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The endpoint is the OpenAI Responses API at /v1/responses, so any OpenAI-compatible client works after you change two settings: the base URL and the key. This walkthrough runs one realistic task end to end: classifying an inbound support ticket and drafting a reply. You send it as a background job, then retrieve the result once Valar finishes. The same pattern scales from this single call to the thousands of concurrent requests an agent fans out at runtime.
For AI agents — if you are an agent migrating an existing app to Valar, follow the condensed instructions in Migrating to Valar. These docs are machine-readable: see LLM & agent access.
1

Create an API key

Sign up at the Valar Dashboard — new workspaces are approved before activation — then create an API key from the dashboard.
2

Point at Valar

Keep your existing OpenAI client. Change two settings:
  1. Set the base URL to https://api.valarhq.ai/v1
  2. Pass the API key as a bearer token.
https://api.valarhq.ai/v1 is the global endpoint, which geo-steers to the nearest region automatically. To pin a specific region, use a regional endpoint instead — see Geo Control.
3

Dispatch the task in the background

Setting background returns a response id immediately rather than holding the connection open. For one ticket this is convenient; across a queue of them it is what lets the work run concurrently. Use a model from the Models page - here, zai-org/GLM-5.2.
4

Retrieve the result

The create call hands back a response id and a status of queued or in_progress. Retrieve that id until it reaches completed, then read output_text. In production you can replace this poll loop with a webhook so you aren’t holding a thread per job.

Going further

A single triaged ticket is the unit; an agent is many of them in a loop. From here: Questions about a specific workload can go to [email protected].