---
title: "OpenLIT"
description: "Complete observability for Browser Use with OpenLIT tracing"
icon: "chart-line"
mode: "wide"
---

## Overview

Browser Use has native integration with [OpenLIT](https://github.com/openlit/openlit) - an open-source opentelemetry-native platform that provides complete, granular traces for every task your browser-use agent performs—from high-level agent invocations down to individual browser actions.

Read more about OpenLIT in the [OpenLIT docs](https://docs.openlit.io).

## Setup

Install OpenLIT alongside Browser Use:

```bash
pip install openlit browser-use
```

## Usage

OpenLIT provides automatic, comprehensive instrumentation with **zero code changes** beyond initialization:

```python {5-6}
from browser_use import Agent, Browser, ChatOpenAI
import asyncio
import openlit

# Initialize OpenLIT - that's it!
openlit.init()

async def main():
	browser = Browser()

	llm = ChatOpenAI(
		model="gpt-4o",
	)

	agent = Agent(
		task="Find the number trending post on Hacker news",
		llm=llm,
		browser=browser,
	)

	history = await agent.run()
	return history

if __name__ == "__main__":
	history = asyncio.run(main())
```

## Viewing Traces

OpenLIT provides a powerful dashboard where you can:

### Monitor Execution Flows
See the complete execution tree with timing information for every span. Click on any `invoke_model` span to see the exact prompt sent to the LLM and the complete response with agent reasoning.

### Track Costs and Token Usage
- Cost breakdown by agent, task, and model
- Token usage per LLM call with full input/output visibility
- Compare costs across different LLM providers
- Identify expensive prompts and optimize them

### Debug Failures with Agent Thoughts
When an automation fails, you can:
- See exactly which step failed
- Read the agent's thinking at the failure point
- Check the browser state and available elements
- Analyze whether the failure was due to bad reasoning or bad information
- Fix the root cause with full context

### Performance Optimization
- Identify slow steps (LLM calls vs browser actions vs HTTP requests)
- Compare execution times across runs
- Optimize max_steps and max_actions_per_step
- Track HTTP request latency for page navigations

## Configuration

### Custom OpenTelemetry Endpoint Configuration

```python
import openlit

# Configure custom OTLP endpoints
openlit.init(
	otlp_endpoint="http://localhost:4318",
	application_name="my-browser-automation",
	environment="production"
)
```

### Environment Variables

You can also configure OpenLIT via environment variables:

```bash
export OTEL_EXPORTER_OTLP_ENDPOINT="http://localhost:4318"
export OTEL_SERVICE_NAME="browser-automation"
export OTEL_ENVIRONMENT="production"
```

### Self-Hosted OpenLIT

If you prefer to keep your data on-premises:

```bash
# Using Docker
docker run -d \
  -p 4318:4318 \
  -p 3000:3000 \
  openlit/openlit:latest

# Access dashboard at http://localhost:3000
```

## Integration with Existing Tools

OpenLIT uses OpenTelemetry under the hood, so it integrates seamlessly with:
- **Jaeger** - Distributed tracing visualization
- **Prometheus** - Metrics collection and alerting
- **Grafana** - Custom dashboards and analytics
- **Datadog** - APM and log management
- **New Relic** - Full-stack observability
- **Elastic APM** - Application performance monitoring

Simply configure OpenLIT to export to your existing OTLP-compatible endpoint.