Evaluations (evals) are essential for deploying reliable LLM-powered applications, providing systematic methods to assess LLM output quality based on specific criteria. The newly introduced packages, openevals and agentevals, offer a set of evaluators and a framework to simplify the process of building evaluations from scratch. Evals involve two components: the data being evaluated and the metrics used for evaluation, both of which significantly impact the reflection of real-world usage. The packages focus on common evaluation types, including LLM-as-a-judge evals for natural language outputs and structured data evaluations for extracting or generating structured content. Additionally, agent evaluations assess the sequence of actions taken by an agent to complete tasks. Openevals and agentevals provide tools to customize evaluations, incorporate human preferences, and ensure consistency, while LangSmith offers capabilities for tracking and sharing evaluation results. Future plans include expanding the libraries with more specific evaluators and encouraging community contributions through GitHub.