How does the Responses API differ from Chat Completions?

Unlike Chat Completions which is stateless, the Responses API automatically manages conversation history and preserves the model's reasoning notebook across turns, leading to better multi-turn performance.

What are the caching benefits of using this skill?

By leveraging conversation IDs and preserved state, applications can see a 40-80% improvement in cache utilization, significantly reducing costs and processing time.

Does this support the Model Context Protocol (MCP)?

Yes, it includes patterns for connecting to external MCP servers to integrate third-party tools like Stripe, databases, or custom APIs directly into the AI workflow.

What tools are built-in to the Responses API?

It includes server-side hosted tools such as Code Interpreter (Python execution), File Search (RAG), Web Search, and DALL-E 3 image generation.

OpenAI Responses API Integration

Name: OpenAI Responses API Integration
Author: jezweb

byjezweb

•

117

数据科学与机器学习

Builds stateful, agentic AI applications using OpenAI's Responses API with preserved reasoning and server-side tool integration.

关于

This skill empowers developers to implement OpenAI's stateful Responses API, the advanced successor to Chat Completions designed specifically for agentic workflows. It provides optimized patterns for maintaining conversation context through persistent IDs, leveraging server-side tools like Code Interpreter and File Search, and integrating Model Context Protocol (MCP) servers. By utilizing the API's ability to preserve reasoning states across turns, this skill helps users achieve up to 80% better cache utilization and improved multi-turn performance in AI-driven applications.

主要功能

Built-in support for Model Context Protocol (MCP) server connections
Server-side tool integration for Code Interpreter, File Search, and Web Search
Migration pathways from legacy Chat Completions to stateful architectures
117 GitHub stars
Polymorphic output handling for reasoning, messages, and tool calls
Automatic state management using persistent conversation IDs

使用场景

Optimizing high-traffic AI applications for better cache utilization and lower latency
Developing complex AI agents that require long-term reasoning persistence
Building RAG systems using native OpenAI File Search and Web Search capabilities

关于

主要功能

Built-in support for Model Context Protocol (MCP) server connections
Server-side tool integration for Code Interpreter, File Search, and Web Search
Migration pathways from legacy Chat Completions to stateful architectures
117 GitHub stars
Polymorphic output handling for reasoning, messages, and tool calls
Automatic state management using persistent conversation IDs

使用场景

Optimizing high-traffic AI applications for better cache utilization and lower latency
Developing complex AI agents that require long-term reasoning persistence
Building RAG systems using native OpenAI File Search and Web Search capabilities