AI-Powered Observability Demo Generator

Overview

This tool generates realistic telemetry data (traces, logs, and metrics) for user-defined microservices scenarios. Users can describe scenarios in natural language, and the system will produce configuration files and continuously stream synthetic telemetry into OpenTelemetry (OTel) Collectors via OTLP.

✨ Key Features

• AI-Powered Config Generation: Natural language → production-ready observability scenarios
• Multi-User Job Management: Multiple concurrent telemetry generation jobs
• Multiple LLM Providers: Support for OpenAI and Amazon Bedrock (Claude Sonnet 4)
• Realistic Telemetry: Traces, logs, and metrics with semantic conventions
• Zero Infrastructure: No need to deploy actual microservices for demos
• Multi-Language Simulation: Runtime-specific metrics per language (Java, Python, Go, etc.)

🚀 Quick Start

1. Clone the repository:

   git clone <repository-url>
   cd otel-demo-gen

2. Set up environment configuration:

   cp .env.example .env
   # Edit .env with your preferred LLM provider (see LLM Setup section)

3. Install dependencies:

   # Backend
   cd backend
   pip install -r requirements.txt
   
   # Frontend  
   cd ../frontend
   npm install

4. Start the application:

   # From root directory
   ./start-local.sh

5. Access the UI:

   • Frontend: http://localhost:5173
   • Backend API: http://localhost:8000

🤖 LLM Provider Setup

Option 1: OpenAI (Default)

LLM_PROVIDER=openai
OPENAI_API_KEY=sk-your-openai-api-key-here
OPENAI_MODEL=gpt-4o-mini

Option 2: Amazon Bedrock (Claude Sonnet 4)

LLM_PROVIDER=bedrock
AWS_ACCESS_KEY_ID=your-aws-access-key
AWS_SECRET_ACCESS_KEY=your-aws-secret-key
AWS_REGION=us-east-1
BEDROCK_MODEL_ID=anthropic.claude-3-5-sonnet-20241022-v2:0

No LLM Provider

If no LLM is configured, you can still:

• Use the "Load Test Config" button
• Create jobs with pre-built configurations
• Access test config via GET /test-config

📖 Detailed Setup: See README-LLM-Setup.md for complete configuration guide.

👥 Multi-User Job Management

The application supports multiple concurrent telemetry generation jobs:

• Create New Job: Generate or load configurations and start telemetry streams
• Manage Jobs: View all running/stopped jobs across all users
• Job Details: See service counts, languages, configuration summaries
• Real-time Updates: Job status updates every 5 seconds

API Endpoints

• GET /jobs - List all jobs
• POST /start - Start new telemetry job
• POST /stop/{job_id} - Stop specific job
• DELETE /jobs/{job_id} - Delete job
• GET /llm-config - Check LLM provider status

🏗️ Architecture

User Input (Natural Language) 
    ↓ 
LLM (OpenAI/Bedrock) 
    ↓ 
YAML Configuration 
    ↓ 
Telemetry Generation Engine 
    ↓ 
OTLP JSON Payloads 
    ↓ 
OpenTelemetry Collector 
    ↓ 
Observability Backend

Tech Stack

• Backend: Python, FastAPI, OpenTelemetry
• Frontend: React, Vite, Tailwind CSS
• LLM Integration: OpenAI API, Amazon Bedrock
• Telemetry: OTLP JSON format

📊 Configuration Schema

services:
  - name: payment-service
    language: java
    role: backend
    operations:
      - name: "ProcessPayment"
        span_name: "POST /payments"
        business_data:
          - name: "amount"
            type: "number"
            min_value: 1.00
            max_value: 999.99
    depends_on:
      - db: postgres-main
      - service: fraud-service
        protocol: http

databases:
  - name: postgres-main
    type: postgres

telemetry:
  trace_rate: 5
  error_rate: 0.05
  include_logs: true

🐳 Deployment

Docker Compose

docker-compose up -d

Kubernetes

kubectl apply -f k8s/

Environment Variables

# Required for LLM generation
LLM_PROVIDER=openai|bedrock
OPENAI_API_KEY=sk-...        # For OpenAI
AWS_ACCESS_KEY_ID=...        # For Bedrock
AWS_SECRET_ACCESS_KEY=...    # For Bedrock

# Optional
OTEL_COLLECTOR_URL=http://localhost:4318
DEBUG=false

🧪 Testing

# Test backend health
curl http://localhost:8000/

# Check LLM configuration
curl http://localhost:8000/llm-config

# Get test configuration
curl http://localhost:8000/test-config

# List jobs
curl http://localhost:8000/jobs

🛠️ Development

Prerequisites

• Python 3.9+
• Node.js 18+
• OpenAI API key OR AWS credentials with Bedrock access

Local Development

# Backend with hot reload
cd backend && uvicorn main:app --reload --port 8000

# Frontend with hot reload  
cd frontend && npm run dev

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

📝 License

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

🆘 Support

• Issues: GitHub Issues
• Documentation: Architecture Guide
• LLM Setup: LLM Setup Guide