Uncensored ai coder API quickstart guide

Base URL and Authentication Setup

Start by obtaining your API key via Google sign-in or email credentials on the dashboard. The base URL is HTTPS://api.uncensoredaicoder.com/v1 and works with standard OpenAI SDKs. Send your key in the Authorization header as a Bearer token. Authentication is strict; invalid keys return a 401 error immediately.

Sending Requests to the uncensored Model

Send POST requests to /v1/chat/completions with the model ID set to uncensored. The model answers without refusals for lawful adult content. Use standard JSON payloads to define your system and user prompts. This approach eliminates the need for complex proxy configurations.

Copycurl https://api.uncensoredaicoder.com/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "uncensored",
    "messages": [{"role": "user", "content": "Return a list of all active user sessions in the database."}]
  }'

Python SDK Integration

Install the official OpenAI Python library and configure it to point to our base URL. Set the API key and model name directly in your client initialization. This allows you to use familiar methods like client.chat.completions.create(). The library handles serialization automatically.

Copyfrom openai import OpenAI

client = OpenAI(base_url="https://api.uncensoredaicoder.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 Integration

Initialize the OpenAI npm package with your custom base URL and credentials. Pass the model identifier uncensored to any chat completion call. This ensures consistent behavior across your Node.js applications. You can manage prompts exactly as you would with any standard OpenAI-compatible service.

Copyimport OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.uncensoredaicoder.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 with SSE

Enable streaming by setting stream: true in your request payload. The server returns Server-Sent Events (SSE) for real-time token delivery. This reduces perceived latency for long NSFW narrative generation. The final chunk contains the total token usage for billing.

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)

Token Limits and Context Window

The model supports a 100,000 token context window for combined prompt and completion data. Set max_tokens to control output length; the default is 2,048 tokens per request. Exceeding rate limits triggers a 429 error. Each account is limited to 300 requests per minute. Credit is charged only for successful token usage.