Developer Quickstart: Uncensored LLM API

Base URL and Authentication Setup

Your API key authenticates every request to our hosted endpoint. Use the base URL https://api.groknsfwprompt.com/v1 and set the model to uncensored for text generation. We do not route traffic to other vendors, so your client code sends requests directly to our servers. Authentication relies on the standard Authorization header with a Bearer token. No subscription fees apply, only prepaid credit charges based on actual token usage.

First Request with cURL

Send a simple completion to verify connectivity and billing. This example requests a creative response for an NSFW comic panel prompt. Ensure your prepaid credit balance covers the estimated token cost before executing. We charge for input and output tokens separately at fixed rates.

Copycurl https://api.groknsfwprompt.com/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "uncensored",
    "messages": [{"role": "user", "content": "generate a text file named report.txt"}]
  }'

Check the response status to confirm successful delivery. The API returns JSON formatted data containing the generated text. If you receive a 402 error, top up your crypto credit via USDT or USDC. Errors like bad requests do not consume your balance.

Python SDK Integration

Install the official OpenAI package and configure the base URL to point to our service. This allows you to use familiar Python syntax for all interactions. Set the base_url parameter explicitly to avoid hitting OpenAI's default endpoint. The model identifier must be set to uncensored to access the unrestricted content mode.

Copyfrom openai import OpenAI

client = OpenAI(base_url="https://api.groknsfwprompt.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)

Handle exceptions gracefully to manage rate limits or insufficient funds. The SDK streams data efficiently, reducing memory overhead for long responses. You can adjust parameters like temperature to control creativity. This approach works identically for simple text generation or complex structured outputs.

Node.js SDK Implementation

Initialize the client with your API key and the custom base URL. Node.js developers can leverage the same OpenAI-compatible interface for rapid prototyping. Configure the model ID to uncensored for consistent behavior. This setup supports both synchronous and asynchronous request patterns.

Copyimport OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.groknsfwprompt.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);

Process the response chunks to assemble the final text. The SDK handles the HTTP connection details automatically. Ensure you handle potential network retries if your infrastructure experiences temporary downtime. This method integrates smoothly with existing Express or Fastify routes.

Streaming Responses and Token Counting

Enable streaming by setting stream: true in your request parameters. This delivers tokens incrementally as they are generated, improving perceived latency for users. The final chunk contains the total token usage statistics for billing verification. Streaming is ideal for chat interfaces or real-time creative writing tools.

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)

Parse each chunk to update your UI or log the progress. The API ensures that all tokens are accounted for in the final usage report. This transparency helps you track costs accurately without guessing token consumption. Streaming does not affect the per-token pricing structure.

Rate Limits, Errors, and Context Window

Your key supports 300 requests per minute and 8 concurrent connections. Exceeding these limits returns a 429 error, which you can retry with exponential backoff. The context window spans 100,000 tokens for both prompt and completion combined. Keep your input size reasonable to avoid hitting the 16,000 token output cap. A 401 error indicates an invalid or missing API key, while 402 means your prepaid credit is exhausted. Refunds are not issued for consumed tokens, but billing errors are resolved via support. This strict limit ensures fair usage across all developers using the uncensored LLM API.