LangSmith

LangSmith is a developer platform for a new type of application. It offers features like observability, testing, evaluation, and monitoring tools for complex LLM (Language Model) apps. The platform provides a flexible and agnostic open-source SDK that allows easy integration and adaptation to different implementations. With LangSmith, developers can add observability and testing to their LLM apps, enabling them to visualize inputs and outputs at each step in the chain. This helps them understand the behavior of LLMs and build intuition for creating more sophisticated applications. The platform also facilitates unit testing for LLM applications, allowing developers to spin up test datasets, run their applications, and inspect results within the LangSmith environment. It supports features like dataset curation, chain performance comparison, AI-assisted evaluation, collaboration, and adherence to best practices. Moreover, LangSmith provides mission-critical observability by offering application-level usage stats, feedback collection, filtered traces, and cost and performance measurement. This helps developers monitor and understand the behavior of their applications in real-time, especially given the stochastic nature of LLMs. LangSmith aims to help developers build and deploy LLM applications with confidence. It not only offers a set of tools but also establishes best practices for developers to rely on. The platform is suitable for open-source contributors, community members, and software engineers working on LLM applications. Access to LangSmith is available through sign-up for the beta version or by filling out a form for early access for open-source contributors and community members.

Tags: apps, development, LLM

Category: apps

Pricing: free

LangSmith
apps

LangSmith

LangSmith is a developer platform for a new type of application. It offers features like observability, testing, evaluation, and monitoring tools for complex LLM (Language Model) apps.

The platform provides a flexible and agnostic open-source SDK that allows easy integration and adaptation to different implementations.

View more details in the About section below...

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LangSmith is a developer platform for a new type of application. It offers features like observability, testing, evaluation, and monitoring tools for complex LLM (Language Model) apps.

The platform provides a flexible and agnostic open-source SDK that allows easy integration and adaptation to different implementations.

With LangSmith, developers can add observability and testing to their LLM apps, enabling them to visualize inputs and outputs at each step in the chain.

This helps them understand the behavior of LLMs and build intuition for creating more sophisticated applications.

The platform also facilitates unit testing for LLM applications, allowing developers to spin up test datasets, run their applications, and inspect results within the LangSmith environment.

It supports features like dataset curation, chain performance comparison, AI-assisted evaluation, collaboration, and adherence to best practices.

Moreover, LangSmith provides mission-critical observability by offering application-level usage stats, feedback collection, filtered traces, and cost and performance measurement.

This helps developers monitor and understand the behavior of their applications in real-time, especially given the stochastic nature of LLMs.

LangSmith aims to help developers build and deploy LLM applications with confidence.

It not only offers a set of tools but also establishes best practices for developers to rely on.

The platform is suitable for open-source contributors, community members, and software engineers working on LLM applications.

Access to LangSmith is available through sign-up for the beta version or by filling out a form for early access for open-source contributors and community members.

Key Benefits

Observability for LLM apps
Testing for LLM apps
Open
source SDK
Flexible integration
Adaptable to different implementations
App
level usage stats
Real
time behavior monitoring
Stochastic nature of LLMs
Unit testing facilitation
Test datasets creation
Chain performance comparison
Collaboration facilitation

Use Cases

apps
development
LLM

Highlights

Observability for LLM apps
Testing for LLM apps
Open
source SDK

Tags

apps
development
LLM