Problem

AI agents are difficult to operate because a single failed outcome can depend on a long, opaque chain of model calls, tool invocations, browser actions, and changing context. At production scale, teams may need to inspect thousands of runs before they can identify which trajectories failed or why, making debugging slow and operationally expensive. The problem is especially acute for browser agents: a textual trace may not show what the agent actually saw, so developers need synchronized visual and event-level evidence to reproduce failures.

Laminar’s central use case is helping teams understand and fix failures in complex, long-running agents. Its browser-agent observability capability acts like a flight recorder, combining the browser session recording with the agent trace so developers can replay what happened rather than rely on incomplete logs or guesswork.

Product / Service

Laminar is an open-source, OpenTelemetry-native observability and evaluation platform for AI agents and LLM applications. Its SDK can auto-instrument common AI frameworks and SDKs, capture inputs and outputs, model and provider information, latency, token usage, pricing, and metadata, then present the resulting traces for live inspection, replay, search, and debugging. Developers can also use dashboards and SQL queries across traces, spans, and events to find patterns and build operational signals.

The platform connects production observability to evaluation: teams can turn real production traces into datasets, annotate them, and run evaluations against them. Laminar offers both self-hosted deployment and a managed platform, with the open-source system designed for high-volume ingestion through Rust-based services, trace compression, realtime viewing, and fast search. The practical benefit is a shorter path from “the agent failed” to identifying the failing step, reproducing it, and measuring whether a fix works.

Market

Laminar competes in the emerging LLM and AI-agent observability market, adjacent to application performance monitoring but focused on model calls, tool use, agent trajectories, evaluations, and the cost and quality of AI behavior. The competitive set includes open-source and commercial products such as Langfuse, LangSmith, Arize Phoenix, Helicone, Braintrust, Weave, and Agenta. Laminar differentiates around long-running and browser-based agents, open-source deployment, SQL-level access to underlying data, and a unified trace-to-evaluation workflow.

The company was founded in 2024, joined Y Combinator’s Summer 2024 cohort, and announced a $3 million seed round in March 2026. Its stated production users include Browser Use, OpenHands, Rye.com, Alai, and LegionIntel; Laminar is also described as Browser Use’s default observability platform. Reported usage includes more than one billion recorded events, more than 50 billion event reads, and ingestion of hundreds of gigabytes daily across millions of traces. The available research does not disclose revenue, so revenue status cannot be determined, but the production deployments, funding, and usage metrics indicate that the company is beyond a merely pre-launch stage.