The test cases SDK provides programmatic access to Gentrace test cases. While commonly used with evalDataset() for batch evaluations, test cases can also be accessed and managed independently.

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Basic usage

import { init, testCases } from 'gentrace';

init({
  apiKey: process.env.GENTRACE_API_KEY,
});

// List test cases from a dataset
const testCasesList = await testCases.list({ 
  datasetId: 'your-dataset-id' 
});

// Access the test cases
for (const testCase of testCasesList.data) {
  console.log(testCase.name);
  console.log(testCase.inputs);
}

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Overview

The SDK is built by Stainless and provides type-safe access to Gentrace entities. The testCases object exposes methods to list, create, update, and delete test cases.

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Test case structure

Each test case contains:
​
id
string
Unique identifier
​
name
string
Human-readable name for the test case
​
inputs
object
required
Dictionary/object containing the input data for your AI function
​
expectedOutputs
object
Optional expected outputs for validation
​
datasetId
string
required
UUID of the dataset this test case belongs to
​
createdAt
string
required
Creation timestamp
​
updatedAt
string
required
Last update timestamp

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Resource methods

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Create a test case

const testCase = await testCases.create({
  datasetId: 'your-dataset-id',
  inputs: { query: 'What is AI?' },
  name: 'Basic AI question',
  expectedOutputs: { answer: 'Artificial Intelligence is...' } // optional
});

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Retrieve a test case

const testCase = await testCases.retrieve('test-case-id');
console.log(testCase.inputs);

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Delete a test case

await testCases.delete('test-case-id');

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List with filters

// Filter by pipeline
const testCasesList = await testCases.list({
  pipelineId: 'pipeline-id',
  // or use pipelineSlug: 'pipeline-slug'
});

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Common usage with evalDataset()

Test cases are frequently used with evalDataset() for running batch evaluations: 
await evalDataset({
  data: async () => {
    const testCasesList = await testCases.list({ datasetId: DATASET_ID });
    return testCasesList.data;
  },
  interaction: yourAIFunction, // See interaction() docs
});
The interaction parameter should be a function wrapped with interaction() for proper OpenTelemetry tracing within experiments.

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See also

  • evalDataset() - Common usage pattern for batch evaluations
  • Datasets - Managing datasets that contain test cases