Apps 2023-07-18
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LangSmith

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Observability & testing for complex LLM applications.
Generated by ChatGPT

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.

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LangSmith was manually vetted by our editorial team and was first featured on August 21st 2023.
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Pros and Cons

Pros

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
Best practices adherence
Cost performance measurement
Feedback collection
Filter traces features
Beta version availability
Early access for contributors

Cons

Limited to LLM applications
Restricted early access
Beta version risks
Relies on flexible adaptation
No standalone testing environment
Stochastic nature uncertainty
Requires explicit integration
Dataset curation needed
Observability depends on adaptation
No mention of cross-platform compatibility

Q&A

What is LangSmith?
What are the key features of LangSmith?
How can LangSmith help with observability and testing of LLM apps?
What is meant by 'LLMs' in the context of LangSmith?
How easy is it to integrate LangSmith into current implementations?
How does LangSmith help visualize inputs and outputs of applications?
Can LangSmith facilitate unit testing for applications?
What benefits does LangSmith offer with its dataset curation feature?
How does LangSmith support the comparison of chain performance?
What is the usefulness of AI-assisted evaluation in LangSmith?
How does LangSmith allow for real-time monitoring and understanding of application behavior?
What benefits does LangSmith offer with application-level usage stats and feedback collection?
How can developers obtain access to LangSmith?
What is unique about LangSmith's open-source SDK?
How does LangSmith adapt to different implementations?
Can LangSmith facilitate collaboration among developers?
What best practices does LangSmith provide for developers?
How can LangSmith help improve the performance measurement of applications?
What resources does LangSmith offer for learning more about the platform?
Why would I want to switch to LangSmith from building testing and monitoring tools in-house?

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