OpenLIT: the open-source agent harness engineering platform

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:

A production agent can involve:

flowchart TD
    U([User]) --> A[AI Agent]
    A --> L[LLM calls]
    A --> T[Tool calls]
    A --> R[Retrieval]
    A --> M[Memory]
    A --> S[Sub-agents]
    A --> P[Prompts]
    A --> C[Code changes]
    L & T & R & M & S & P & C --> E{{Evaluation}}
    E --> O[["Cost / Quality / Errors"]]

    style U fill:#F97316,stroke:#7C2D12,color:#fff
    style A fill:#111827,stroke:#F97316,stroke-width:2px,color:#fff
    style E fill:#111827,stroke:#F97316,stroke-width:2px,color:#fff
    style O fill:#F97316,stroke:#7C2D12,color:#fff

⚡ Get started in 5 minutes

1. Start OpenLIT

git clone https://github.com/openlit/openlit.git
cd openlit

docker compose up -d

Open:

http://127.0.0.1:3000

2. Install the SDK

Python:

pip install openlit

TypeScript:

npm install openlit

3. Instrument your application

Python:

import openlit

openlit.init()

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:

export OTEL_EXPORTER_OTLP_ENDPOINT="http://127.0.0.1:4318"

Or:

import openlit

openlit.init(
    otlp_endpoint="http://127.0.0.1:4318"
)

Open your dashboard and start exploring your AI application's traces, metrics, costs, and performance.

🤖 Coding agent observability: Claude Code, Cursor, Codex & Windsurf

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

curl -fsSL https://raw.githubusercontent.com/openlit/openlit/main/cli/scripts/install.sh | sh

Windows

iwr -useb https://raw.githubusercontent.com/openlit/openlit/main/cli/scripts/install.ps1 | iex

Configure OpenLIT:

openlit configure --endpoint http://127.0.0.1:4318

Install coding-agent instrumentation:

openlit coding install --vendor=all

Or install individual integrations:

openlit coding install --vendor=cursor
openlit coding install --vendor=claude-code
openlit coding install --vendor=codex

Check your installation:

openlit doctor

Now OpenLIT can capture:

flowchart LR
    S([Coding Agent Session]) --> P[User prompt]
    S --> L[LLM calls]
    S --> T[Tool calls]
    T --> T1[File reads]
    T --> T2[File edits]
    T --> T3[Shell commands]
    T --> T4[Search]
    S --> SA[Sub-agent activity]
    S --> TU[Token usage]
    S --> CO[Cost]
    S --> CI[Code impact]

    style S fill:#111827,stroke:#F97316,stroke-width:2px,color:#fff
    style T fill:#111827,stroke:#F97316,stroke-width:2px,color:#fff

Explore the resulting sessions in the Coding Agents dashboard.

🔍 Agent observability & LLM tracing (OpenTelemetry)

Traces

Understand exactly what happened during an AI request.

All represented using OpenTelemetry.

💰 LLM cost tracking

Track the cost of your AI applications across:

Support custom pricing for custom and fine-tuned models.

🧪 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."

🐛 Debug production AI agents

Find the requests that matter.

Investigate: