--- title: Evaluating Large Language Models With OpenEvals | daily.dev description: LangChain introduces two new open-source packages — openevals and agentevals — designed to simplify LLM evaluation. The packages provide pre-built evaluators... canonical: https://daily.dev/posts/evaluating-large-language-models-with-openevals-ngkjlrlol twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Evaluating Large Language Models With OpenEvals | daily.dev og:description: LangChain introduces two new open-source packages — openevals and agentevals — designed to simplify LLM evaluation. The packages provide pre-built evaluators... og:url: https://daily.dev/posts/evaluating-large-language-models-with-openevals-ngkjlrlol og:image: https://api.daily.dev/og/posts/ngKjLRlOl.png og:image:alt: Evaluating Large Language Models With OpenEvals 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. # Evaluating Large Language Models With OpenEvals **[LangChain](https://daily.dev/sources/langchain)** · 5 min read · 0 upvotes · 0 comments ## Summary LangChain introduces two new open-source packages — openevals and agentevals — designed to simplify LLM evaluation. The packages provide pre-built evaluators covering three main use cases: LLM-as-a-judge for natural language outputs (chatbot quality, hallucination detection), structured data evaluation (JSON extraction, tool call validation), and agent trajectory evaluation (verifying tool selection sequences in LangGraph agents). Key features include customizable prompts, few-shot example support, scoring schemas with reasoning comments, and integration with LangSmith for tracking evaluation results over time. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Evaluating Agents via LLM-as-a-Judge in Langfuse](https://daily.dev/posts/evaluating-agents-via-llm-as-a-judge-in-langfuse-3scbxjmmw) · Niklas Heidloff · 1 upvotes · 0 comments - [How we Build Evals for Deep Agents](https://daily.dev/posts/how-we-build-evals-for-deep-agents-jnpktgn8x) · LangChain · 19 upvotes · 0 comments - [Evaluate LLM and agent quality in Dynatrace AI Observability with dt-evals](https://daily.dev/posts/evaluate-llm-and-agent-quality-in-dynatrace-ai-observability-with-dt-evals-dkozgp7zn) · Dynatrace · 1 upvotes · 0 comments --- Tags: [#llm](https://daily.dev/tags/llm), [#langchain](https://daily.dev/tags/langchain), [#langsmith](https://daily.dev/tags/langsmith) [View this post on daily.dev](https://daily.dev/posts/evaluating-large-language-models-with-openevals-ngkjlrlol) ```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":"Evaluating Large Language Models With OpenEvals","url":"https://daily.dev/posts/evaluating-large-language-models-with-openevals-ngkjlrlol","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/evaluating-large-language-models-with-openevals-ngkjlrlol"},"datePublished":"2026-07-08T19:22:57.316Z","dateModified":"2026-08-24T06:58:37.600Z","description":"LangChain introduces two new open-source packages — openevals and agentevals — designed to simplify LLM evaluation. 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