Laminar — LLM Observability Tool

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What is Laminar?

Laminar is an open-source platform for tracing, evaluating, and labeling LLM products. It offers tracing with minimal code integration, runs prompt evaluations for quality testing, and organizes labeled data into datasets to help LLM engineers and development teams build more reliable AI applications.

What sets Laminar apart?

Laminar differentiates itself with semantic retrieval capabilities that allow LLM engineers to dynamically find and incorporate the most relevant examples into their prompts rather than hard-coding them. This dynamic approach helps development teams build more accurate AI applications by ensuring the examples guiding their LLMs are contextually appropriate to each specific input. Laminar's pipeline builder further enables rapid prototyping of both simple prompts and complex LLM chains without requiring extensive coding knowledge.

Laminar Use Cases

LLM application monitoring
Prompt chain management
Model output evaluation
Training data labeling

Who uses Laminar?

Machine Learning Engineers Research Scientists Software Engineers Data Scientists Software Developers

Features and Benefits

Framework-Agnostic Tracing
Trace entire LLM application executions with minimal code changes, automatically capturing data from popular LLM frameworks and SDKs.
Evaluation Automation
Run LLM-as-judge evaluations and Python-based evaluators to test prompts and measure performance through both offline and online methods.
Data Labeling and Analytics
Label and annotate traces for measuring human agreement, organizing labeled data into datasets for prompt updates and model fine-tuning.
Prompt Chain Management
Build and host chains of prompts and LLMs that function as single callable operations for experimentation with advanced techniques.
Semantic Retrieval
Find semantically similar examples from indexed datasets to create dynamic few-shot examples that improve prompt performance and task alignment.