LLM API Integration: Complete Developer Guide

LLM API Integration: Developer Guide

Integrating LLM APIs into your applications enables powerful AI capabilities. This guide covers practical implementation for major LLM providers, including authentication, request formatting, error handling, and optimization techniques.

Getting Started

Most LLM providers offer REST APIs with similar patterns:

1. Sign up and get API keys: Register on the provider's platform
2. Set up authentication: Use API keys in request headers
3. Make API calls: Send prompts via HTTP requests
4. Handle responses: Process and use generated content
5. Manage rate limits: Implement retry logic and respect limits

Provider-Specific Implementation

OpenAI (ChatGPT) API

Authentication: Bearer token in Authorization header

Endpoint: https://api.openai.com/v1/chat/completions

Key Features: Multiple models (GPT-3.5, GPT-4, GPT-5.1), streaming support, function calling

Rate Limits: Varies by tier, check documentation for current limits

Best For: General purpose, code generation, multimodal tasks

Anthropic (Claude) API

Authentication: x-api-key header with API key

Endpoint: https://api.anthropic.com/v1/messages

Key Features: Long context (200K tokens), strong safety features, streaming

Rate Limits: Tiered based on plan, generous free tier

Best For: Long documents, safe AI interactions, code review

Google (Gemini) API

Authentication: API key in query parameter or header

Endpoint: https://generativelanguage.googleapis.com/v1beta/models

Key Features: Multimodal inputs, very large context (up to 2M tokens for Gemini 2.0 Pro), Google ecosystem integration

Rate Limits: Free tier with generous limits, paid tiers for higher volume

Best For: Multimodal tasks, Google Workspace integration, large context

DeepSeek API

Authentication: Bearer token in Authorization header

Endpoint: https://api.deepseek.com/v1/chat/completions

Key Features: Cost-effective pricing, open-source models available, strong code generation

Rate Limits: Varies by plan, very cost-effective

Best For: High-volume use cases, cost-sensitive applications, code generation

Best Practices

• Secure API Keys: Never expose keys in client-side code. Use environment variables or secure key management
• Implement Retry Logic: Handle rate limits and temporary failures with exponential backoff
• Set Timeouts: Configure appropriate timeout values for API requests
• Monitor Usage: Track API calls and costs to avoid surprises
• Cache Responses: Cache common queries to reduce API calls and costs
• Handle Errors Gracefully: Implement proper error handling for all failure scenarios
• Use Streaming: For long responses, use streaming for better user experience

Common Integration Patterns

Simple Chat Completion

Basic pattern for sending a prompt and receiving a response. Works for most use cases.

Streaming Responses

For long responses, stream tokens as they're generated for better UX. Reduces perceived latency.

Function Calling

Enable LLMs to call external functions or APIs. Useful for tool use, data retrieval, and complex workflows.

Multi-Turn Conversations

Maintain conversation context by including message history in each request.

Error Handling

Common errors and how to handle them:

• Rate Limits: Implement exponential backoff and retry logic
• Authentication Errors: Verify API keys and permissions
• Token Limits: Truncate or summarize input to fit within context windows
• Network Errors: Implement retry logic with appropriate timeouts
• Invalid Requests: Validate input before sending to API

Explore our curated selection of LLM tools with API access. For choosing the right LLM, see our guide on choosing the right LLM.