langsmith-evaluator

To build LLM-as-Judge evaluators for LangChain agent testing, you define custom evaluation functions that grade agent outputs and execution trajectories. This skill standardizes run function definition to capture outputs consistently for validation against expected results.

Validating agent trajectories in a LangSmith evaluation pipeline involves creating custom code evaluators that check if the execution path matches expected tool call sequences. This approach ensures structured grading of agent accuracy before production deployment.

Yes, you can use Python or TypeScript SDKs to create offline and online evaluation pipelines for LLM agents. Integration with the LangSmith ecosystem enables dataset-linked or project-linked evaluation runs to measure output quality and accuracy.

Yes, managing and deploying custom code evaluators requires integration with the LangSmith CLI. This setup facilitates the deployment of structured evaluation pipelines to validate LLM agent performance within your testing environment.

Yes, langsmith-evaluator supports grading agent responses against a golden dataset of expected answers. You can automatically evaluate customer support agents by comparing outputs with predefined expected data to ensure high accuracy.