The Ultimate Guide to LLM APIs in TypeScript: OpenAI, Anthropic, and Google Gemini

This technical documentation provides a comprehensive guide to implementing LLM APIs from OpenAI, Anthropic, and Google Gemini in TypeScript backend applications. It covers model capabilities, pricing, implementation details, and best practices for each provider.

Introduction

Large Language Models (LLMs) have become essential components in modern applications, powering everything from chatbots to content generation. This documentation focuses on implementing three major LLM providers in TypeScript:

1. OpenAI (GPT models)
2. Anthropic (Claude models)
3. Google (Gemini models)

For each provider, we cover implementation details, model capabilities, pricing structures, and best practices. The documentation is structured to help you:

• Choose the right provider and model for your needs
• Implement TypeScript integrations correctly
• Understand pricing and calculate costs
• Handle common challenges and troubleshoot issues
• Use advanced features like streaming, JSON mode, and multimodal inputs

Common TypeScript Implementation Patterns

Before diving into provider-specific details, let's explore patterns that apply across all LLM implementations.

Modular Architecture

Structure your code with providers as interchangeable components:

// services/llm/providers/base.ts
interface LLMProvider {
  generateText(prompt: string, options?: any): Promise<string>;
  streamText(prompt: string, options?: any): Promise<AsyncIterable<string>>;
}

// services/llm/index.ts
class LLMService {
  private provider: LLMProvider;

  constructor(provider: LLMProvider) {
    this.provider = provider;
  }

  async generateText(prompt: string, options?: any): Promise<string> {
    return this.provider.generateText(prompt, options);
  }
}

Asynchronous Patterns

All LLM APIs are asynchronous. Use modern async/await patterns and implement retries for resilience: