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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.
4.7/5
High, consistent, and on-brand
Clean and minimal
Open-source observability and debugging for long-running AI agents.
Integrates with Python SDK, popular agent frameworks, and browser automation tools.
Freemium with paid tiers
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
AI Engineers
Agent Developers
ML Teams
Startups
Enterprise Teams
QA Engineers
Product Managers
Open Source Contributors
Browser Automation Developers
Data Scientists
Free
Hobby
Weekly intelligence on AI strategy, operations, and market shifts. No noise. No narrative. Direct to your inbox.
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