> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.

# How to add evaluators to an existing experiment (Python only)

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.

## Example

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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])
```

## Related topics

* [Retry failed examples in experiments](/langsmith/evaluate-with-retry)
* [Run an evaluation](/langsmith/evaluate-llm-application)
* [Run an evaluation asynchronously](/langsmith/evaluation-async)

***

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