In this tutorial, we walk you through the design and implementation of a custom agent framework built on PyTorch and key Python tooling, ranging from web intelligence and data science modules to advanced code generators. We’ll learn how to wrap core functionalities in monitored CustomTool classes, orchestrate multiple agents with tailored system prompts, and define…
The development and deployment of advanced AI systems increasingly depend on flexible, robust orchestration layers that bridge diverse models, tools, and resources. IBM’s MCP Gateway addresses this need by providing a FastAPI-based gateway for the Model Context Protocol (MCP), offering a unified interface to scale and manage the modern AI toolchain. This article explores MCP…
In this tutorial, we demonstrate how to use the UAgents framework to build a lightweight, event-driven AI agent architecture on top of Google’s Gemini API. We’ll start by applying nest_asyncio to enable nested event loops, then configure your Gemini API key and instantiate the GenAI client. Next, we’ll define our communication contracts, Question and Answer…
The Agent-to-Agent (A2A) protocol is a new standard by Google that enables AI agents—regardless of their underlying framework or developer—to communicate and collaborate seamlessly. It works by using standardized messages, agent cards (which describe what an agent can do), and task-based execution, allowing agents to interact via HTTP without custom integration logic. A2A makes it…
The Importance of Symbolic Reasoning in World Modeling Understanding how the world works is key to creating AI agents that can adapt to complex situations. While neural network-based models, such as Dreamer, offer flexibility, they require massive amounts of data to learn effectively, far more than humans typically do. On the other hand, newer methods…
In this tutorial, we’ll build a powerful and interactive Streamlit application that brings together the capabilities of LangChain, the Google Gemini API, and a suite of advanced tools to create a smart AI assistant. Using Streamlit’s intuitive interface, we’ll create a chat-based system that can search the web, fetch Wikipedia content, perform calculations, remember key…
Introduction AI agents are increasingly moving from pure backend automators to visible, collaborative elements within modern applications. However, making agents genuinely interactive—capable of both responding to users and proactively guiding workflows—has long been an engineering headache. Each team ends up building custom communication channels, event handling, and state management, all for similar interaction needs. The…
OpenAI has open-sourced a new multi-agent customer service demo on GitHub, showcasing how to build domain-specialized AI agents using its Agents SDK. This project—titled openai-cs-agents-demo—models an airline customer service chatbot capable of handling a range of travel-related queries by dynamically routing requests to specialized agents. Built with a Python backend and a Next.js frontend, the…
In this tutorial, we walk you through building an enhanced web scraping tool that leverages BrightData’s powerful proxy network alongside Google’s Gemini API for intelligent data extraction. You’ll see how to structure your Python project, install and import the necessary libraries, and encapsulate scraping logic within a clean, reusable BrightDataScraper class. Whether you’re targeting Amazon…
Python A2A is an implementation of Google’s Agent-to-Agent (A2A) protocol, which enables AI agents to communicate with each other using a shared, standardized format—eliminating the need for custom integration between services. In this tutorial, we’ll use the decorator-based approach provided by the python-a2a library. With simple @agent and @skill decorators, you can define your agent’s…