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What is Laminar?

Laminar is an open-source observability platform designed specifically for long-running AI agents. Built by a team backed by Y Combinator, it solves the problem of debugging and understanding complex agent behaviors that span many steps and tool calls. Core capabilities include one-line tracing integration, a first-of-its-kind agent debugger that allows rerunning from any step with preserved context, and Signals for extracting custom events from traces at scale. It also offers session replay for browser agents, SQL querying across all platform data, and automated insights delivered via email or Slack. The platform fits workflows of AI engineers, agent developers, and teams building production-grade autonomous systems. Laminar supports popular frameworks like Browser Use, Stagehand, Playwright, and more, making it easy to adopt in existing stacks. Its open-source nature and generous free tier make it accessible for both individual developers and large teams.

AI Tool Review Summary

Performance Score

4.7/5

Content/Output Quality

High, consistent, and on-brand

Interface

Clean and minimal

AI Technology
LLMNLP
Purpose of Tool

Open-source observability and debugging for long-running AI agents.

Compatibility

Integrates with Python SDK, popular agent frameworks, and browser automation tools.

Pricing

Freemium with paid tiers

Features

Features with the highest value for users are highlighted here.

True Agent Debugger with step replay

AI-powered trace summarization and analysis

Signals for automated pattern detection

Session replay for browser agents

SQL querying across all platform data

One-line integration with AI frameworks

Open-source observability platform

Y Combinator backed

How It Works

1

Integrate Tracing

Add two lines of code to your agent to start tracing with Laminar's SDK.

2

Debug with Agent Debugger

Run your agent locally and debug in the browser, rerunning from any step with preserved context.

3

Analyze with Signals

Define custom events and let Laminar extract them from past and future traces at scale.

4

Query with SQL

Use the SQL editor to query all platform data and feed evaluations or datasets.

Who Is It For?

AI Engineers

Agent Developers

ML Teams

Startups

Enterprise Teams

QA Engineers

Product Managers

Open Source Contributors

Browser Automation Developers

Data Scientists

Pricing

Free

$0/free
  • 1GB data
  • 15-day retention
  • Community support

Hobby

$25/monthly
  • 2GB data
  • 30-day retention
  • Email support
Popular

Pro

$99/monthly
  • 10GB data
  • 90-day retention
  • Priority support
  • Advanced Signals

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Pros & Cons

Pros

  • Excellent debugging experience with step replay and context preservation.
  • Powerful Signals feature for automated anomaly detection at scale.

Cons

  • Pricing for higher tiers may be steep for small teams.
  • Documentation could be more comprehensive for advanced use cases.

FAQs

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Release History

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Rating

5.0

Pros

Cons

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