OpenAI SDK Integration

Overview

The platform is fully compatible with the official OpenAI SDK. Just change the base URL and you get access to a broad range of models with TEE privacy protection.

The easiest way to get started - works with your existing OpenAI code!

Installation

pip install openai

npm install openai

Python Quick Start

Basic Setup

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.redpill.ai/v1"
)

response = client.chat.completions.create(
    model="openai/gpt-5",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain how TEE protects my data"}
    ]
)

print(response.choices[0].message.content)

Streaming Responses

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.redpill.ai/v1"
)

stream = client.chat.completions.create(
    model="openai/gpt-5",
    messages=[{"role": "user", "content": "Write a story about AI"}],
    stream=True
)

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

Function Calling

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.redpill.ai/v1"
)

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather in a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "City name, e.g. San Francisco"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"]
                    }
                },
                "required": ["location"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="openai/gpt-5",
    messages=[{"role": "user", "content": "What's the weather in Paris?"}],
    tools=tools
)

# Check if model wants to call a function
if response.choices[0].message.tool_calls:
    tool_call = response.choices[0].message.tool_calls[0]
    print(f"Function: {tool_call.function.name}")
    print(f"Arguments: {tool_call.function.arguments}")

Embeddings

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.redpill.ai/v1"
)

response = client.embeddings.create(
    model="openai/text-embedding-3-small",
    input="The platform protects your AI requests with hardware TEE"
)

embedding = response.data[0].embedding
print(f"Embedding dimensions: {len(embedding)}")  # 1536

Vision (Image Analysis)

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.redpill.ai/v1"
)

response = client.chat.completions.create(
    model="openai/gpt-5",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://example.com/image.jpg"
                    }
                }
            ]
        }
    ]
)

print(response.choices[0].message.content)

TypeScript/JavaScript Quick Start

Basic Setup

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.API_KEY,
  baseURL: 'https://api.redpill.ai/v1'
});

async function main() {
  const response = await client.chat.completions.create({
    model: 'openai/gpt-5',
    messages: [
      { role: 'system', content: 'You are a helpful assistant.' },
      { role: 'user', content: 'Explain how TEE protects my data' }
    ]
  });

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

main();

Streaming in TypeScript

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.API_KEY,
  baseURL: 'https://api.redpill.ai/v1'
});

async function main() {
  const stream = await client.chat.completions.create({
    model: 'openai/gpt-5',
    messages: [{ role: 'user', content: 'Write a story about AI' }],
    stream: true
  });

  for await (const chunk of stream) {
    process.stdout.write(chunk.choices[0]?.delta?.content || '');
  }
}

main();

Function Calling in TypeScript

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.API_KEY!,
  baseURL: 'https://api.redpill.ai/v1'
});

async function main() {
  const tools: OpenAI.Chat.ChatCompletionTool[] = [
    {
      type: 'function',
      function: {
        name: 'get_weather',
        description: 'Get current weather in a location',
        parameters: {
          type: 'object',
          properties: {
            location: {
              type: 'string',
              description: 'City name'
            },
            unit: {
              type: 'string',
              enum: ['celsius', 'fahrenheit']
            }
          },
          required: ['location']
        }
      }
    }
  ];

  const response = await client.chat.completions.create({
    model: 'openai/gpt-5',
    messages: [{ role: 'user', content: "What's the weather in Paris?" }],
    tools
  });

  const toolCall = response.choices[0].message.tool_calls?.[0];
  if (toolCall) {
    console.log('Function:', toolCall.function.name);
    console.log('Arguments:', toolCall.function.arguments);
  }
}

main();

Using Multiple Models

The platform gives you access to a broad range of models through the same SDK:

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.redpill.ai/v1"
)

# GPU TEE model for sensitive reasoning
tee_response = client.chat.completions.create(
    model="z-ai/glm-5.1",
    messages=[{"role": "user", "content": "Process this confidential..."}]
)

# Confidential (is_tee) model for systems engineering
near_response = client.chat.completions.create(
    model="z-ai/glm-5",
    messages=[{"role": "user", "content": "Analyze this architecture..."}]
)

# OpenAI GPT-5 for general tasks
gpt_response = client.chat.completions.create(
    model="openai/gpt-5",
    messages=[{"role": "user", "content": "Summarize this article..."}]
)

# Anthropic Claude for analysis
claude_response = client.chat.completions.create(
    model="anthropic/claude-sonnet-4.5",
    messages=[{"role": "user", "content": "Analyze this data..."}]
)

Environment Variables

Never hardcode API keys. Use environment variables:

.env

API_KEY=YOUR_API_KEY

Python

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("API_KEY"),
    base_url="https://api.redpill.ai/v1"
)

