LangSmith is a comprehensive platform designed to help teams debug, test, and monitor the performance of AI applications, particularly those built with LangChain. It offers a suite of tools for tracing, evaluating, and iterating on AI models, making it easier to ensure the reliability and quality of AI-driven applications.
Key Features
- Agent Observability: Quickly debug and understand non-deterministic LLM app behavior with detailed tracing. See what your agent is doing step by step to improve latency and response quality.
- Performance Evaluation: Evaluate your app by saving production traces to datasets and scoring performance with LLM-as-Judge evaluators. Gather human feedback to assess response relevance, correctness, harmfulness, and other criteria.
- Prompt Engineering: Experiment with models and prompts in the Playground, compare outputs across different prompt versions, and collaborate on prompt improvements.
- Business Monitoring: Track business-critical metrics like costs, latency, and response quality with live dashboards. Get alerted when problems arise and drill into root causes.
- Flexibility: Works with or without LangChain, offers hybrid and self-hosted deployment options, and is API-first and OTEL-compliant to complement existing DevOps investments.
Use Cases
- Debugging AI Applications: Identify and fix issues in AI app behavior quickly.
- Performance Testing: Ensure AI models meet performance standards through comprehensive evaluation.
- Collaborative Development: Facilitate collaboration across teams for prompt engineering and feedback collection.
- Business Insights: Monitor key metrics to maintain high performance and reliability of AI applications.
LangSmith is designed for flexibility and can be integrated into various environments, making it a valuable tool for developers, data scientists, and business analysts working with AI technologies.
