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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.

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Setup

Install OpenLIT alongside Browser Use: 
pip install openlit browser-use

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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())

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Viewing Traces

OpenLIT provides a powerful dashboard where you can:

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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.

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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

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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

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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

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Configuration

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Custom OpenTelemetry Endpoint Configuration

import openlit

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

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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"

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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

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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.