TypeScript

// Uses process.env.API_KEY automatically
const client = new OpenAI({
  baseURL: 'https://api.redpill.ai/v1'
});

Error Handling

from openai import OpenAI, APIError, RateLimitError

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.redpill.ai/v1"
)

try:
    response = client.chat.completions.create(
        model="openai/gpt-5",
        messages=[{"role": "user", "content": "Hello!"}]
    )
    print(response.choices[0].message.content)

except RateLimitError:
    print("Rate limit exceeded. Please wait and retry.")

except APIError as e:
    print(f"API error: {e}")

except Exception as e:
    print(f"Unexpected error: {e}")

Async Support

Python Async

import asyncio
from openai import AsyncOpenAI

client = AsyncOpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.redpill.ai/v1"
)

async def main():
    response = await client.chat.completions.create(
        model="openai/gpt-5",
        messages=[{"role": "user", "content": "Hello!"}]
    )
    print(response.choices[0].message.content)

asyncio.run(main())

TypeScript Async (built-in)

// All OpenAI SDK methods are async by default in TypeScript
const response = await client.chat.completions.create({
  model: 'openai/gpt-5',
  messages: [{ role: 'user', content: 'Hello!' }]
});

Migration from OpenAI

Update Base URL

Add base_url="https://api.redpill.ai/v1" to your client initialization

Update API Key

Replace your OpenAI API key with your platform API key

Add Provider Prefix

Change model="gpt-4" to model="openai/gpt-5"

Test

Run your application - everything else stays the same!

Detailed Migration Guide

Full migration guide with examples →

Supported Features

Feature	Python	TypeScript	Notes
Chat Completions	✅	✅	All models
Streaming	✅	✅	Real-time responses
Function Calling	✅	✅	Tool use support
Embeddings	✅	✅	Vector generation
Vision	✅	✅	Image analysis
Async/Await	✅	✅	Non-blocking calls

Popular Models

Model	Best For	Context
`z-ai/glm-5.1`	Confidential reasoning (GPU TEE)	128K tokens
`z-ai/glm-5`	Confidential systems engineering (GPU TEE)	128K tokens
`qwen/qwen3.5-27b`	Confidential general purpose (GPU TEE)	128K tokens
`openai/gpt-5`	General purpose	128K tokens
`anthropic/claude-sonnet-4.5`	Reasoning, analysis	200K tokens
`deepseek/deepseek-chat-v3.1`	Coding tasks	64K tokens
`google/gemini-2.5-pro`	Multimodal tasks	2M tokens

View All Models

Browse complete model list →

Example Projects

CLI Chatbot

Build a command-line AI assistant

Document Q&A

Create a RAG system with embeddings

Code Assistant

Build an AI pair programmer

Data Analyzer

Analyze datasets with AI

Next Steps

Function Calling Guide

Learn advanced function calling

Streaming Guide

Implement streaming responses

Vision Guide

Work with images and vision models

Error Handling

Handle errors gracefully

Core

Privacy

Migration

Integrations

OpenAI SDK Integration

Overview

Installation

Python Quick Start

Basic Setup

Streaming Responses

Function Calling

Embeddings

Vision (Image Analysis)

TypeScript/JavaScript Quick Start

Basic Setup

Streaming in TypeScript

Function Calling in TypeScript

Using Multiple Models

Environment Variables

Error Handling

Async Support

Python Async

TypeScript Async (built-in)

Migration from OpenAI

Detailed Migration Guide

Supported Features

Popular Models

View All Models

Example Projects

CLI Chatbot

Document Q&A

Code Assistant

Data Analyzer

Next Steps

Function Calling Guide

Streaming Guide

Vision Guide

Error Handling

​Overview

​Installation

​Python Quick Start

​Basic Setup

​Streaming Responses

​Function Calling

​Embeddings

​Vision (Image Analysis)

​TypeScript/JavaScript Quick Start

​Basic Setup

​Streaming in TypeScript

​Function Calling in TypeScript

​Using Multiple Models

​Environment Variables

​Error Handling

​Async Support

​Python Async

​TypeScript Async (built-in)

​Migration from OpenAI

Detailed Migration Guide

​Supported Features

​Popular Models

View All Models

​Example Projects

CLI Chatbot

Document Q&A

Code Assistant

Data Analyzer

​Next Steps

Function Calling Guide

Streaming Guide

Vision Guide

Error Handling

Overview

Installation

Python Quick Start

Basic Setup

Streaming Responses

Function Calling

Embeddings

Vision (Image Analysis)

TypeScript/JavaScript Quick Start

Basic Setup

Streaming in TypeScript

Function Calling in TypeScript

Using Multiple Models

Environment Variables

Error Handling

Async Support

Python Async

TypeScript Async (built-in)

Migration from OpenAI

Supported Features

Popular Models

Example Projects

Next Steps