Integrate the Uncensored LLM in Minutes

Base URL and Authentication

Use the dedicated base URL and a valid API key to route requests to our uncensored model. The endpoint is HTTPS://api.gptnorestrictions.com/v1. Pass your key in the Authorization header as a Bearer token. This setup works with any OpenAI SDK by overriding the base_url parameter. You get one active key per account, which you generate on the dashboard. No phone number is required for signup, just email or Google login.

Sending Your First Completion Request

Send a POST request to /v1/chat/completions with your system and user messages. The model ID is "uncensored". This returns a text response without content filters for lawful adult topics. You can test connectivity instantly with this curl command:

Copycurl https://api.gptnorestrictions.com/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "uncensored",
    "messages": [{"role": "user", "content": "List the five core components of a standard REST API."}]
  }'

Replace YOUR_API_KEY with your generated key. The response includes token usage details for accurate billing. Errors like 401 indicate an invalid key, while 402 means you need to top up credit via crypto.

Python SDK Integration

Use the official OpenAI Python library to interact with our API seamlessly. Set the base URL to our endpoint and provide your API key. The model "uncensored" supports standard parameters like temperature and top_p. This allows you to leverage familiar code structures for your applications. Here is the minimal setup:

Copyfrom openai import OpenAI

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

Ensure you handle the response object to extract the generated text. This approach maintains compatibility with existing OpenAI clients while providing unrestricted content generation.

Node SDK Configuration

Integrate our API into your Node.js applications using the standard OpenAI npm package. Configure the base URL and API key before making requests. The uncensored model responds to standard chat completion calls. This ensures your existing logic works without major refactoring. Initialize the client like this:

Copyimport OpenAI from "openai";

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

Call the chat.completions.create method with your messages. The response provides the content generated by the LLM. This method is ideal for server-side applications needing reliable, uncensored text generation.

Handling Streaming Responses

Enable streaming by setting stream to true in your request. The API returns Server-Sent Events (SSE) containing incremental text chunks. This improves perceived latency for users interacting with your application. Token usage details are included in the final chunk. Use this code to process the stream:

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)

Accumulate the content parts to form the complete response. This method is efficient for real-time chat interfaces. It allows you to display text as it is generated without waiting for the full completion.

Limits, Errors, and Context Window

Our API supports a context window of 100,000 tokens for prompt and completion combined. You are limited to 300 requests per minute and 8 concurrent requests per key. If you exceed these limits, you receive a 429 error. Common errors include 401 for invalid keys and 402 for insufficient credit. Requests under 8 MB are accepted. Errors and refusals do not consume credit. This ensures you only pay for successful token usage. Manage your limits by monitoring your usage dashboard.