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Laminar is best used for observability and debugging of long-running AI agents. It is especially useful when an agent chains LLM calls, tool actions, browser interactions, and evaluations, because the platform keeps those steps visible in a trace. The website emphasizes understanding why an agent failed, rerunning from a specific step, and analyzing repeated failure patterns.
Laminar's debugger is designed to preserve context from previous steps so developers can rerun at step N instead of restarting an entire agent task. The site describes a workflow where teams can run locally, debug in the browser, tune system prompts, and see changes reflected as they save. This is most valuable for failures that happen late in a long workflow.
Yes. The website says Laminar captures browser screen recordings and automatically syncs them with agent traces. It lists integrations with Browser Use, Stagehand, Playwright, Kernel, Browserbase, and more, which makes it relevant for web automation agents that click, navigate, and extract information.
Signals are Laminar's natural-language analysis feature for finding patterns in traces. Users describe what they are looking for, define an output format, and Laminar extracts matching events from past and future traces. The supplied site content shows examples such as categorizing agent failures and returning structured details.
The public pricing page lists a Free tier with 1 GB of data, 1,000 Signals steps, 15-day retention, 1 project, 1 seat, and community support; a Hobby tier at $30/month with 3 GB data, 5,000 Signals steps, 30-day retention, unlimited projects and seats, and email support; a Pro tier at $150/month with 10 GB data, 50,000 Signals steps, 90-day retention, unlimited projects and seats, and Slack support; and custom Enterprise pricing with custom limits, on-premise deployment, unlimited projects and seats, and dedicated support. Teams should still confirm current limits and enterprise terms before buying.
Start with the free tier and upgrade when you need more features
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