This article provides a comprehensive, step‑by‑step guide to Microsoft AutoGen 0.4, explaining its layered architecture, core concepts such as Agent, Runtime, and Agent ID, and demonstrating both a simple Hello‑World multi‑agent example and an AI‑enabled agent with full Python code snippets.
This article introduces LlamaIndex Workflows, explains its event‑driven design, walks through a multi‑agent demo that combines weather search and email sending, provides complete Python code for defining events, steps, and the orchestrator, and compares its strengths and limitations against similar frameworks.
AutoGen, Microsoft’s advanced multi‑agent framework, lets developers quickly assemble collaborative LLM agents—supporting chat, tool use, and hierarchical group chats—through concise Python code, with examples ranging from simple two‑agent dialogues to complex three‑agent reporting pipelines, while outlining its strengths, limitations, and upcoming v0.4 enhancements.
Alibaba’s AI digital employee “AI XiaoWan” uses a native multi‑agent architecture where a Controller Agent interprets intent, plans tasks, and orchestrates execution while an Executable Agent performs domain‑specific operations, communicating via a standardized Agent Communication Protocol, leveraging a centralized Tool Center, a retrieval‑augmented knowledge base, and a data‑flywheel feedback loop to continuously improve and evolve toward memory‑based reasoning and self‑learning.
This article introduces CrewAI, a high‑level Python framework for constructing multi‑agent systems, explains its core concepts such as Crew, Agent, Tool, Task and Process, walks through a complete demo with code, evaluates its strengths and limitations, and showcases the new Flows feature for more flexible workflow orchestration.
This article walks through implementing a multi‑agent workflow using LangGraph, comparing it with the lightweight Swam framework, and detailing the code for defining models, tools, agents, graph structures, testing, and evaluating the framework's strengths, limitations, and suitable use cases.
This article details how to design, implement, and evaluate a complex‑table intelligent Q&A solution for automotive maintenance using large language models, RAG pipelines, multi‑agent architectures, prompt engineering, and Alibaba Cloud services, achieving up to 93.8% accuracy.
This article explains why large‑language‑model agents become hard to manage, outlines the benefits of modular multi‑agent designs, compares several connection architectures, and provides concrete LangGraph code for supervisor‑based, tool‑calling, and custom workflow patterns.
This article details Huolala's exploration of large‑language‑model (LLM) based AI assistants across multiple business scenarios, describing their architecture, implementation challenges, prompt engineering techniques, and the progressive stages from professional assistants to multi‑agent systems that drive efficiency and innovation in logistics.
This article presents 37 Mobile Games' exploration of AI-driven intelligent analysis, covering abnormal diagnosis, root‑cause analysis, QBI fluctuation insights, AI data analysis reports, and a multi‑agent workflow for generating analytical reports within a gaming BI platform.
This article examines the typical business and technical architectures for large language model applications, covering AI Embedded, Copilot, and Agent modes, single‑ and multi‑agent systems, core frameworks, and guidance on selecting appropriate technical routes.
Microsoft Research’s AutoGen Studio offers a low‑code web and Python interface built on the open‑source AutoGen framework, enabling developers to quickly prototype, enhance, and combine AI agents into complex workflows while providing drag‑and‑drop design, debugging tools, and Azure integration for secure, scalable multi‑agent applications.
This article presents a comprehensive technical overview of how large AI models are reshaping financial data production, governance, multimodal document understanding, lakehouse storage, private‑domain model deployment, data‑centric engineering methods, and multi‑agent intelligent advisory within the finance sector.
This article explains how to construct a multi‑agent AI research assistant using LangGraph and the open‑source GPT‑Researcher project, detailing the system architecture, agent roles, state design, workflow creation, parallel sub‑processes, and code examples for autonomous online research and report generation.
This article introduces Alibaba Tongyi Lab's multimodal mobile AI agent, Mobile‑Agent, covering the background of large‑model agents, the design and capabilities of V1 and V2, the multi‑agent framework, evaluation results, open‑source resources, and future development directions.
This article explains how Baidu Cloud evolved from manual and integrated network fault detection to AI-driven localization using large language models, detailing structured prompting, multi‑agent workflows, and real‑world comparisons that demonstrate improved accuracy and faster mitigation.
