Observability

orq.ai offers comprehensive Observability capabilities for monitoring your AI applications and deployments.

Core Observability Features

Built-in Monitoring

Once your Deployments are in use, orq.ai automatically provides:

• Logs - Record requests, responses, errors, and performance metrics
• Traces - Track end-to-end request flows across services and operations
• Threads - Monitor concurrent execution contexts and conversation flows

OpenTelemetry Integration

For advanced observability, orq.ai supports industry-standard OpenTelemetry for detailed application monitoring:

• Observability Frameworks - Quick setup guide for sending OpenTelemetry traces to orq.ai
• OTLP Endpoint: https://api.orq.ai/v2/otel - Standard OpenTelemetry Protocol endpoint
• Multi-Language Support - Python, Node.js, Java, and other OpenTelemetry-supported languages

OpenTelemetry Use Cases

Application Performance Monitoring

• Track LLM request latencies and token usage
• Monitor embedding generation and vector operations
• Analyze RAG pipeline performance

AI Agent Observability

• Trace multi-step agent workflows and tool usage
• Monitor conversation flows and context management
• Debug complex agentic interactions

Custom Instrumentation

• Add spans for business logic and custom operations
• Track model performance across different environments
• Implement distributed tracing across microservices