Implementation
A Coding Implementation of an Advanced Tool-Using AI Agent with Semantic Kernel and Gemini
In this tutorial, we build an advanced AI agent using Semantic Kernel combined with Google’s Gemini free model, and we run it seamlessly on Google Colab. We start by wiring Semantic Kernel plugins as tools, like web search, math evaluation, file I/O, and note-taking, and then let Gemini orchestrate them through structured JSON outputs. We…
A Code Implementation to Build a Multi-Agent Research System with OpenAI Agents, Function Tools, Handoffs, and Session Memory
In this tutorial, we begin by showcasing the power of OpenAI Agents as the driving force behind our multi-agent research system. We set up our Colab environment with the OpenAI API key, installed the OpenAI Agents SDK, and then defined custom function tools, web_search, analyze_data, and save_research, to harness the agents’ capabilities. We instantiate three…
A Code Implementation for Designing Intelligent Multi-Agent Workflows with the BeeAI Framework
BeeAI FrameworkIn this tutorial, we explore the power and flexibility of the beeai-framework by building a fully functional multi-agent system from the ground up. We walk through the essential components, custom agents, tools, memory management, and event monitoring, to show how BeeAI simplifies the development of intelligent, cooperative agents. Along the way, we demonstrate how…
Building Event-Driven AI Agents with UAgents and Google Gemini: A Modular Python Implementation Guide
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…
A Coding Implementation on Introduction to Weight Quantization: Key Aspect in Enhancing Efficiency in Deep Learning and LLMs
In today’s deep learning landscape, optimizing models for deployment in resource-constrained environments is more important than ever. Weight quantization addresses this need by reducing the precision of model parameters, typically from 32-bit floating point values to lower bit-width representations, thus yielding smaller models that can run faster on hardware with limited resources. This tutorial introduces…