API Documentation for the uncensored model

Base URL and authentication

Start by setting your base URL to HTTPS://api.storyuncensored.com/v1 and adding your API key to the Authorization header.

Sign up with Google or email to receive a key instantly. Use the header Authorization: Bearer YOUR_API_KEY for every request.

First request

Make a POST request to /v1/chat/completions with the model set to uncensored.

Send your system prompt and user messages to get started.

Copycurl https://api.storyuncensored.com/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "uncensored",
    "messages": [{"role": "user", "content": "Generate a summary of chapter five focusing on plot points"}]
  }'

Python SDK example

Use the official OpenAI Python SDK to interact with the API.

Set the base_url to our endpoint and the API key in your client configuration.

Copyfrom openai import OpenAI

client = OpenAI(base_url="https://api.storyuncensored.com/v1", api_key="YOUR_KEY")

resp = client.chat.completions.create(
    model="uncensored",
    messages=[{"role": "user", "content": "Summarise this thread without softening it."}],
)
print(resp.choices[0].message.content)

Node.js SDK example

Initialize the OpenAI client in Node.js with our base URL.

Pass the uncensored model ID to the chat.completions.create method.

Copyimport OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.storyuncensored.com/v1", apiKey: process.env.API_KEY });

const resp = await client.chat.completions.create({
  model: "uncensored",
  messages: [{ role: "user", content: "Draft a villain monologue for my game." }],
});
console.log(resp.choices[0].message.content);

Streaming responses with SSE

Enable streaming by setting stream: true in your request body.

Receive token-by-token updates via Server-Sent Events. The final chunk includes usage data.

Copystream = client.chat.completions.create(
    model="uncensored",
    messages=[{"role": "user", "content": "Tell the story in second person."}],
    stream=True,
)
for chunk in stream:
    if chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

Limits, errors, and context window

The context window supports 100,000 tokens total. Handle 401 for invalid keys, 402 for no credit, and 429 for rate limits.

Max output is 16,000 tokens per request. Rate limit is 300 requests per minute.