## Connect to LangFuse

We need your API keys to access your datasets and evaluators.

Secret Key
Public Key
Host (optional)
Get your keys at cloud.langfuse.com. Use `https://us.cloud.langfuse.com` for US region.

## Select a Dataset

Choose the dataset to evaluate your target function against.

## Dataset Preview

Sample rows from your dataset.

## Source Files & Target

Select the files Weco will optimize, then choose the target function.

Source Files
Select the Python files that Weco will modify during optimization.
Target Function
Function (module:function)
e.g. `agent:run_chain` imports `run_chain` from `agent.py`

## Evaluators

Evaluators score your target function's outputs on each dataset example.

Evaluator Files
Select Python files containing your evaluator functions.

Code Evaluators
Space-separated. Use `module:function` for custom evaluators. Leave empty to default to your metric name ().
Managed Evaluators (LLM-as-a-Judge)
Managed evaluators run server-side in LangFuse using LLM-as-a-Judge. Enter the name of each evaluator as configured in your LangFuse dashboard under Evaluation → Evaluators.
Metric Function (optional)
A custom function that receives all aggregated evaluator scores as a dict and returns a single float. Use this to combine multiple evaluators into one composite metric (e.g. a weighted average).

## Run Settings

Configure optimization parameters. All fields have sensible defaults.

Optimization
Metric
The metric name your evaluator outputs
Goal
Steps
Optimization iterations (default: 100)
Model
LLM model. Leave blank for auto.
Additional Instructions
LangFuse
Summary Aggregation
Experiment Name
Max Concurrency
Parallel eval threads
Managed Evaluator Timeout
Seconds to poll for server-side LLM-as-a-Judge scores (default: 900)
Execution
Eval Timeout
Seconds per evaluation
Log Directory
Save execution logs and code snapshots
Auto-apply best solution
Require manual review of each change

## Review & Start

Confirm your configuration and begin optimization.

Next time, skip the wizard with:

✓

## Configuration Sent

Return to your terminal — optimization is starting.