - Groq API key ([Get one here](https://console.groq.com)) ### Installation ```bash # Clone the repository git clone cd api-debugger # Create virtual environment python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate # Install dependencies pip install -r requirements.txt # Setup environment cp .env.example .env # Edit .env and add your GROQ_API_KEY ``` ### Configuration Edit `.env`: ```env GROQ_API_KEY=your_actual_groq_api_key MODEL_NAME=llama-3.3-70b-versatile TEMPERATURE=0.1 MAX_TOKENS=2000 PORT=8000 HOST=0.0.0.0 ``` ## 📖 Usage ### 1. FastAPI Server (Recommended for Frontend Integration) Start the server: ```bash python -m uvicorn api.main:app --host 0.0.0.0 --port 8000 ``` Or: ```bash cd api python main.py ``` API will be available at `http://localhost:8000` **API Documentation**: `http://localhost:8000/docs` ### 2. CLI Interface ```bash python main.py ``` Follow the interactive prompts. ## 🔌 API Endpoints ### POST /debug Main debugging endpoint. **Request Body:** ```json { "issue_description": "Getting 401 error when calling the API", "api_request": { "method": "POST", "url": "https://api.example.com/users", "headers": { "Authorization": "Bearer token123", "Content-Type": "application/json" }, "body": { "name": "John Doe", "email": "john@example.com" } }, "api_response": { "status_code": 401, "body": "{\"error\": \"Invalid token\"}" }, "auth_type": "bearer" } ``` **Response:** ```json { "root_cause": "JWT token is expired", "solution": "1. Request a new access token...\n2. Update your authorization header...", "analysis_results": { "request_analysis": {...}, "response_analysis": {...}, "auth_analysis": {...} }, "status": "completed" } ``` ### POST /test-request Test an API request directly. **Request Body:** ```json { "method": "GET", "url": "https://api.example.com/users", "headers": { "Authorization": "Bearer token123" } } ``` ### GET /health Health check endpoint. ```json { "status": "healthy", "service": "api-debugger" } ``` ## 🎯 Example Scenarios ### Scenario 1: Expired JWT Token ```bash curl -X POST http://localhost:8000/debug \ -H "Content-Type: application/json" \ -d '{ "issue_description": "Getting 401 unauthorized error", "api_response": { "status_code": 401, "body": "Token expired" }, "auth_type": "bearer" }' ``` ### Scenario 2: Schema Validation Error ```bash curl -X POST http://localhost:8000/debug \ -H "Content-Type: application/json" \ -d '{ "issue_description": "API returns 400 bad request", "api_request": { "method": "POST", "url": "https://api.example.com/users", "body": { "name": "John", "age": "twenty" } }, "api_response": { "status_code": 400, "body": "Invalid age format" } }' ``` ### Scenario 3: CORS Error ```bash curl -X POST http://localhost:8000/debug \ -H "Content-Type: application/json" \ -d '{ "issue_description": "CORS error in browser console", "api_response": { "error": "CORS policy blocked" } }' ``` ## 🌐 Deploying on Render ### 1. Create `render.yaml` ```yaml services: - type: web name: api-debugger env: python buildCommand: pip install -r requirements.txt startCommand: uvicorn api.main:app --host 0.0.0.0 --port $PORT envVars: - key: GROQ_API_KEY sync: false - key: PYTHON_VERSION value: 3.11.0 ``` ### 2. Deploy 1. Push code to GitHub 2. Connect repository in Render dashboard 3. Add `GROQ_API_KEY` environment variable 4. Deploy! Your API will be live at: `https://your-app.onrender.com` ## 🛠️ Tools Available to Agents 1. **make_http_request**: Execute HTTP requests 2. **decode_jwt_token**: Analyze JWT tokens 3. **validate_json_schema**: Validate against JSON schemas 4. **parse_openapi_spec**: Extract OpenAPI specifications 5. **test_oauth_flow**: Test OAuth2 authentication ## 📦 Project Structure ``` api-debugger/ ├── agents/ # AI agent implementations │ ├── supervisor.py │ ├── request_analyzer.py │ ├── response_parser.py │ ├── auth_troubleshooter.py │ ├── schema_validator.py │ └── solution_generator.py ├── graph/ # LangGraph workflow │ ├── state.py │ └── workflow.py ├── tools/ # Agent tools │ ├── http_client.py │ ├── jwt_decoder.py │ └── schema_validator_tool.py ├── api/ # FastAPI application │ └── main.py ├── config.py # Configuration ├── main.py # CLI interface └── requirements.txt # Dependencies ``` ## 🔒 Security Notes - Never commit `.env` file - Use environment variables for sensitive data - Configure CORS properly for production - Validate all inputs in production - Rate limit the API endpoints ## 🧪 Testing ```bash # Test the API curl http://localhost:8000/health # Run a debug request curl -X POST http://localhost:8000/debug \ -H "Content-Type: application/json" \ -d @examples/request.json ``` ## 🤝 Integration with Frontend This backend is designed as a microservice. Your frontend can: 1. Call `/debug` endpoint with API issues 2. Display real-time agent progress 3. Show analysis results and solutions 4. Test API requests via `/test-request` **Example Frontend Integration:** ```javascript const response = await fetch('http://localhost:8000/debug', { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ issue_description: userInput, api_request: requestData, api_response: responseData }) }); const result = await response.json(); console.log(result.solution); ``` ## 📝 Environment Variables | Variable | Description | Default | |----------|-------------|---------| | GROQ_API_KEY | Groq API key (required) | - | | MODEL_NAME | LLM model name | llama-3.3-70b-versatile | | TEMPERATURE | LLM temperature | 0.1 | | MAX_TOKENS | Max tokens per response | 2000 | | PORT | Server port | 8000 | | HOST | Server host | 0.0.0.0 | ## 🐛 Troubleshooting **Issue**: Module not found error ```bash # Make sure you're in the right directory and venv is activated pip install -r requirements.txt ``` **Issue**: GROQ_API_KEY not found ```bash # Check your .env file exists and has the key cat .env ``` **Issue**: Port already in use ```bash # Change PORT in .env or use different port PORT=8001 python -m uvicorn api.main:app ``` ## 📄 License MIT License - feel free to use in your projects! ## 🙏 Acknowledgments - Built with [LangGraph](https://github.com/langchain-ai/langgraph) - Powered by [Groq](https://groq.com) - Framework: [FastAPI](https://fastapi.tiangolo.com) --- **Made with ❤️ for developers debugging APIs** # API_Analyse