LLM No Login: API Quickstart

Authentication

Every request requires a valid API key. You obtain this key immediately after signing up with just an email address and password on our key generation page. No phone verification, credit card, or corporate identity gate is required to start. For the trial tier, no payment method is needed, though paid credit is required for sustained usage beyond the initial trial allowance.

Include the key in the Authorization header using the Bearer scheme. Keep your key secure; you can regenerate it at any time from your dashboard, which instantly revokes the previous key. If you see a 401 error, verify that the key is active and correctly formatted.

Chat Completions Endpoint

The core functionality resides at the standard chat-completions endpoint. This API accepts text input and returns text output, supporting both simple prompts and structured conversations with multiple turns. It is compatible with the official OpenAI SDKs and any client that supports the OpenAI API specification.

To make your first request, point your client to our base URL: https://api.llmnologin.com/v1. Specify the model ID as uncensored in your request body. This model is an open-weight variant tuned to answer without content refusals for lawful adult use, making it ideal for creative writing, coding, or research where standard filters might interfere.

Base URL: https://api.llmnologin.com/v1

Endpoint: POST /v1/chat/completions

Here is a basic example using cURL to send a simple prompt:

Copycurl https://api.llmnologin.com/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "uncensored",
    "messages": [{"role": "user", "content": "Write a blunt product review of a cheap VPN."}]
  }'

Python SDK

For Python developers, the official OpenAI Python SDK works out of the box with minimal configuration. You only need to update the base URL to point to our service and provide your API key. This approach allows you to leverage familiar libraries and tools while accessing an uncensored model without the restrictions of standard commercial providers.

Initialize the client with your key and the custom base URL. Then, call the chat.completions.create method with the uncensored model ID. This setup is ideal for integrating LLM capabilities into scripts, bots, or backend services where you need reliable, unfiltered text generation.

Copyfrom openai import OpenAI

client = OpenAI(base_url="https://api.llmnologin.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

JavaScript and TypeScript developers can use the official openai npm package to interact with our API. Similar to Python, you must configure the client to use our base URL and authentication header. This ensures that your Node.js applications can seamlessly integrate with our uncensored model for tasks ranging from code generation to content analysis.

Create a new OpenAI client instance, set the apiKey and baseURL, and then invoke the chat completion endpoint. This method is efficient for server-side rendering, API endpoints, or automated workflows where low latency and high throughput are necessary.

Copyimport OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.llmnologin.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

For applications that benefit from real-time feedback, such as chat interfaces or live coding assistants, enable streaming by setting stream: true in your request. The API returns a Server-Sent Events (SSE) stream, allowing you to process tokens as they are generated rather than waiting for the entire response.

This reduces perceived latency significantly. The SDKs handle the parsing of SSE streams automatically, exposing an async iterator or callback for each token. This is particularly useful for long-form content generation where users prefer to see progress as it happens.

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

Our service supports a 100,000-token context window, accommodating both prompt and completion tokens. This allows for extensive conversation history or large document processing within a single request. Be mindful of the 8 MB request body limit and the rate limit of 300 requests per minute per API key.

Common error codes include 401 for invalid keys, 402 for insufficient prepaid credit, and 429 for rate limit exceeded. If you encounter a 402 error, top up your account via crypto (USDT or USDC) to continue usage. Paid credit never expires, so you can use it at your own pace. Note that while the model is uncensored, requests involving sexual content with minors are always blocked.