OpenAI SDK Compatibility

Use Halfred as a drop-in replacement for OpenAI with existing OpenAI SDK libraries. Simply point your client to Halfred's endpoint.

Why Use OpenAI SDK with Halfred?

Ready-to-use SDKs: Access Halfred through battle-tested OpenAI SDKs available in most common programming languages (Go, PHP, Ruby, Java, .NET, Rust, and more)
Drop-in replacement: Use existing OpenAI code with zero modifications
Intelligent model routing: Benefit from Halfred's profile-based model selection
Cost optimization: Automatic selection of the best model for your use case
Multi-provider access: Access models from OpenAI, Anthropic, Google, and more through a single interface
Enhanced reliability: Built-in failover and load balancing

💡 Tip: For the most up-to-date OpenAI SDK documentation and library references for your programming language, visit the official OpenAI SDK libraries page. All official and community-maintained OpenAI SDKs listed there are compatible with Halfred.

Quick Setup

Base URL Configuration

Instead of using OpenAI's default endpoint, configure your client to use Halfred's API:

Base URL: https://api.halfred.ai/v1/
API Key: Your Halfred API key (starts with "halfred_")

Language-Specific Examples

JavaScript/TypeScript (Node.js)

Installation

npm install openai

Configuration

import OpenAI from "openai";

const openai = new OpenAI({
  apiKey: "halfred_xxxxxxxxxxxxxxxxxxxxxxxxxxxx",
  baseURL: "https://api.halfred.ai/v1/",
});

// Use exactly like the OpenAI SDK
async function main() {
  const completion = await openai.chat.completions.create({
    model: "standard", // Use Halfred profiles: lite, standard, deepthink, dev
    messages: [
      { role: "system", content: "You are a helpful assistant." },
      { role: "user", content: "What is the capital of France?" },
    ],
    temperature: 0.7,
  });

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

main();

Environment Variables

# .env file
OPENAI_API_KEY=halfred_xxxxxxxxxxxxxxxxxxxxxxxxxxxx
OPENAI_BASE_URL=https://api.halfred.ai/v1/

import OpenAI from "openai";

// Automatically uses OPENAI_API_KEY and OPENAI_BASE_URL from environment
const openai = new OpenAI();

const completion = await openai.chat.completions.create({
  model: "lite",
  messages: [{ role: "user", content: "Hello!" }],
});

Python

Installation

pip install openai

Configuration

from openai import OpenAI

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

# Use exactly like the OpenAI client
completion = client.chat.completions.create(
    model="lite",  # Use Halfred profiles: lite, standard, deepthink, dev
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is the capital of France?"}
    ],
    temperature=0.7
)

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

Environment Variables

# .env file or environment
export OPENAI_API_KEY="halfred_xxxxxxxxxxxxxxxxxxxxxxxxxxxx"
export OPENAI_BASE_URL="https://api.halfred.ai/v1/"

from openai import OpenAI

# Automatically uses OPENAI_API_KEY and OPENAI_BASE_URL from environment
client = OpenAI()

completion = client.chat.completions.create(
    model="lite",
    messages=[{"role": "user", "content": "Hello!"}]
)

Go

Installation

go get github.com/sashabaranov/go-openai

Configuration

package main

import (
    "context"
    "fmt"
    "github.com/sashabaranov/go-openai"
)

func main() {
    config := openai.DefaultConfig("halfred_xxxxxxxxxxxxxxxxxxxxxxxxxxxx")
    config.BaseURL = "https://api.halfred.ai/v1/"

    client := openai.NewClientWithConfig(config)

    resp, err := client.CreateChatCompletion(
        context.Background(),
        openai.ChatCompletionRequest{
            Model: "standard", // Use Halfred profiles
            Messages: []openai.ChatCompletionMessage{
                {
                    Role:    openai.ChatMessageRoleSystem,
                    Content: "You are a helpful assistant.",
                },
                {
                    Role:    openai.ChatMessageRoleUser,
                    Content: "What is the capital of France?",
                },
            },
            Temperature: 0.7,
        },
    )

    if err != nil {
        fmt.Printf("Error: %v\n", err)
        return
    }

    fmt.Println(resp.Choices[0].Message.Content)
}

PHP

Installation

composer require openai-php/client

Configuration

<?php

require_once 'vendor/autoload.php';

use OpenAI;

$client = OpenAI::factory()
    ->withApiKey('halfred_xxxxxxxxxxxxxxxxxxxxxxxxxxxx')
    ->withBaseUri('https://api.halfred.ai/v1/')
    ->make();

$response = $client->chat()->create([
    'model' => 'standard', // Use Halfred profiles
    'messages' => [
        ['role' => 'system', 'content' => 'You are a helpful assistant.'],
        ['role' => 'user', 'content' => 'What is the capital of France?'],
    ],
    'temperature' => 0.7,
]);

echo $response->choices[0]->message->content;

