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OpenLIT Integration

Overview

Browser Use has native integration with 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.

Setup

Install OpenLIT alongside Browser Use:

pip install openlit browser-use

Usage

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

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

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:

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:

# 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.

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