API Basics
definition
Making HTTP API calls to LLM providers is the foundational skill for building any agent system, as every agentic tool — from IDE agents to custom pipelines — ultimately sends requests to a model API and processes the structured response. Understanding request anatomy (authentication, endpoints, model parameters, streaming), response handling (token usage, finish reasons, tool call outputs), and provider differences (OpenAI's chat completions vs Anthropic's messages API vs Google's Gemini API) is essential for any developer moving beyond pre-built tools.
Making HTTP API calls to LLM providers is the foundational skill for building any agent system, as every agentic tool — from IDE agents to custom pipelines — ultimately sends requests to a model API and processes the structured response. Understanding request anatomy (authentication, endpoints, model parameters, streaming), response handling (token usage, finish reasons, tool call outputs), and provider differences (OpenAI's chat completions vs Anthropic's messages API vs Google's Gemini API) is essential for any developer moving beyond pre-built tools. The API layer is also where you control cost, latency, and reliability through parameters like temperature, max_tokens, and stop sequences, and where you implement production concerns like retries, fallbacks, and rate limit handling. This concept connects to function calling for the tool-use capability exposed through APIs, token economics for understanding the cost implications of API parameters, and model selection for choosing which API endpoint to target.