Ruby

Installation

gem install ruby-openai

Configuration

require 'openai'

client = OpenAI::Client.new(
  access_token: 'halfred_xxxxxxxxxxxxxxxxxxxxxxxxxxxx',
  uri_base: 'https://api.halfred.ai/v1/'
)

response = client.chat(
  parameters: {
    model: 'lite', # Use Halfred profiles
    messages: [
      { role: 'system', content: 'You are a helpful assistant.' },
      { role: 'user', content: 'What is the capital of France?' }
    ],
    temperature: 0.7
  }
)

puts response.dig('choices', 0, 'message', 'content')

Model Selection

When using the OpenAI SDK with Halfred, use Halfred's profile names instead of specific model names:

Available Models

Model Name

Halfred Profile

Description

lite

LITE

Fast and cost-effective for simple tasks

standard

STANDARD

Balanced performance for most applications

deepthink

DEEPTHINK

Advanced reasoning for complex tasks

dev

DEV

Free tier for development and testing

Alternative Model Names

You can also use the prefixed format:

halfred-lite
halfred-standard
halfred-deepthink
halfred-dev

Both formats are equivalent and will work identically.

Supported Features

✅ Fully Supported

Chat Completions: Full support for conversational AI
Message Roles: system, user, assistant roles
Temperature: Control randomness (0.0 to 2.0)
Response Format: JSON mode and structured outputs
Token Usage: Accurate token counting and usage statistics

Response Format

Halfred returns responses in the exact same format as OpenAI, with additional metadata:

{
  "id": "chatcmpl-abc123",
  "object": "chat.completion",
  "created": 1677652288,
  "model": "gpt-4o",
  "provider": "openai",
  "profile": "standard",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "The capital of France is Paris."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 12,
    "completion_tokens": 8,
    "total_tokens": 20
  }
}

Additional Fields

Halfred adds these extra fields to the standard OpenAI response:

provider: The underlying AI provider used (e.g., "openai", "anthropic")
profile: The Halfred profile that was selected

Migration from OpenAI

Step 1: Update Configuration

Replace your OpenAI configuration:

// Before (OpenAI)
const openai = new OpenAI({
  apiKey: "sk-your-openai-key",
});

// After (Halfred)
const openai = new OpenAI({
  apiKey: "halfred_xxxxxxxxxxxxxxxxxxxxxxxxxxxx",
  baseURL: "https://api.halfred.ai/v1/",
});

Step 2: Update Model Names

Replace specific OpenAI model names with Halfred profiles:

// Before (OpenAI)
model: "gpt-4o";
model: "gpt-3.5-turbo";

// After (Halfred)
model: "standard"; // Automatically selects best model
model: "lite"; // For cost-effective tasks

Step 3: Test Your Integration

Your existing code should work without any other changes. Test with a simple completion:

const completion = await openai.chat.completions.create({
  model: "dev",
  messages: [{ role: "user", content: "Hello, world!" }],
});

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

Best Practices

Model Selection Strategy

// For simple tasks (cost-effective)
const quickResponse = await openai.chat.completions.create({
  model: "lite",
  messages: [{ role: "user", content: "Summarize this in one sentence." }],
});

// For most applications (balanced)
const standardResponse = await openai.chat.completions.create({
  model: "standard",
  messages: [{ role: "user", content: "Explain quantum computing." }],
});

// For complex reasoning (high-quality)
const deepResponse = await openai.chat.completions.create({
  model: "deepthink",
  messages: [{ role: "user", content: "Analyze this complex business scenario..." }],
});

// For development/testing (free)
const testResponse = await openai.chat.completions.create({
  model: "dev",
  messages: [{ role: "user", content: "Test message" }],
});

Error Handling

try {
  const completion = await openai.chat.completions.create({
    model: "standard",
    messages: [{ role: "user", content: "Hello!" }],
  });

  console.log(completion.choices[0].message.content);
} catch (error) {
  if (error.status === 401) {
    console.error("Invalid API key");
  } else if (error.status === 429) {
    console.error("Rate limit exceeded or insufficient credits");
  } else {
    console.error("API error:", error.message);
  }
}

Troubleshooting

Common Issues

Authentication Error

Error: 401 Unauthorized

Solution: Ensure you're using a valid Halfred API key (format: halfred_xxxx...).

Invalid Model Error

Error: Model 'gpt-4' not found

Solution: Use Halfred profile names (lite, standard, deepthink, dev) instead of specific model names.

Base URL Not Set

Error: Connection refused

Solution: Make sure you've set the baseURL to https://api.halfred.ai/v1/.

Timeout Configuration

const openai = new OpenAI({
  apiKey: "halfred_xxxxxxxxxxxxxxxxxxxxxxxxxxxx",
  baseURL: "https://api.halfred.ai/v1/",
  timeout: 30000, // 30 seconds
});

Retry Logic

import OpenAI from "openai";

const openai = new OpenAI({
  apiKey: "halfred_xxxxxxxxxxxxxxxxxxxxxxxxxxxx",
  baseURL: "https://api.halfred.ai/v1/",
  maxRetries: 3,
});

Support

For OpenAI SDK compatibility issues:

Check our API Reference for detailed endpoint documentation
Contact support at [email protected]
Join our community Discord

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