Integrate the Open Source LLM API in Minutes

Base URL and Authentication

Connect to the open source llm api by pointing your client to our base URL: https://api.opensourcellmapi.com/v1. Authentication is handled via a standard API key passed in the Authorization header. You receive this key immediately after signing up on the Get API key page with just an email and password. No phone number or credit card is required to start. If you need to rotate credentials, you can regenerate your key at any time, which immediately revokes the previous one. This ensures you maintain control over your access without relying on a complex dashboard interface.

Send Your First Request

Make a standard POST request to /v1/chat/completions. The endpoint expects a JSON body with a model field set to uncensored and a messages array. This endpoint supports the standard chat format, allowing you to switch from OpenAI or other providers with minimal code changes. Below is a basic cURL example to verify connectivity and receive a text response.

Copycurl https://api.opensourcellmapi.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 Integration

Using the official OpenAI Python library simplifies integration. Install the package via pip, then initialize the client with your API key and our base URL. Set the model to uncensored and call the chat completions method. This approach lets you leverage existing Python LLM applications with zero modification to the inference logic. The library handles serialization and error parsing automatically.

Copyfrom openai import OpenAI

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

For JavaScript or TypeScript projects, use the OpenAI Node.js SDK. Install the package and configure the client with the same base URL and API key. The uncensored model ID ensures you are hitting our uncensored inference endpoint. This allows you to integrate unrestricted LLM capabilities directly into your web or server-side applications while maintaining compatibility with standard OpenAI client code.

Copyimport OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.opensourcellmapi.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);

Enable Streaming (SSE)

For lower latency and real-time user experiences, enable streaming by setting stream: true. The API returns Server-Sent Events (SSE), allowing you to process tokens as they are generated. This is ideal for chat interfaces where typing indicators improve perceived performance. The response structure remains consistent with standard OpenAI streaming formats, ensuring your event handlers work without custom parsing logic.

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 Window

Your API key is limited to 300 requests per minute and an 8 MB request body. The context window supports up to 64,000 tokens for the combined prompt and completion. If you receive a 401, your key is invalid or expired. A 402 indicates insufficient prepaid credit; you can top up from $10 via crypto (USDT or USDC). A 429 means you have exceeded the rate limit. All credits are prepaid and never expire, with bonuses available for larger top-ups.