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

# LLM spans with tools (Agentic)

> Train a Luna metric that evaluates tool-aware assistant behavior.

Use this tutorial when your metric depends on the available tools, the chat history, and the assistant action or response. This is the standard pattern for agentic tool-use metrics.

## Dataset schema

Required columns:

* `tools`: the available tool definitions or tool context
* `input`: the chat history or user context
* `output`: the assistant action or response
* `label`: the ground-truth class for the metric

## Config shape

Set:

* `data_generation.metric.input_format: "span_with_tools"`
* `data_generation.source_data.dataset.columns.features: ["tools", "input", "output"]`
* `generation.context_examples: 1`

## Minimal end-to-end config

```yaml theme={null}
run_steps:
  - data_generation
  - training

pipeline_provider: "local"
metric_name: "custom"

data_generation:
  metric:
    name: "Tool Selection Quality"
    type: "binary"
    input_format: "span_with_tools"
    llmaj_source_prompt: "Determine whether the bot's tool selection decision follows proper guidelines given the chat history and available tools."
  source_data:
    dataset:
      source_type: "huggingface"
      huggingface:
        name: "tool-selection-quality-dataset"
  generation:
    context_examples: 1
  output:
    dataset:
      repo_name: "tool-selection-quality-training-dataset"

training:
  dataset:
    name: "tool-selection-quality-training-dataset"
  output:
    model_name: "tool-selection-quality-model"
```