Your agent ran for 40 minutes and failed. You spend 20 minutes digging through logs, but still have no clue what went wrong. Now multiply that by a thousand runs in production, and you have a second problem: you don’t even know which runs are failing, or what patterns are emerging across them.
We built Laminar to fix this. Today we’re announcing our $3M seed round led by Atlantic.vc, with participation from Y Combinator, AAL.vc, and angels including Ben Sigelman (co-creator of OpenTelemetry) and Ant Wilson (CTO of Supabase).
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Where existing tools fall short
Most observability platforms are, at their core, data collectors. They ingest traces and metrics, store them, and then leave it entirely up to you to figure out what’s going on. You get a tree of spans. You click into one, read it, go back, click the next one, try to hold the context in your head. For an agent that ran for 30 minutes and made 200 decisions, this simply doesn’t work.
What Laminar does
Instead of just collecting data and leaving you to make sense of it, Laminar actively helps you understand what happened in a single run, reproduce and fix it, and detect what’s going wrong across thousands of runs in production.
Tracing that actually helps you understand what happened.
With a single line of code, Laminar SDK auto-instruments the vast majority of AI frameworks and SDKs, including Claude Agent SDK, AI SDK, LiteLLM, Browser Use, Stagehand, OpenHands SDK, and more.
Laminar’s tracing UI gives you as much information as quickly as possible. Our trace timeline and reader mode lay out the agent’s reasoning and actions as a clean, readable feed, instead of tree of spans. For browser agents, we record full browser sessions synced with traces so you can see exactly what the agent saw at every step. If a trace is too complex to parse visually, you can chat with it in natural language instead of manually digging through hundreds of steps.
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