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.
Funding
$500K 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.
- Startup funding source · Source unavailable
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