Grokified API Documentation

Base URL and authentication setup

Your requests begin at https://api.grokified.cc/v1. Pass your API key in the Authorization header. You receive one active key per account, shown immediately after signup via Google or email.

First request

Send a POST request to /v1/chat/completions with the model uncensored. The system processes your input and returns raw text without corporate filters.

• Use temperature for creativity.
• Set max_tokens to control output length.

Copycurl https://api.grokified.cc/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "uncensored",
    "messages": [{"role": "user", "content": "get the last modified date of index html"}]
  }'

Python SDK integration

Install the official OpenAI library. Set the base URL to our endpoint. Pass your key as the API key value. This ensures compatibility with existing client code.

Copyfrom openai import OpenAI

client = OpenAI(base_url="https://api.grokified.cc/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 setup

Import the OpenAI package. Initialize the client with your key and base URL. Call chat.completions.create with your prompt. The response contains the generated text.

Copyimport OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.grokified.cc/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 and token usage

Enable streaming by setting stream: true. Receive Server-Sent Events (SSE). The final chunk includes token usage data. This helps you track costs in real-time.

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, errors, and context window

You get 300 requests per minute and 8 concurrent requests. The context window is 100,000 tokens. Errors like 401 or 402 do not consume credit. Refusals are also free.