Uncensored Chat Completions Quickstart

Authentication and Base URL

Configure your client with the provided API key grok and point it to our base URL. Set base_url to https://api.apikeygrok.com/v1 and pass your key in the Authorization header. This setup works with any OpenAI-compatible SDK without modifying the rest of your code.

Sending Your First Request

Make a standard chat completion call using the uncensored model ID. The endpoint accepts text-in and returns text-out with no content filters for lawful adult content. Send your prompt and receive the raw response directly.

Copycurl https://api.apikeygrok.com/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "uncensored",
    "messages": [{"role": "user", "content": "List the current system time in UTC"}]
  }'

Python SDK Integration

Use the official openai Python package to interact with the API. Initialize the client with your key and the correct base URL, then call chat.completions.create(). This approach requires zero changes to your existing logic if you are migrating from another provider.

Copyfrom openai import OpenAI

client = OpenAI(base_url="https://api.apikeygrok.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 SDK Integration

Initialize the Node.js SDK with your credentials and the specific base URL. Call the chat completions method with the uncensored model. This ensures your TypeScript or JavaScript applications receive the same raw output as the curl or Python examples.

Copyimport OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.apikeygrok.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 via SSE

Enable streaming by setting stream to true in your request. The server returns Server-Sent Events (SSE) with incremental tokens. Parse the stream to display real-time output to your users without waiting for the full response.

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)

Rate Limits and Constraints

You are limited to 300 requests per minute and 8 concurrent requests per key. The context window supports 100,000 tokens total, with a max output of 16,000 tokens. Errors like 401 invalid key or 429 rate limit are common; ensure your key is active and you have prepaid credit.