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OpenLIT is an open-source agent harness engineering platform. It gives teams OpenTelemetry-native tracing, evaluations, guardrails, prompt and context management, and cost and GPU monitoring for the harness around their AI agents and coding agents, so every agent failure can be traced, scored, and turned into a harness fix. OpenLIT is free to self-host under Apache 2.0 and works with any model, framework, or harness (Claude Code, Codex, OpenAI Agents SDK, LangGraph, CrewAI, and more).

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What is agent harness engineering?

An AI agent is a model plus a harness. The harness is everything except the model: tools, context, prompts, memory, hooks, guardrails, and feedback loops. Agent harness engineering is the discipline of designing, measuring, and improving that harness so agents are reliable in production. See the Concepts glossary for short definitions of agent harness, agent observability, agent evals, guardrails, and related terms.
LLM observability and AI agent observability for agents built with CrewAI, LangGraph, or any agent framework. OpenLIT brings OpenTelemetry-native instrumentation so you can analyze how your agents perform in production with full stack visibility:
  • Tracing - Instrument once via OpenTelemetry-native SDKs for 90+ LLMs and agent frameworks to produce OpenTelemetry traces.
  • Lowest level transparency - Exceptions Monitoring surfaces errors with full stack traces and span context so you know exactly where and why something failed. All telemetry is automatically redacted for sensitive data before it leaves your stack.
  • Understand cost and latency - Track token consumption, spend per model, provider, and request. Monitor GPU utilization alongside LLM call latency in custom dashboards built on your raw telemetry.

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Get Started

Instrument an AI Agent

Production-ready AI Observability in 2 steps with zero code changes

Observe coding agents

Track spend and usage for Claude Code, Cursor, & more across teams

Deploy OpenLIT

Self-host the full platform with Docker Compose or Helm

Evaluate LLM responses

Score live traces automatically with prebuilt LLM-as-a-judge evaluators

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Frequently asked questions

OpenLIT is an open-source agent harness engineering platform. It provides OpenTelemetry-native tracing, evaluations, guardrails, prompt management, and cost and GPU monitoring for AI agents and coding agents, and it is free to self-host under Apache 2.0.
An agent harness is everything in an AI agent except the model: the tools, context, prompts, memory, hooks, guardrails, and feedback loops that turn a model into a working agent. Claude Code, Codex, and frameworks such as LangGraph or CrewAI are harnesses; OpenLIT observes, evaluates, and improves any of them via OpenTelemetry.
Agent harness engineering is the discipline of designing, measuring, and improving everything around the model in an AI agent so the agent is reliable in production. Teams observe failures in traces, evaluate them, fix the harness, and verify the fix. OpenLIT is the open-source platform for that loop.
Yes. OpenLIT is fully open source under Apache 2.0 and self-hostable via Docker Compose or Helm, so your telemetry and prompts never have to leave your infrastructure.
No. OpenLIT’s SDK auto-instruments 70+ LLMs, agent frameworks, and vector databases with zero code changes, or you can call openlit.init() once for manual instrumentation if you want more control - both produce the same OpenTelemetry traces.
Yes. Because OpenLIT is OpenTelemetry-native, you can export traces and metrics to Grafana, Datadog, New Relic, SigNoz, or any OTLP-compatible backend - see Destinations.