MultiAgent
Building Advanced MCP (Model Context Protocol) Agents with Multi-Agent Coordination, Context Awareness, and Gemini Integration
class MCPAgent: “””Advanced MCP Agent with evolved capabilities – Jupyter Compatible””” def __init__(self, agent_id: str, role: AgentRole, api_key: str = None): self.agent_id = agent_id self.role = role self.api_key = api_key self.memory = [] self.context = AgentContext( agent_id=agent_id, role=role, capabilities=self._init_capabilities(), memory=[], tools=self._init_tools() ) self.model = None if GEMINI_AVAILABLE and api_key: try: genai.configure(api_key=api_key) self.model = genai.GenerativeModel(‘gemini-pro’) print(f”✅…
Google AI Introduces Personal Health Agent (PHA): A Multi-Agent Framework that Enables Personalized Interactions to Address Individual Health Needs
What is a Personal Health Agent? Large language models (LLMs) have demonstrated strong performance across various domains like clinical reasoning, decision support, and consumer health applications. However, most existing platforms are designed as single-purpose tools, such as symptom checkers, digital coaches, or health information assistants. These approaches often fail to address the complexity of real-world…
Alibaba Qwen Team Releases Mobile-Agent-v3 and GUI-Owl: Next-Generation Multi-Agent Framework for GUI Automation
Image source: Marktechpost.com Introduction: The Rise of GUI Agents Modern computing is dominated by graphical user interfaces across devices—mobile, desktop, and web. Automating tasks in these environments has traditionally been limited to scripted macros or brittle, hand-engineered rules. Recent advances in vision-language models offer the tantalizing possibility of agents that can understand screens, reason about…
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…
Building a Multi-Agent Conversational AI Framework with Microsoft AutoGen and Gemini API
class GeminiAutoGenFramework: “”” Complete AutoGen framework using free Gemini API Supports multi-agent conversations, code execution, and retrieval “”” def __init__(self, gemini_api_key: str): “””Initialize with Gemini API key””” self.gemini_api_key = gemini_api_key self.setup_gemini_config() self.agents: Dict[str, autogen.Agent] = {} self.group_chats: Dict[str, GroupChat] = {} def setup_gemini_config(self): “””Configure Gemini for AutoGen””” os.environ[“GOOGLE_API_KEY”] = self.gemini_api_key self.llm_config = { “config_list”: […
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 Advanced Multi-Agent AI Workflows by Leveraging AutoGen and Semantic Kernel
In this tutorial, we walk you through the seamless integration of AutoGen and Semantic Kernel with Google’s Gemini Flash model. We begin by setting up our GeminiWrapper and SemanticKernelGeminiPlugin classes to bridge the generative power of Gemini with AutoGen’s multi-agent orchestration. From there, we configure specialist agents, ranging from code reviewers to creative analysts, demonstrating…
Beyond single-model AI: How architectural design drives reliable multi-agent orchestration
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More We’re seeing AI evolve fast. It’s no longer just about building a single, super-smart model. The real power, and the exciting frontier, lies in getting multiple specialized AI agents to work together. Think of them as…
Meet PC-Agent: A Hierarchical Multi-Agent Collaboration Framework for Complex Task Automation on PC
Multi-modal Large Language Models (MLLMs) have demonstrated remarkable capabilities across various domains, propelling their evolution into multi-modal agents for human assistance. GUI automation agents for PCs face particularly daunting challenges compared to smartphone counterparts. PC environments present significantly more complex interactive elements with dense, diverse icons and widgets often lacking textual labels, leading to perception…
Meet AI Co-Scientist: A Multi-Agent System Powered by Gemini 2.0 for Accelerating Scientific Discovery
Biomedical researchers face a significant dilemma in their quest for scientific breakthroughs. The increasing complexity of biomedical topics demands deep, specialized expertise, while transformative insights often emerge at the intersection of diverse disciplines. This tension between depth and breadth creates substantial challenges for scientists navigating an exponentially growing volume of publications and specialized high-throughput technologies….