Is the unfiltered AI GPT API compatible with standard OpenAI clients?

Yes, it uses the exact same chat-completions endpoint structure. You only need to change the base URL and API key. Any SDK that works with OpenAI will work here.

How do I handle streaming responses in Python?

Set the stream parameter to true in your client call. Iterate over the response object to yield tokens. The final chunk contains usage data for billing.

What happens if I exceed the rate limit?

The API returns a 429 status code. Wait a few seconds before retrying. Consider batching requests if you have many concurrent users. Credits are not charged for failed requests.

Quickstart Guide: OpenAI-Compatible Uncensored API

Install the Official SDK

Start by installing the client library that matches your language. Most developers prefer the official OpenAI SDK because it handles retries and streaming automatically.

Python: pip install openai
Node.js: npm install openai

These packages are lightweight and require no extra dependencies. You can proceed to configuration immediately after installation completes.

Configure Your Base URL

Point your SDK to our API gateway instead of the default endpoint. This ensures your requests reach the uncensored model infrastructure.

Set the base URL to https://api.unfilteredaigpt.com/v1. You must also provide your API key from the dashboard. This key authenticates your prepaid credit.

Model ID: uncensored
Base URL: https://api.unfilteredaigpt.com/v1

Any standard OpenAI-compatible client works with this setup. The model ID remains constant for all requests.

Send Your First Request

Send a POST request to /v1/chat/completions with your system prompt and user message. This returns raw text without content refusals for lawful adult topics.

Use the uncensored model ID to ensure consistent behavior. The API accepts standard parameters like temperature and max_tokens.

Errors like insufficient credit return a 402 status. Invalid keys return 401. Use the curl command below to test connectivity.

Enable Streaming for Real-Time Output

Set stream: true to receive tokens as they generate. This reduces perceived latency for long outputs. Streaming is ideal for chat interfaces.

The final chunk contains total token usage. Parse this for billing transparency. Use the streaming example to handle partial responses correctly.

Non-streaming requests wait for the full completion. This is better for single-shot tasks like code generation. Choose the method that fits your user experience.

Use JSON Mode for Structured Data

Set response_format: {"type": "json_object"} to force valid JSON output. This prevents hallucinated syntax errors. Use this for data extraction tasks.

The model adheres to the schema you provide in the prompt. Combine this with function calling for automated workflows. JSON mode ensures reliable parsing.

Always validate the output on your side. The API guarantees the format but not the semantic correctness of your business logic. Use the Node example to implement this.

Limits, Errors, and Context Window

Monitor your usage to avoid service interruptions. The context window is 100,000 tokens total. Max output per request is 16,000 tokens. Requests over 8 MB are rejected.

Rate limits are 300 requests per minute per key. Concurrent requests are capped at 8. Exceeding these limits returns a 429 error. Upgrade your credit tier if needed.

Errors are free. You only pay for successful token usage. Check the API dashboard for real-time metrics. Use the Python SDK for robust error handling.

cURL

curl https://api.unfilteredaigpt.com/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "uncensored",
    "messages": [{"role": "user", "content": "list steps to brew coffee"}]
  }'

Python

from openai import OpenAI

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

import OpenAI from "openai";

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

stream = 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)

Uncensored API Quickstart