The model export is particularly valuable for cost analysis and optimization. You can use this data to:
Identify the most expensive models in your usage
Track cost trends over time
Optimize model selection for different use cases
Budget for future AI model usage
Additional details on prerequisites, rate limits, and export size limits are in the main Usage Export API documentation: https://docs.langdock.com/api-endpoints/usage-export/intro-to-usage-export-api
1
Request
POST /export/models
Description: Export model usage data.
Example request body (application/json):
2
Headers
Authorization (string, required)
API key as Bearer token with USAGE_EXPORT_API scope. Format: "Bearer YOUR_API_KEY"
Content-Type: application/json
3
Response (200)
Successful response fields:
success (boolean) — indicates if the export was successful. Example: true
data (object)
data.filePath (string) — Path to the generated export file. Example: "assistants-usage/workspace-id/assistants-usage-2024-01-01-2024-01-31-abc12345.csv"
data.downloadUrl (string) — Signed URL to download the export file. Example: "https://storage.example.com/signed-url"
data.dataType (enum) — Type of data exported. Options: assistants, users, workflows, projects, models. Example: "assistants"
data.recordCount (integer) — Number of records in the export. Example: 1250
data.dateRange (object)
data.dateRange.from (string) — Start date of the export. Example: "2024-01-01T00:00:00.000Z"
data.dateRange.to (string) — End date of the export. Example: "2024-01-31T23:59:59.999Z"