Debugging and optimizing AI applications, particularly those using Large Language Models (LLMs), is challenging due to the complexity of tracing data flow, evaluating performance, and managing datasets. Existing tools often lack the integration and real-time capabilities needed for efficient development and monitoring.

Laminar provides an open-source platform designed to streamline the development and monitoring of AI applications. It offers tools for tracing, evaluating, and labeling LLM products, enabling teams to ship reliable AI solutions more efficiently. The platform allows developers to trace every execution step, gaining visibility into data flow and collecting valuable data for evaluations and fine-tuning. Laminar also supports online evaluations, allowing real-time assessment of LLM call results, and provides a playground for experimenting with prompts and models.

Automatic tracing of popular LLM SDKs and frameworks with minimal code integration

Real-time traces for immediate debugging and monitoring of AI workflows

Browser agent observability to record browser sessions and sync them with agent traces

LLM playground for experimenting with prompts and models

Tools for building datasets from span data for evaluations, fine-tuning, and prompt engineering

Online evaluations to assess LLM call results in real-time using code or LLMs as judges

SQL editor for advanced analytics and dataset creation

Fully open-source and self-hostable

Laminar is designed for AI developers and teams building applications using LLMs, including those working on agents, workflows, and other AI-powered products.

Laminar offers a tiered pricing model, including a free tier with limited data and features, as well as paid Hobby and Pro tiers with increased data allowances and team member support, and an Enterprise tier with custom pricing and dedicated support. The Hobby tier starts at $25/month and the Pro tier at $50/month.