Reliability
OpenAI background response polling without client loops
Let LLM Proxy own OpenAI Responses background mode and return the final answer in one REST call.
Backend teams with long OpenAI prompts that should not require client polling logic.
The problem
Long OpenAI Responses work can push polling, resume tokens, or streaming complexity into every caller if the gateway does not own the lifecycle.
How LLM Proxy helps
LLM Proxy sends stored OpenAI background requests upstream and polls server-side until the answer is terminal or the configured request deadline expires.
How it works
- Call GET, POST, or /v2 as a normal blocking proxy request.
- Let the OpenAI adapter use background: true and store: true internally.
- Wait for the same HTTP response to return the final formatted answer.
- Treat 504 as the proxy deadline expiring, not as a prompt to poll llm-proxy.
Feature-to-benefit table
| Feature | Why it matters here | Example |
|---|---|---|
| One-shot REST contract | Clients do not stream, poll, or follow resume endpoints. | The final answer arrives in the original response. |
| Server-side polling | The backend polls stored OpenAI response IDs internally. | Provider lifecycle details stay out of product code. |
| Timeout ownership | Each request can select a bounded proxy work budget; omission uses server.request_timeout_seconds. | A budget expiry becomes a canonical 504. |
Use-case examples
Semantic review
A workflow sends a long JSON-only review prompt and waits on one proxy request.
CLI caller
The bundled CLI can stay a simple v2 transport without polling support.
Backend integration
A service keeps its existing synchronous request path while the proxy handles OpenAI background work.
Objections and limitations
- The client still blocks until the proxy returns or times out.
- OpenAI-specific background behavior does not imply other providers support the same upstream mode.
- Very long provider work must fit within the configured request timeout.
FAQ
What is the main job of OpenAI background response polling?
LLM Proxy sends stored OpenAI background requests upstream and polls server-side until the answer is terminal or the configured request deadline expires.
Who should read this reliability resource?
Backend teams with long OpenAI prompts that should not require client polling logic.
Does this page claim provider performance or pricing advantages?
No. The supported claim is about LLM Proxy's documented routing, configuration, management, security, usage, and deployment contracts. Provider cost, speed, rankings, and benchmark claims are not made here.
Where should setup details come from?
Use the main README and implementation notes for current command, config, and endpoint details. This page summarizes the workflow without replacing LLM Proxy documentation.
What should I read next?
A closely related resource is Upstream worker and queue limits for LLM traffic, which covers upstream worker queue limits.
Related resources
Use this pattern in LLM Proxy
Start from the canonical API reference, then use the management surface when the workflow needs tenant or provider configuration.
Open API reference