LangSmith is a unified observability & evals platform. It aims to help teams debug, test, and monitor AI app performance — whether building with LangChain or not.

Key Features:

• Agent observability for finding failures fast
• Tracing to debug non-deterministic LLM app behavior
• Performance evaluation with LLM-as-Judge evaluators
• Prompt experimentation and collaboration in the Playground
• Live dashboards for tracking business-critical metrics
• Hybrid and self-hosted deployment options

Use Cases:

• Debugging AI applications to improve latency and response quality
• Evaluating app performance with production traces and human feedback
• Collaborating on prompt design and improvement across teams
• Monitoring costs, latency, and response quality with live dashboards
• Building GenAI apps with involvement from PMs to subject matter experts

Benefits:

• Improved understanding of complex LLM app behavior
• Faster debugging and issue resolution
• Enhanced collaboration across development and non-development teams
• Flexibility with API-first and OTEL-compliant design
• Support for both LangChain and non-LangChain applications