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

# PydanticAI integration counts wrapper spans as LLM calls

export const plans_0 = "Any"

export const deployments_0 = "Braintrust-hosted"

export const data_plane_version_0 = undefined

export const use_case_0 = "Use case - PydanticAI agents with `setup_pydantic_ai()` integration"

<Note>
  **Applies to:**

  * Plan - {plans_0}
  * Deployment - {deployments_0}
  * {data_plane_version_0}
  * {use_case_0}
</Note>

## Summary

**Issue:** When using `setup_pydantic_ai()`, Braintrust incorrectly marks internal wrapper spans as type "llm", causing a single API call to appear as 4 separate LLM calls in metrics and dashboards.

**Cause:** PydanticAI creates nested spans for streaming, agent execution, fallback handling, and the actual API call - all are marked as type "llm" instead of distinguishing wrapper spans from actual API calls.

**Resolution:** Filter queries to count only actual LLM API calls by model name patterns, excluding internal wrapper spans.

## Resolution Steps

### Step 1: Filter queries for accurate LLM counts

When querying LLM metrics in dashboards or reports, filter to count only actual API calls by model name patterns.

```javascript theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}}
span_attributes.type = 'llm' AND (
  span_attributes.name LIKE 'chat gpt%' OR
  span_attributes.name LIKE 'chat gemini%' OR
  span_attributes.name LIKE 'chat claude%'
)

```

### Step 2: Apply filters to cost calculations

Use the same filtering pattern when calculating costs or token usage to ensure accurate metrics based on actual API calls rather than wrapper spans.

### Step 3: Update dashboard queries

Modify existing dashboard queries and alerts to use the filtered approach to prevent inflated LLM call counts in monitoring and reporting.