OpenLIT is an open-source observability platform purpose-built for AI applications that takes an OpenTelemetry-native approach to LLM monitoring. Rather than creating yet another proprietary tracing format, OpenLIT instruments LLM calls as standard OpenTelemetry spans and metrics, which means traces flow directly into whatever observability backend a team already runs. This design philosophy eliminates vendor lock-in and lets AI observability coexist with existing infrastructure monitoring in a single pane of glass.
The platform covers the full AI engineering lifecycle beyond basic tracing. An evaluation framework lets teams define and run quality checks on LLM outputs. A prompt management system provides version control and A/B testing for prompts. A secrets vault protects API keys and sensitive configuration. GPU telemetry tracks utilization, memory, and temperature across inference infrastructure. All of these capabilities share the same data pipeline and dashboard, avoiding the tool sprawl that typically accompanies production LLM deployments.
OpenLIT provides SDK instrumentation for Python, TypeScript, Java, and C# with auto-instrumentation for over 50 LLM providers and frameworks including OpenAI, Anthropic, LangChain, LlamaIndex, and Hugging Face. The self-hosted deployment runs on standard infrastructure with no special requirements. The project is Apache 2.0 licensed and has an active development community shipping regular releases throughout 2026.
