Uncensored LLM API Documentation

Authentication and Base URL

Use your API key with the specified base URL to access the service. The endpoint is https://api.uncenai.cc/v1 and requires the header Authorization: Bearer YOUR_API_KEY. This works with any OpenAI-compatible client by updating the base URL. You receive a key instantly after signup via Google or email.

Copycurl https://api.uncenai.cc/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "uncensored",
    "messages": [{"role": "user", "content": "What is the capital of France?"}]
  }'

First Chat Request

Send a POST request to /v1/chat/completions with the model ID uncensored. Include your system prompt and user message in the body. The API returns text without content filters for lawful adult use. You can adjust parameters like temperature or top_p as needed.

Copyfrom openai import OpenAI

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

Initialize the SDK with your base URL and key. Pass the model ID and messages array to the completion method. The response contains the generated text and token usage. This setup mirrors standard OpenAI SDK patterns for easy migration.

Copyimport OpenAI from "openai";

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

Enable streaming by setting the stream parameter to true. The API returns Server-Sent Events (SSE) for each token. Token usage details appear in the final chunk. This reduces perceived latency for long outputs.

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 Constraints

The context window is 100,000 tokens total. Max output is 16,000 tokens per request, or 2,048 if unspecified. Rate limits allow 300 requests per minute. Request bodies cannot exceed 8 MB. Errors and refusals do not consume credit.

Error Handling and Rates

Handle standard HTTP status codes for common issues. A 401 indicates an invalid or missing API key. A 402 error means your prepaid credit is exhausted. A 429 response signals you have exceeded the 300 requests per minute limit. Always check the response body for details.