Responses API Overview - OpenAI introduced the Responses API to evolve beyond the Chat Completions API, addressing design limitations and enabling new functionalities for building agentic applications [1] - The Responses API combines the simplicity of chat completions with the ability to perform more agentic tasks, simplifying workflows like tool use, code execution, and state management [1] - The core of the Responses API is an agentic loop, allowing multiple actions within a single API request, unlike Chat Completions which only allows one model sample per request [2] - The Responses API uses "items" for everything, including messages, function calls, and MCP calls, making coding easier compared to Chat Completions where function calling was bolted onto messages [2] - The Responses API is purpose-built for reasoning models, preserving reasoning from request to request, boosting tool calling performance by 5% in primary tool calling eval tobench [2] - The Responses API facilitates multimodal workflows, making it easier to work with images and other multimodal content, including support for context stuffing with files like PDFs [2] - Streaming is rethought in the Responses API, emitting a finite number of strongly typed events, simplifying development compared to Chat Completions' object deltas [2] - Long multi-turn rollouts with the Responses API are 20% faster and less expensive due to the ability to rehydrate context from request to request, preserving the chain of thought [2] Agent Platform and Tools - OpenAI is changing deployment with its agent platform, centering on the Responses API and Agents SDK for building embeddable, customizable UIs [3] - Agent Builder and Chatkit, built on the Responses API, make it easy to build workflows into applications with minimal effort [3] - The Responses API is at the core of the improvement flywheel, enabling distillation and reinforcement fine-tuning using stateful data, along with tools like web search and file search [3]