Laminar GTM brain

category

market profile

• text: Long-running agents generate complex traces of model calls, tool use and sub-agents that are hard to debug manually.
• text: Agent teams need to find recurring failure patterns across production runs and feed them back into evaluation and fixes.

personas

• title: AI agent engineer
  pains:

  • A multi-step agent fails far into a run and the root cause is buried in traces
  • Reproducing browser and tool errors takes too long
• title: Engineering lead for an AI product
  pains:

  • Cannot quantify recurring failure modes across production traffic
  • Needs a repeatable path from incidents to evaluations before shipping changes
• title: Platform or infrastructure engineer
  pains:

  • Agent instrumentation must fit existing frameworks and telemetry
  • Data retention, security and costs become harder as trace volume grows

icp

• text: Software companies shipping multi-step or long-running AI agents to users, especially browser and tool-using agents.
• text: Engineering organizations requiring self-hosting, enterprise data controls or high-volume agent monitoring.

message pillars

• text: Make agents reliable by finding failures and helping teams fix them.
• text: Show the full execution in a readable trace.
• text: Surface unexpected failure modes instead of relying only on predefined checks.
• text: Cluster failures to understand their distribution.

objections

• objection: We already have logs and general application monitoring.
  response: Compare a representative agent run in both systems, including tool calls, sub-agents and clustered failures.
• objection: Will instrumentation require rewriting our agents?
  response: Laminar states its SDK auto-instruments many existing frameworks; verify support for the team's specific runtime in a pilot.
• objection: Will monitoring costs scale unpredictably?
  response: Use the published data and Signals rates to model actual trace sizes and analysis volume.

competitors

• name: Langfuse
  website: https://www.ycombinator.com/companies/langfuse
  why: Open-source LLM and agent tracing and evaluation platform.
• name: Arize
  why: AI observability platform competing for model and agent monitoring.
• name: Helicone
  why: LLM observability alternative for teams monitoring AI applications.

influencers

• name: Robert Kim
  why: Co-founder and CEO.
• name: Dinmukhamed Mailibay
  why: Co-founder and CTO.

keywords

• AI agent observability
• agent trace debugging
• LLM tracing open source
• AI agent failure detection
• agent evaluation platform
• browser agent replay
• OpenTelemetry LLM

voice

• Technical
• Direct
• Developer-oriented

signals

• text: A team is launching long-running, tool-using agents into production.
• text: Engineers are hiring for agent reliability or struggling to diagnose recurring failures across traces.
• text: An enterprise needs a self-hosted agent observability layer or more control over trace data.

stack

• OpenTelemetry
• Claude Agent SDK
• OpenAI Agents SDK
• LangChain
• Browser Use
• Playwright

pricing

motion

channels

• GitHub
• developer documentation
• SEO
• Discord
• X
• LinkedIn

confidence

Founders

• name: Robert Kim
  title: Founder/CEO
  role: CEO
  bio: Co-founder and CEO @ Laminar (lmnr.ai). Previously, I interned at Palantir where I built semantic search package which now powers many internal AI teams and worked on resource allocation engine at core infrastructure team. I also interned at Bloomberg where I scaled market tick processing pipeline by 10x to 10M ticks/s.
• name: Din Mailibay
  title: Founder/CTO
  role: CTO
  bio: Co-founder and CTO at Laminar (laminar.sh). Previously, I have worked at Amazon for 2 years building and scaling critical payments infrastructure. Before that, I've spent a year creating ML infrastructure for a drug discovery biotech startup in Korea.