Laminar.ai

An open-source full-stack platform that provides support for building top-tier LLM products.

PremiumNewProductProgrammingAI EngineeringLLM

Laminar is an open-source full-stack platform focused on AI engineering from first principles. It assists users in collecting, understanding, and leveraging data to enhance the quality of large language model (LLM) applications. Laminar supports tracking for text and image models and will soon support audio models. Key advantages of the product include zero-overhead observability, online evaluations, dataset construction, and LLM chain management. Laminar is fully open-source, easy to self-host, and is well-suited for developers and teams looking to build and manage LLM products.

Traces: Provides a clear view of each step executed by LLM applications while collecting valuable data.
Zero-overhead observability: All tracing is sent via gRPC in the background
resulting in minimal overhead.
Online evaluations: Allows for LLM-as-a-judge or Python script evaluators to assess each span received.
Datasets: Enables the construction of datasets from tracing
which can be utilized in evaluation
fine-tuning
and prompt engineering.
Prompt chain management: Facilitates the construction and hosting of complex chains
including hybrid or self-reflective LLM pipelines.

\Target audience includes developers
data scientists
and AI engineers who need to build
test
and optimize large language model products. Laminar provides a comprehensive suite of tools to enhance efficiency and effectiveness throughout the entire process from data collection to model optimization.\

Developers use Laminar to track their LLM applications
collecting execution data to optimize model performance.
Data scientists automate the evaluation process using Laminar's online evaluation features
enhancing assessment efficiency.
AI engineers leverage Laminar to build complex LLM pipelines

1. Visit the Laminar GitHub page and clone the repository.
2. Navigate to the cloned directory and start the service using Docker Compose.
3. Access the Laminar documentation to learn how to initialize projects and get started.
4. Utilize Laminar's tracing feature to collect application data.
5. Set up online evaluations based on the collected data to automate the assessment process.

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