It is an all-in-one developer platform designed to support every phase of the lifecycle of LLM-powered applications, whether built with LangChain or not.
It is an all-in-one developer platform designed to support every phase of the lifecycle of LLM-powered applications, whether built with LangChain or not. LangSmith enables developers to debug, collaborate, test, and monitor their LLM applications, addressing the unique challenges posed by the non-deterministic nature of LLMs and unpredictable natural language inputs. It provides tools to reimagine traditional engineering practices for LLM development, ensuring applications meet quality and performance standards.
LangSmith offers full visibility into the sequence of LLM calls, allowing developers to identify errors and performance bottlenecks in real-time with precision. It supports debugging, experimentation, and iterative improvement until desired results are achieved. The platform fosters collaboration between developers and subject matter experts, ensuring applications are both technically sound and contextually accurate.
Key features include **Traces**, which allow users to share detailed chain traces with colleagues or clients for explainability; **Hub**, a tool for crafting, versioning, and commenting on prompts without requiring engineering expertise; **Annotation Queues**, which enable human feedback and labeling on traces; and **Datasets**, which help collect and construct datasets from production data or existing sources for evaluations, few-shot prompting, and fine-tuning.
LangSmith also supports human feedback and AI-assisted evaluations, using off-the-shelf or custom evaluators to assess relevance, correctness, harmfulness, and other criteria. Developers can save debugging and production traces to datasets, which serve as collections of exemplary or problematic inputs and outputs. The platform allows users to score application outputs using LLMs or custom functional tests and track performance changes over time.
For monitoring, LangSmith provides real-time insights into live applications, enabling developers to spot issues and ensure quality. It supports logging traces via Python SDK, TypeScript SDK, or API, and offers self-hosting options for enterprise customers, ensuring data remains within their environment. Traces are stored securely in GCP us-central-1, with logical separation and encryption in transit and at rest.
LangSmith does not add latency to applications, as traces are sent asynchronously via a distributed process. It guarantees data ownership and privacy, with no training on user data. Pricing details are available on the LangSmith website, with plans tailored to different needs. The platform is designed to help developers ship reliable GenAI applications faster, from prototype to production.
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