Laminar - Observability and Application Monitoring Tool

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Laminar

Open-source platform to trace, evaluate, and improve AI agents. Debug LLM calls, track tool use, and run evaluations on your AI applications.

Founded by: Robert Kim

You can use Laminar to monitor and debug AI agents by tracing their execution, evaluating their performance, and analyzing failures. It provides full observability into agent behavior, captures browser recordings for web agents, extracts insights from traces using AI, and runs evaluations to catch regressions. You can query all platform data with SQL, create custom dashboards, and identify patterns in agent failures to improve performance.

Use Cases

Debug LLM calls in production AI applications
Track tool usage patterns in AI agents
Evaluate agent performance with custom metrics
Identify why web automation agents fail
Monitor token usage and latency across models
Analyze conversation flows in chatbots

Standout Features

Two-line integration with AI frameworks
Browser screen recording for web agents
AI-powered trace analysis and debugging
Full context trace visualization
Automatic trace clustering by behavior
SQL queries across all platform data

Tasks it helps with

Trace AI agent execution step-by-step
Debug failed LLM calls with full context
Evaluate agent accuracy with custom metrics
Record browser interactions automatically
Cluster traces by failure patterns
Query trace data with SQL commands

Who is it for?

AI Engineer, Machine Learning Engineer, Software Engineer, AI Research Scientist, Data Scientist, Full-Stack Developer, DevOps Engineer, Product Manager

Overall Web Sentiment

People love it

Time to value

Quick Setup (< 1 hour)
Laminar, AI agent observability, agent tracing, LLM debugging, AI evaluation, agent monitoring, trace analysis, AI agent performance, observability platform, agent development, AI debugging, trace visualization, AI metrics, agent failures, evaluation framework, AI testing, agent analytics
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