OpenLIT

**Trace, evaluate, guard, and improve AI agents and coding agents with OpenTelemetry.**
Agent observability · Evals · Guardrails · Prompt management · Cost & GPU monitoring · Self-host free (Apache 2.0)
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What is OpenLIT?
**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, Cursor, OpenAI Agents SDK, LangGraph, CrewAI, and 70+ more integrations.
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. The core loop is *run → observe → evaluate → fix the harness → verify*, and OpenLIT gives you each step:
| Harness engineering step | OpenLIT feature |
|---|---|
| **Observe** every LLM call, tool call, MCP request, retrieval and agent step | Agent observability & OpenTelemetry LLM tracing |
| **Evaluate** quality, safety and cost on real traces | Evals (LLM-as-a-judge, programmatic, human feedback) + CI gates |
| **Guard** the agent at runtime | Guardrails (prompt injection, sensitive topics, topic restriction) |
| **Fix** the harness without redeploying | Prompt Hub, Context, Rule Engine, Vault (secrets) |
| **Compare** models and prompts before shipping | OpenGround |
| **Account** for cost, latency and hardware | Cost tracking, custom pricing, GPU monitoring (NVIDIA, AMD, Intel) |
A production agent can involve:
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⚡ Get started in 5 minutes
1. Start OpenLIT
Open:
2. Install the SDK
Python:
TypeScript:
3. Instrument your application
Python:
That's it.
OpenLIT automatically instruments supported LLM providers, frameworks, vector databases, and other AI infrastructure and exports OpenTelemetry traces and metrics.
4. Send telemetry
By default, configure the OTLP endpoint:
Or:
Open your dashboard and start exploring your AI application's traces, metrics, costs, and performance.
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🤖 Coding agent observability: Claude Code, Cursor, Codex, Windsurf & OpenCode
AI coding agents are powerful — but understanding what they actually did can be difficult.
OpenLIT gives you an OpenTelemetry-native view of coding-agent sessions.
Install the CLI:
macOS / Linux
Windows
Configure OpenLIT:
Install coding-agent instrumentation:
Or install individual integrations:
Check your installation:
Now OpenLIT can capture:
Explore the resulting sessions in the **Coding Agents** dashboard.
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🔍 Agent observability & LLM tracing (OpenTelemetry)
Traces
Understand exactly what happened during an AI request.
All represented using OpenTelemetry.
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💰 LLM cost tracking
Track the cost of your AI applications across:
Support custom pricing for custom and fine-tuned models.
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🧪 Agent evals: LLM-as-a-judge, programmatic evals & CI gates
Automatically evaluate LLM and agent outputs using LLM-as-a-Judge evaluations.
Built-in evaluation types include:
Use evaluations to move from:
**"The agent produced an answer."**
to:
**"The agent produced a good answer."**
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🐛 Debug production AI agents
Find the requests that matter.
Investigate:
Go from:
> Something went wrong.
to a fully traced root cause:
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📸 See OpenLIT in action
Traces — full agent conversation, spans & cost
Evaluations — hallucination, bias & toxicity checks