About Laminar

Laminar provides an open-source platform for observability, analytics, evaluations, and chain management, enabling organizations to monitor and analyze their data flows effectively. This platform addresses the challenges of data visibility and management in complex systems, enhancing operational efficiency and decision-making.

```xml <problem> 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. </problem> <solution> 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. </solution> <features> - 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 </features> <target_audience> Laminar is designed for AI developers and teams building applications using LLMs, including those working on agents, workflows, and other AI-powered products. </target_audience> <revenue_model> 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. </revenue_model> ```

What does Laminar do?

Where is Laminar located?

Laminar is based in San Francisco, United States.

When was Laminar founded?

Laminar was founded in 2024.

How much funding has Laminar raised?

Laminar has raised $500.0K.

Who founded Laminar?

Laminar was founded by Dinmukhamed Mailibay and Robert Kim.

Dinmukhamed Mailibay - Co-Founder
Robert Kim - Founder

Location

San Francisco, United States

Founded

2024

Funding

$500.0K

Employees

2 employees

Investors

Y Combinator

Laminar

San Francisco, United StatesFounded 202421K+ followers10/10 TractionRelative Traction Score based on online presence metrics compared to companies in the same age group.

Updated 18 months ago

Funding

$500.0K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.

Funding rounds are not available yet.

Founders

Product

Problem

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.

Solution

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.

Target Audience

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

Features

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