This article explains how Baidu Cloud evolved its physical network fault‑location workflow—from manual analysis and integrated multi‑signal algorithms to AI‑driven reasoning with large language models—highlighting structured prompting, multi‑agent collaboration, and measurable improvements in accuracy and automation.
This article walks through creating a LangGraph‑based multi‑agent application that automatically generates a movie scene, selects characters, simulates dialogue, and writes the script to a Word document using local Ollama‑powered LLMs and a custom workflow.
This article explores JD Retail's AI‑driven multi‑agent system that mimics real‑world merchant decision processes, detailing the ReAct paradigm, agent roles, workflow, training methods, monitoring, and future directions for building intelligent e‑commerce assistants.
The article describes JD Retail's AI‑driven multi‑agent platform that models real‑world merchant decision‑making with ReAct‑based LLM agents, detailing the system architecture, agent roles, reasoning loops, workflow examples, training pipelines, monitoring, and future directions for e‑commerce support.
This article presents a comprehensive overview of JD Retail's AI‑driven multi‑agent architecture for e‑commerce assistance, detailing how real‑world merchant decision processes are modeled with ReAct‑based LLM agents, the hierarchical workflow, training pipelines, monitoring mechanisms, and future directions for scalable intelligent commerce support.
This article explores why large‑model Agent technology is essential for AIOps, explains single‑ and multi‑Agent architectures, memory and tool integration, and demonstrates practical applications such as anomaly detection, fault diagnosis, automated remediation, ChatOps, and future directions for intelligent, autonomous operations.
The article introduces AgentUniverse, a large‑model multi‑agent framework that orchestrates specialized agents through a PEER collaboration pattern to overcome LLM limitations in complex financial tasks, demonstrates its architecture, workflow, experimental superiority on benchmarks, and provides open‑source installation details.
This article reviews the common business and technical architectures used in large language model (LLM) applications, explains AI Embedded, AI Copilot, and AI Agent modes—including single‑ and multi‑agent systems—and offers guidance on selecting appropriate technology stacks such as prompt‑only, function‑calling agents, RAG, and fine‑tuning.
This article surveys the emerging field of large‑language‑model (LLM) based agents, detailing their modular architecture—including profiling, memory, planning, and action components—while discussing critical challenges such as role‑playing, memory design, reasoning, multi‑agent collaboration, and outlining promising research directions and practical case studies.
This tutorial walks through installing LangChain, LangGraph and related packages in Google Colab, configuring environment variables, defining search and Twitter‑writer tools, constructing a StateGraph workflow with supervisor logic, and executing a multi‑agent LLM pipeline using LangChain’s new multi‑agent capabilities.
This article reviews the background of multi‑agent systems, compares major open‑source frameworks such as AutoGen, MetaGPT, AgentVerse, and XAgent, discusses design principles, collaboration strategies, and offers conclusions on LLM‑driven versus SOP‑driven approaches for building multi‑agent applications.
This article introduces the experimental open‑source project ‘Agents’, explains common challenges of LLM‑based AI agents, compares it with tools like AutoGPT, LangChain and MetaGPT, and demonstrates how its configuration‑driven SOP approach enables more controllable, multi‑agent interactions and easier deployment.
At WSDM 2022, Alibaba’s advertising team presented four papers introducing a meta‑learning multi‑task multi‑scenario model for advertiser forecasting, a low‑cost Feature Co‑Action Network that boosts CTR prediction, an Adaptive Unified Allocation Framework that improves guaranteed display fulfillment and CTR, and a cooperative‑competitive multi‑agent auto‑bidding system that enhances both advertiser welfare and platform profit.
Tencent AI Lab’s WeKick AI clinched the inaugural Google Football Kaggle championship with a score of 1785.8, outpacing over 1,100 research teams by leveraging a migrated full‑stack architecture, custom reinforcement‑learning frameworks, GAIL imitation learning, and a multi‑style League training pipeline that together showcase the power and generality of deep reinforcement learning for complex multi‑agent tasks.
This article explains why Alibaba's Cognitive Computing Lab uses StarCraft as a research platform, outlines the game's unique challenges for AI, and details their deep reinforcement learning and multi‑agent approaches, including the BiCNet architecture and experimental results.
