LLM Multi Agent

大模型多智能体

Focus on Multi-Agent Applications in Large Models

FastMCP 2.0: Building the USB-C of AI with Python

A comprehensive guide to FastMCP 2.0, the Python framework revolutionizing how LLMs connect with external tools and resources through the Model Context Protocol.

Building Intelligent Agents ---- A Practical Framework from Concept to Deployment

A comprehensive guide to building AI agents that deliver real value, from defining realistic tasks to deploying and iterating in production using a proven six-step methodology.

Beyond the Tool ---- Crafting AI Products with Technical Excellence and Human-Centered Design

Exploring the evolution of AI products from functional tools to meaningful companions through engineering excellence, context management, and human-centric design principles

MemOS----Revolutionizing LLM Memory Management as a First-Class Operating System

Explore how MemOS transforms LLM capabilities by elevating memory to a first-class resource, solving critical challenges in knowledge retention, context management, and personalized AI interactions through innovative memory architecture.

The Rise of Context Engineering--Building the Foundation for Next-Generation AI Agents

Exploring how context engineering is transforming AI agent development, bridging the gap between prompt engineering and robust agent architectures through practical insights and real-world applications

The Art of Multi-Agent Collaboration：Deep Reflections on Anthropic's Research System Engineering

A comprehensive reflection on Anthropic's multi-agent research system construction experience, exploring modern AI system architecture design, engineering challenges, and practical insights. From philosophical significance to engineering practice, from architectural wisdom to future prospects, this article provides a thorough analysis of multi-agent system design principles and implementation approaches.

AutoGen and MCP： Building Powerful Multi-Agent Systems

An in-depth exploration of Microsoft's Model Context Protocol (MCP) in the AutoGen framework, and how to leverage it to build powerful multi-agent systems with Jira MCP, Confluence MCP, and Github MCP.

A2A----Ushering in a New Era of Agent Interoperability

This blog post introduces Google's A2A (Agent to Agent) framework, an open standard and protocol designed to enable seamless interoperability between intelligent agents.

OpenManus Technical Analysis - Architecture and Implementation of an Open-Source Agent Framework

OpenManus is an open-source agent framework designed to enable users to perform complex tasks through simple instructions, featuring a modular architecture with core components like the Agent Layer, Tool Layer, and LLM Interaction Layer. It supports tasks such as programming, web browsing, and file processing, offering a flexible and extensible platform for building AI agents, with future plans to enhance planning capabilities, expand tool sets, and improve user experience.

AI Agent

This blog is a brief summary of the Agent application section in the paper "Agent AI： Surveying the Horizons of Multimodal Interaction" by Professor Fei-Fei Li et al.

Agentic AI：The Key to Unlocking a New Era of Intelligence

A summary of OPENAI's paper "Practices for Governing Agentic AI Systems," discussing the potential benefits of Agentic AI and how to maintain its safety.

AutoGen

An Open-Source Programming Framework for Agentic AI.

LangChain

LangChain is a framework for developing applications powered by large language models.

MCP

MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.