Skyvern is an open-source browser automation framework that uses LLMs and Computer Vision to automate browser-based workflows.Laminar provides comprehensive instrumentation of Skyvern with all core functions being traced automatically. This includes full browser session recordings that capture every interaction, making it immensely valuable for debugging failed workflows and evaluating automation performance.
Laminar excels at tracing AI-powered browser automation by providing visibility into LLM decision-making processes, browser interaction outcomes, and complete session recordings synchronized with execution steps.
To trace Skyvern workflows with Laminar, initialize Laminar and configure LiteLLM callbacks at the top of your project. This will automatically capture all LLM calls, browser session recordings, and workflow execution details.
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from skyvern import Skyvernimport asyncioimport litellmfrom lmnr import Laminar, LaminarLiteLLMCallback, Instrumentsfrom dotenv import load_dotenvload_dotenv()# Initialize Laminar# This will automatically trace all Skyvern functions# Disable OpenAI to avoid double instrumentation of LLM callsLaminar.initialize(disabled_instruments=set([Instruments.OPENAI]))# Configure LiteLLM to trace all LLM calls made by Skyvernlitellm.callbacks = [LaminarLiteLLMCallback()]skyvern = Skyvern()async def main(): task = await skyvern.run_task( prompt="go to lmnr.ai, summarize the pricing page." ) print(task)if __name__ == "__main__": asyncio.run(main())
You can view traces in the Laminar UI by navigating to the traces tab in your project. When you select a trace, you can see:
Browser Session Recording: Full video recording of the browser window synchronized with execution steps - immensely valuable for debugging failed workflows and evaluating automation quality
LLM Interactions: All prompts sent to the language model and their responses
Workflow Steps: Sequential execution of tasks and their outcomes
Performance Metrics: Latency, token usage, and cost
Image tracing: Browser screenshots that were sent to LLM for analysis
Error Handling: Exceptions and errors that occurred during the execution
The trace timeline shows the complete workflow execution with synchronized browser recordings, making it easy to debug issues, understand failure points, and optimize performance. This is particularly powerful for evaluations where you can visually verify whether the automation achieved the intended outcome.