OpenAI SDK

Use the official OpenAI Python or TypeScript SDK with OpenCompress.

OpenCompress is fully compatible with the OpenAI SDK. Change base_url and api_key — everything else stays the same.

Python

pip install openai

from openai import OpenAI

client = OpenAI(
    base_url="https://www.opencompress.ai/api/v1",
    api_key="sk-occ-your-key-here",
)

# Non-streaming
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a senior software engineer."},
        {"role": "user", "content": "Review this code and suggest improvements..."},
    ],
)

print(response.choices[0].message.content)
print(f"Tokens used: {response.usage.total_tokens}")

TypeScript / Node.js

npm install openai

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://www.opencompress.ai/api/v1",
  apiKey: "sk-occ-your-key-here",
});

const response = await client.chat.completions.create({
  model: "claude-sonnet-4-6",
  messages: [
    { role: "system", content: "You are a senior software engineer." },
    { role: "user", content: "Review this code and suggest improvements..." },
  ],
});

console.log(response.choices[0].message.content);

Streaming

Python

stream = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Write a haiku about compression."}],
    stream=True,
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

Streaming works identically to the OpenAI API. Compression happens before the stream starts — there is no additional latency during token generation.

Switching between models

You can use any supported model by changing the model parameter. No other code changes needed.

# OpenAI
client.chat.completions.create(model="gpt-4o", ...)

# Anthropic
client.chat.completions.create(model="claude-sonnet-4-6", ...)

# Google
client.chat.completions.create(model="gemini-2.5-pro", ...)

# Meta (via OpenRouter)
client.chat.completions.create(model="meta-llama/llama-4-maverick", ...)