Integrate the Uncensored AI API

Base URL and Authentication Setup

Start by pointing your client to our base URL. Use https://api.apiaiuncensored.com/v1 as the endpoint and provide your API key for authentication. This works with the official OpenAI SDKs and any compatible client. You need an API key to make requests. Generate one on the dashboard page. The key grants access to the uncensored model. It is the only credential you need. Keep it secure. Treat it like a password. The service uses prepaid credits. You can top up with crypto only. USDT on TRC20 or USDC on Base are the accepted currencies. Credits never expire. There are no monthly fees. You pay only for what you use. Errors and refusals cost nothing. This model is not GPT or Claude. It is a distinct open-weight model tuned for unfiltered output. The context window is 100,000 tokens. You can send long prompts. Output is limited to 16,000 tokens per request.

Sending Your First Chat Completion Request

Make a POST request to /v1/chat/completions with your messages. Set the model to uncensored. Send your system prompt and user message. The API returns a text response. It does not filter lawful adult or controversial topics. You can use it for roleplay or creative writing. The uncensored LLM API handles text generation. It does not support images or audio. You must specify the model ID. It is always uncensored. Do not use other model names. The request body includes the messages array. Each message has a role and content. The system role sets the behavior. The user role provides the input. The assistant role contains the response. You can stream the output. Or you can wait for the full response. Streaming is recommended for long outputs. The uncensored API works with any language. It supports function calling. You can define tools in the request. The model will follow the tool schema. It returns structured data. You can parse the JSON response. Use the JSON mode for strict output. Set the response format to json_object. This ensures the output is valid JSON. The uncensored coding AI works well with code generation. It does not refuse technical explanations. It provides direct answers. You can use it for debugging. It helps with complex logic. The uncensored LLM online is fast. It has low latency. You can integrate it into your app. It is reliable. It is stable. It is secure. Privacy is key. Prompts are not used for training. Your data stays yours. No phone number is needed for signup. Google or email works. Get your key now. Start building. The uncensored AI chatbot is ready. It is waiting for your input. It is eager to help. It is unfettered. It is free from restrictions. It is pure text generation. It is powerful. It is fast. It is cheap. It is uncensored.

Python SDK Integration

Use the official OpenAI Python library. Install it with pip. Set the base URL to our endpoint. Set the API key. Create an instance of the client. Call the chat completions method. Pass the model and messages. The response contains the generated text. You can access it via the choices attribute. The uncensored AI API works seamlessly with Python. It is easy to use. It is robust. It is well-documented. You can loop through responses. You can handle errors. You can process large datasets. The uncensored LLM API is perfect for batch processing. It is efficient. It is scalable. It is flexible. You can use it with LangChain. You can use it with LlamaIndex. You can use it with any framework. It is compatible. It is standard. It is reliable. The uncensored coding AI is great for code generation. It produces clean code. It follows best practices. It understands context. It remembers previous messages. It maintains state. It is smart. It is helpful. It is accurate. It is precise. It is fast. It is cheap. It is uncensored. Get started today. Write your first script. Run it. See the output. It is amazing. It is powerful. It is uncensored.

Node.js SDK Configuration

Install the OpenAI Node.js package. Configure the base URL. Set the API key. Initialize the client. Call the chat.completions.create method. Pass the model and messages. The response contains the text. Access it from the response object. The uncensored AI API works with Node.js. It is fast. It is efficient. It is lightweight. You can use it in serverless functions. You can use it in Express apps. You can use it in Next.js. It is versatile. It is adaptable. It is easy to integrate. The uncensored LLM API is perfect for web apps. It is real-time. It is interactive. It is engaging. You can build chatbots. You can build assistants. You can build agents. It is capable. It is intelligent. It is responsive. It is reliable. The uncensored coding AI is excellent for code. It generates snippets. It explains logic. It debugs errors. It is a developer's best friend. It is uncensored. It is free from bias. It is neutral. It is objective. It is factual. It is clear. It is concise. It is direct. It is useful. It is valuable. It is essential. Get coding. Build something great. Use the uncensored API. It is ready. It is waiting. It is here.

Streaming Responses and Token Usage

Enable streaming by setting the stream parameter to true. The API returns chunks of text. Each chunk contains part of the response. You can display tokens as they arrive. This improves user experience. The uncensored AI API supports streaming. It is smooth. It is responsive. It is real-time. You can see the text appearing. It is like typing. It is engaging. It is immersive. The uncensored LLM API provides token counts. You can track usage. You can monitor costs. You can optimize performance. The uncensored coding AI works well with streaming. It generates code line by line. It is helpful for developers. It is clear. It is precise. It is fast. It is efficient. It is uncensored. It is free. It is cheap. It is reliable. It is secure. It is private. No data is stored. No training occurs. Your prompts are yours. Keep them safe. Use the uncensored API. It is the best. It is the only. It is the right choice. Get started. Stream your first response. Watch the tokens. See the usage. It is informative. It is useful. It is valuable. It is uncensored.

Handling Rate Limits and Errors

The API enforces rate limits. You can make 300 requests per minute. You can have 8 concurrent requests. The request body limit is 8 MB. If you exceed limits, you get a 429 error. Handle this by retrying with backoff. If your key is invalid, you get a 401 error. Check your API key. If you have no credit, you get a 402 error. Top up with crypto. The uncensored AI API is reliable. It is stable. It is consistent. It is predictable. The uncensored LLM API handles errors gracefully. It returns clear messages. It helps you debug. It is helpful. It is informative. The uncensored coding AI works within limits. It respects quotas. It is fair. It is balanced. It is uncensored. It is free. It is cheap. It is secure. It is private. Google or email signup. Get your key. Start using. The uncensored API is here. It is ready. It is waiting. It is yours.

cURL

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

Python

Copyfrom openai import OpenAI

client = OpenAI(base_url="https://api.apiaiuncensored.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.js

Copyimport OpenAI from "openai";

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

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)