> ## Documentation Index
> Fetch the complete documentation index at: https://docs.auriko.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Overview
> Create model responses using the OpenAI Response API format through Auriko
`POST /v1/responses` accepts a string or structured input and returns typed output items instead of a messages array. Auriko routes these requests across providers, and routing features (multi-model, cost optimization, and extensions) work with both endpoints. If you're building with the OpenAI SDK's `client.responses.create()`, use this endpoint.
The Response API is in preview. The interface may change before GA.
## Prerequisites
* An [Auriko API key](https://auriko.ai/signup?redirectTo=%2Fdashboard%3Ftab%3Dapi-keys)
* Python 3.10+ with the OpenAI SDK (`pip install openai`) or the Auriko SDK (`pip install auriko`)
* OR Node.js 18+ with the OpenAI SDK (`npm install openai`) or `@auriko/sdk` (`npm install @auriko/sdk`)
## Send requests
Send a request and read the output text:
```python Python OpenAI theme={null}
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["AURIKO_API_KEY"],
base_url="https://api.auriko.ai/v1"
)
response = client.responses.create(
model="gpt-4o",
input="What is the capital of France?"
)
print(response.output_text)
```
```typescript TypeScript OpenAI theme={null}
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.AURIKO_API_KEY,
baseURL: "https://api.auriko.ai/v1",
});
const response = await client.responses.create({
model: "gpt-4o",
input: "What is the capital of France?",
});
console.log(response.output_text);
```
```python Python Auriko theme={null}
import os
from auriko import Client
client = Client(
api_key=os.environ["AURIKO_API_KEY"],
base_url="https://api.auriko.ai/v1"
)
response = client.responses.create(
model="gpt-4o",
input="What is the capital of France?"
)
print(response.output_text)
```
```typescript TypeScript Auriko theme={null}
import { Client } from "@auriko/sdk";
const client = new Client({
apiKey: process.env.AURIKO_API_KEY,
baseUrl: "https://api.auriko.ai/v1",
});
const response = await client.responses.create({
model: "gpt-4o",
input: "What is the capital of France?",
});
console.log(response.output_text);
```
```bash cURL theme={null}
curl https://api.auriko.ai/v1/responses \
-H "Authorization: Bearer $AURIKO_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-4o",
"input": "What is the capital of France?"
}'
```
## Check model support
Chat models work with both `/v1/chat/completions` and `/v1/responses`. Some models are only available via the Response API — OpenAI pro-tier models such as `gpt-5.5-pro` and `o3-pro` — and chat-format requests to them return [`response_api_only`](/errors/response_api_only).
To check endpoint support for a model, read `supported_endpoints` on each provider entry in the [model directory](/api-reference/model-directory), or list every Response-API-callable model directly:
```bash theme={null}
curl "https://api.auriko.ai/v1/models?endpoint=responses" \
-H "Authorization: Bearer $AURIKO_API_KEY"
```
## Map parameters
If you're migrating existing Chat Completions code, use this table to translate parameter names:
| Concept | Chat Completions | Response API |
| ----------------- | -------------------------------------------------- | ------------------------------------------------- |
| Input | `messages` array | `input` (string or items) |
| System prompt | `messages[0].role: "system"` | `instructions` parameter |
| Output | `choices[].message` | `output` items array |
| Output text | `choices[0].message.content` | `output_text` |
| Structured output | `response_format` | `text.format` |
| Reasoning | `reasoning_effort` top-level | `reasoning.effort` nested |
| Tool definition | `{type: "function", function: {name, parameters}}` | `{type: "function", name, parameters}` |
| Tool results | `{role: "tool", tool_call_id, content}` | `{type: "function_call_output", call_id, output}` |
| Usage fields | `prompt_tokens` / `completion_tokens` | `input_tokens` / `output_tokens` |
## Check feature support
| Feature | Status |
| ---------------------- | -------------------------------------------------------------------------------------------------------------------------- |
| Text completions | Supported |
| Streaming | Supported (18 SSE event types) |
| Tool calling | Supported (function tools) |
| Structured output | Supported (`json_schema`, `json_object`) |
| Vision | Supported (image URLs in input) |
| Multi-model routing | Supported (`gateway.models`) |
| Prompt caching | Supported (`prompt_cache_key`) |
| Token logprobs | Supported (`top_logprobs`) |
| Reasoning | Supported (`reasoning.effort`); [summary availability varies by model](/response-api/reasoning#access-reasoning-summaries) |
| `store` | Returns 400 `operation_not_allowed` |
| `previous_response_id` | Returns 400 `operation_not_allowed` |
| File/audio inputs | Returns 400 `operation_not_allowed` |
| Background execution | Returns 400 `operation_not_allowed` |
## Resources
Stream events as they're generated
Call functions with the input/output item format
Constrain output to a JSON Schema
Control reasoning effort and access summaries
Multi-model routing and provider extensions
Full endpoint specification
Python SDK reference for responses
TypeScript SDK reference for responses