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- LangSmith - LangChain
LangSmith is a unified observability evals platform where teams can debug, test, and monitor AI app performance — whether building with LangChain or not
- Get started with LangSmith | ️ ️ LangSmith
Get started with LangSmith LangSmith is a platform for building production-grade LLM applications It allows you to closely monitor and evaluate your application, so you can ship quickly and with confidence
- LangSmith Pricing - LangChain
LangSmith pricing for teams of any size Choose the plan that suits your needs, whether you're an individual developer or enterprise Debug, test, and monitor your LLM apps confidently
- Evaluation concepts | ️ ️ LangSmith - LangChain
LangSmith makes building high-quality evaluations easy This guide explains the LangSmith evaluation framework and AI evaluation techniques more broadly The building blocks of the LangSmith framework are: Datasets: Collections of test inputs and reference outputs Evaluators: Functions for scoring outputs Datasets
- Harden your application with LangSmith evaluation - LangChain
Testing needs to happen continuously for any live application LangSmith helps you monitor not only latency, errors, and cost, but also qualitative measures to make sure your application responds effectively and meets company expectations
- Concepts | ️ ️ LangSmith - LangChain
This conceptual guide covers topics that are important to understand when logging traces to LangSmith A Trace is essentially a series of steps that your application takes to go from input to output Each of these individual steps is represented by a Run A Project is simply a collection of traces
- LangSmith
Create your LangSmith account to access tools for building, testing, and improving LLM applications with LangChain and other frameworks
- Evaluation Quick Start | ️ ️ LangSmith - LangChain
Because this quickstart uses OpenAI models, you'll need to set the OPENAI_API_KEY environment variable as well as the required LangSmith ones: Shell export LANGSMITH_TRACING=true
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