OpenAI has expanded its Responses API to facilitate the development of agentic workflows. This includes support for a shell tool, an agent execution loop, a hosted container workspace, context compaction, and reusable agent skills. The new features aim to offload the complexities of building execution environments from developers, providing a managed infrastructure for handling tasks like file management, prompt optimization, secure network access, and handling timeouts.
A core component is the agent execution loop, where the model proposes actions (running commands, querying data) that are executed in a controlled environment, with the results fed back to refine the process. Skills allow for the creation of reusable task patterns.
A core component is the agent execution loop, where the model proposes actions (running commands, querying data) that are executed in a controlled environment, with the results fed back to refine the process. Skills allow for the creation of reusable task patterns.
This article details how to use Ollama to run large language models locally, protecting sensitive data by keeping it on your machine. It covers installation, usage with Python, LangChain, and LangGraph, and provides a practical example with FinanceGPT, while also discussing the tradeoffs of using local LLMs.
Understand API rate limits and restrictions. This document details how OpenAI’s rate limit system works, including usage tiers, headers, error mitigation strategies like exponential backoff, and batching requests.
Agoda engineers developed API Agent, a system with zero code and zero deployments that enables a single Model Context Protocol (MCP) server to connect to internal REST or GraphQL APIs. The system is designed to reduce the operational overhead of managing multiple APIs with distinct schemas and authentication methods, allowing teams to query services through AI assistants without building individual MCP servers for each API.
This document details the features, best practices, and migration guidance for GPT-5, OpenAI's most intelligent model. It covers new API features like minimal reasoning effort, verbosity control, custom tools, and allowed tools, along with prompting guidance and migration strategies from older models and APIs.
High-performance deployment of the vLLM serving engine, optimized for serving large language models at scale.
This article discusses how to overcome limitations of retrieval-augmented generation (RAG) models by creating an AI assistant using advanced SQL vector queries. The author uses tools such as MyScaleDB, OpenAI, LangChain, Hugging Face and the HackerNews API to develop an application that enhances the accuracy and efficiency of data retrieval process.
A tutorial showing you how how to bring real-time data to LLMs through function calling, using OpenAI's latest LLM GTP-4o.
Function calling allows you to more reliably get structured data back from the model. For example, you can:
Create chatbots that answer questions by calling external APIs (e.g. like ChatGPT Plugins)
e.g. define functions like send_email(to: string, body: string), or get_current_weather(location: string, unit: 'celsius' | 'fahrenheit')
Convert natural language into API calls
e.g. convert "Who are my top customers?" to get_customers(min_revenue: int, created_before: string, limit: int) and call your internal API
Extract structured data from text
e.g. define a function called extract_data(name: string, birthday: string), or sql_query(query: string)
Create chatbots that answer questions by calling external APIs (e.g. like ChatGPT Plugins)
e.g. define functions like send_email(to: string, body: string), or get_current_weather(location: string, unit: 'celsius' | 'fahrenheit')
Convert natural language into API calls
e.g. convert "Who are my top customers?" to get_customers(min_revenue: int, created_before: string, limit: int) and call your internal API
Extract structured data from text
e.g. define a function called extract_data(name: string, birthday: string), or sql_query(query: string)
