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Documentation Index

Fetch the complete documentation index at: https://docs.openlit.io/llms.txt

Use this file to discover all available pages before exploring further.

OpenLIT is an open-source AI engineering platform that helps teams build, evaluate and observe AI applications across the entire lifecycle from development to production. OpenLIT provides the following OSS tools to support this goal:
  • OpenLIT - Open-source platform for tracing, prompt management, evaluations, and scalable AI observability with dashboards, metrics, logs, and remote collectors.
  • OpenLIT SDKs - OpenTelemetry-native auto-instrumentation to trace LLMs, agents, vector databases and GPUs with zero-code.
  • OpenLIT Controller - Zero-code LLM and Agent observability for Kubernetes, Docker, and Linux using eBPF and automatic SDK injection.
The project proudly follows OpenTelemetry Semantic Conventions for AI observability standards.

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Features

OpenLIT provides distributed tracing capabilities for understanding and debugging AI applications:
  • OpenTelemetry-native SDKs - Automatic instrumentation for LLMs, agents, frameworks, Vector databases, MCP and GPUs.
  • Exceptions Monitoring - Track and debug application errors with detailed stack traces.
  • Universal Compatibility - View traces from any OpenTelemetry-instrumented tool or LLM instrumentation frameworks like OpenInference, OpenLLMetry.

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Upgrade Information

Important Upgrade Notice: When upgrading from OpenLIT versions prior to 1.15.0 (which introduced Fleet Hub), special attention is required for Docker Compose deployments.If you’re running OpenLIT using Docker Compose, you must use the --remove-orphans flag when upgrading:
# Upgrade with orphan removal
docker-compose up -d --remove-orphans
This is necessary because the OpenTelemetry Collector has been integrated directly into the OpenLIT container, and the standalone otel-collector container is no longer needed. Without --remove-orphans, the old collector container will conflict with the new integrated collector on ports 4317 and 4318.

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Getting Started

Choose your path to start building better AI applications with OpenLIT:

Quickstart: LLM Observability

Production-ready AI monitoring setup in 2 simple steps with zero code changes

Deploy OpenLIT

Deployment options for scalable LLM monitoring infrastructure

Create a dashboard

Create custom visualizations with flexible widgets, queries, and real-time AI monitoring

Manage prompts

Version, deploy, and collaborate on prompts with centralized management and tracking

Zero-code observability with the OpenLIT Controller

Discover and instrument LLM traffic across Kubernetes, Docker, and Linux using eBPF — no code changes required.