--- title: LangSmith Explained: Debugging and Evaluating LLM Agents description: LangSmith is a framework-agnostic platform for debugging, evaluating, and monitoring LLM-powered applications and agents. It provides end-to-end tracing that... canonical: https://daily.dev/posts/langsmith-explained-debugging-and-evaluating-llm-agents-wy5ceqqjb twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: LangSmith Explained: Debugging and Evaluating LLM Agents | daily.dev og:description: LangSmith is a framework-agnostic platform for debugging, evaluating, and monitoring LLM-powered applications and agents. It provides end-to-end tracing that... og:url: https://daily.dev/posts/langsmith-explained-debugging-and-evaluating-llm-agents-wy5ceqqjb og:image: https://api.daily.dev/og/posts/wy5CeqQjB.png og:image:alt: LangSmith Explained: Debugging and Evaluating LLM Agents og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # LangSmith Explained: Debugging and Evaluating LLM Agents **[DigitalOcean Community](https://daily.dev/sources/do_community)** · 16 min read · 1 upvotes · 0 comments ## Summary LangSmith is a framework-agnostic platform for debugging, evaluating, and monitoring LLM-powered applications and agents. It provides end-to-end tracing that captures every step of an agent's execution (LLM calls, tool invocations, prompts), enabling developers to diagnose failures like hallucinations, tool errors, and reasoning issues. The platform supports both offline evaluation using datasets with automated or human evaluators, and online monitoring in production. Key features include trace visualization, prompt playground for testing variants, annotation queues for human review (single-run and pairwise comparisons), and deployment capabilities. The tutorial walks through instrumenting agents with tracing via environment variables or SDK wrappers, creating evaluation datasets, running experiments with evaluators, and iterating on prompts based on results. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Debugging Deep Agents with LangSmith](https://daily.dev/posts/debugging-deep-agents-with-langsmith-v1oyf16pl) · LangChain · 1 upvotes · 0 comments --- Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#langchain](https://daily.dev/tags/langchain), [#langsmith](https://daily.dev/tags/langsmith), [#llm](https://daily.dev/tags/llm), [#observability](https://daily.dev/tags/observability) [View this post on daily.dev](https://daily.dev/posts/langsmith-explained-debugging-and-evaluating-llm-agents-wy5ceqqjb) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"LangSmith Explained: Debugging and Evaluating LLM Agents","url":"https://daily.dev/posts/langsmith-explained-debugging-and-evaluating-llm-agents-wy5ceqqjb","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/langsmith-explained-debugging-and-evaluating-llm-agents-wy5ceqqjb"},"datePublished":"2026-01-17T00:37:22.054Z","dateModified":"2026-06-04T02:13:18.038Z","description":"LangSmith is a framework-agnostic platform for debugging, evaluating, and monitoring LLM-powered applications and agents. 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