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Evaluation of existing experiments is currently only supported in the Python SDK. After running an experiment, you may want to add new evaluation metrics without re-running your application. This is useful when you’ve added new evaluators or want to apply different scoring criteria to existing results. Instead of re-executing your target function on all examples, you can evaluate the existing experiment traces directly. To add evaluators to an existing experiment, pass the experiment name or ID to evaluate() / aevaluate() instead of a target function. The evaluators will run on the cached traces from the original experiment, accessing the inputs, outputs, and any intermediate steps that were logged.

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Example

from langsmith import evaluate

def always_half(inputs: dict, outputs: dict) -> float:
    return 0.5

experiment_name = "my-experiment:abc"  # Replace with an actual experiment name or ID

evaluate(experiment_name, evaluators=[always_half])
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