LangSmith is a unified developer platform for debugging, testing, evaluating, and monitoring LLM applications, providing full observability into complex chains and agents to help ship reliable, production-grade AI products.
LangSmith is an observability and testing platform designed specifically for developers building applications with large language models (LLMs). It addresses the 'black box' problem by providing detailed, step-by-step traces of every run, showing exactly what prompts, tools, and model outputs were involved. Primarily serving AI engineers and MLOps teams, the platform enables them to debug failures, optimize for cost and latency, and rigorously test their applications against predefined datasets. Its unique value lies in its deep, native integration with the LangChain framework, providing a near zero-configuration observability experience for developers within that ecosystem. Beyond debugging, LangSmith facilitates a continuous improvement loop by allowing developers to collect human feedback, curate datasets from production runs, and monitor performance over time.
AI/ML engineers, data scientists, and software developers building, deploying, and maintaining applications that use Large Language Models (LLMs), particularly those using the LangChain framework.
Based on 0 reviews
2023
San Francisco, USA
Community
Up to 5,000 trace events per month. Includes core tracing, testing, and monitoring features for individuals and small teams.
Free
Pro
Includes a higher base limit of 10,000 trace events/mo, plus pay-as-you-go pricing for additional traces, evaluations, and feedback. Adds organization-level access controls and priority support.
$20/mo + usage
Enterprise
Custom pricing for large-scale deployments. Includes features like SSO, dedicated support, custom data retention policies, and options for private deployment.
Custom
Choose for a broader ML observability platform that supports traditional ML models alongside LLMs, ideal for teams wanting a single solution for all model types.
A good choice for teams already using W&B for experiment tracking, as its Prompts tool integrates LLM evaluation directly into their existing MLOps workflows.
The best option for organizations already standardized on Datadog for APM, allowing them to monitor LLMs within their existing observability platform.
An open-source and enterprise tool focused heavily on evaluating and refining LLM applications with a wide range of pre-built checks and visualizations.
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