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最新Agent框架,读这一篇就够了
自动驾驶之心· 2025-08-18 23:32
Core Viewpoint - The article discusses various mainstream AI Agent frameworks, highlighting their unique features and suitable application scenarios, emphasizing the growing importance of AI in automating complex tasks and enhancing collaboration among agents [1]. Group 1: Mainstream AI Agent Frameworks - Current mainstream AI Agent frameworks are diverse, each focusing on different aspects and applicable to various scenarios [1]. - The frameworks discussed include LangGraph, AutoGen, CrewAI, Smolagents, and RagFlow, each with distinct characteristics and use cases [1][2]. Group 2: CrewAI - CrewAI is an open-source multi-agent coordination framework that allows autonomous AI agents to collaborate as a cohesive team to complete tasks [3]. - Key features of CrewAI include: - Independent architecture, fully self-developed without reliance on existing frameworks [4]. - High-performance design focusing on speed and resource efficiency [4]. - Deep customizability, supporting both macro workflows and micro behaviors [4]. - Applicability across various scenarios, from simple tasks to complex enterprise automation needs [4][7]. Group 3: LangGraph - LangGraph, created by LangChain, is an open-source AI agent framework designed for building, deploying, and managing complex generative AI agent workflows [26]. - It utilizes a graph-based architecture to model and manage the complex relationships between components in AI workflows [28]. Group 4: AutoGen - AutoGen is an open-source framework from Microsoft for building agents that collaborate through dialogue to complete tasks [44]. - It simplifies AI development and research, supporting various large language models (LLMs) and advanced multi-agent design patterns [46]. - Core features include: - Support for agent-to-agent dialogue and human-machine collaboration [49]. - A unified interface for standardizing interactions [49][50]. Group 5: Smolagents - Smolagents is an open-source Python library from Hugging Face aimed at simplifying the development and execution of agents with minimal code [67]. - It supports various functionalities, including code execution and tool invocation, while being model-agnostic and easily extensible [70]. Group 6: RagFlow - RagFlow is an end-to-end RAG solution focused on deep document understanding, addressing challenges in data processing and answer generation [75]. - It supports various document formats and intelligently identifies document structures to ensure high-quality data input [77][78]. Group 7: Summary of Frameworks - Each AI Agent framework has unique characteristics and suitable application scenarios: - CrewAI is ideal for multi-agent collaboration and complex task automation [80]. - LangGraph is suited for state-driven multi-step task orchestration [81]. - AutoGen is designed for dynamic dialogue processes and research tasks [86]. - Smolagents is best for lightweight development and rapid prototyping [86]. - RagFlow excels in document parsing and multi-modal data processing [86].
X @Avi Chawla
Avi Chawla· 2025-07-02 19:45
RT Avi Chawla (@_avichawla)After MCP, A2A, & AG-UI, there's another Agent protocol (open-source).ACP (Agent Communication Protocol) is a standardized, RESTful interface for Agents to discover and coordinate with other Agents, regardless of their framework (CrewAI, LangChain, etc.).Here's how it works:- Build your Agents and host them on ACP servers.- The ACP server will receive requests from the ACP Client and forward them to the Agent.- ACP Client itself can be an Agent to intelligently route requests to t ...
X @Avi Chawla
Avi Chawla· 2025-07-02 06:30
Agent Communication Protocols - ACP (Agent Communication Protocol) is presented as a new open-source protocol for agent communication, offering a standardized RESTful interface [1] - The protocol facilitates agent discovery and coordination across different frameworks like CrewAI and LangChain [1] - An ACP Client can function as an intelligent router for requests to agents, similar to MCP Client [1] ACP vs A2A - ACP is designed for local-first, low-latency communication, while A2A is optimized for web-native, cross-vendor interoperability [3] - ACP utilizes a RESTful interface for easier integration, whereas A2A supports more flexible interactions [3] - ACP is suited for controlled, edge, or team-specific environments, while A2A excels in broader cloud-based collaboration [3] Development and Deployment - The industry is encouraged to build ACP-compliant agents using frameworks like CrewAI and Smolagents [3] - Agents can be chained in sequential and hierarchical workflows, managed by ACP clients [3] - Agents can be imported to a public registry for easy discovery [3] - DeeplearningAI offers a course on building and hosting agents on ACP servers [2]