Thanks for exploring LangChain Education. We offer a few different ways to learn – including courses, YouTube videos, and documentation – and I’d love to give you a quick overview to help you find the format that works for you. LangChain Academy offers three types of courses: Foundational, Project, and Quickstart.Foundational courses cover topics very methodically, taking you from introduction to mastery. These are longer courses and will take some time to complete. Project courses guide you through buildin ...

Open Source LLM Framework - A framework connects any LLM to any MCP server (open-source) [1] - The framework enables building custom MCP Agents without closed-source apps [1] - Compatible with Ollama, LangChain, etc [1] - Allows building 100% local MCP clients [1]

LLM & MCP Integration - A framework enables connecting any LLM to any MCP server [1] - The framework facilitates building custom MCP Agents without relying on closed-source applications [1] - It is compatible with tools like Ollama and LangChain [1] - The framework allows building 100% local MCP clients [1]

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 ...

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 the Agents (just like MCP Cl ...

Core Insights - The article discusses the challenges faced by Go developers in creating AI applications, highlighting the need for a native AI development experience tailored for Go language [1][2] - It introduces a new framework, Eino, aimed at enabling Go developers to build AI agents and applications more efficiently [4][5] Group 1: Event Overview - A live demonstration will be organized for backend developers, focusing on the Deep Research application, Deerflow, which utilizes LangChain and LangGraph [4] - The goal of the live session is to build a complete AI application from scratch using the Eino framework, showcasing the architecture and design principles [4][5] Group 2: Expert Involvement - Two engineers from ByteDance will participate in the event, with one acting as the "architect decoder" to explain the design of Deerflow, and the other as the "Go AI application master" to demonstrate the implementation using Eino [5] - This collaboration aims to provide insights into defining powerful AI agents and the practical application of the Eino framework [5][7] Group 3: Target Audience - The event is targeted at Go developers looking to enhance their competitive edge in AI, AI/LLM application developers seeking efficient frameworks, and backend engineers curious about AI technology [7] - Participants are encouraged to engage with the content if they are passionate about creating intelligent solutions through code [7] Group 4: Event Details - The live session is scheduled for July 9, 2025, at 7:30 PM, with opportunities for participants to win custom prizes [8] - Registration is available through a QR code for reminders and exclusive materials [8] Group 5: Conclusion - The article emphasizes the potential for a significant shift in the Go language's capabilities in the AI agent domain, promising an exciting event for attendees [9]

Core Insights - The article discusses the transformation in the AI industry, emphasizing the shift from individual AI model usage to a collaborative network of agents, termed as "Agent collaboration network" [8][10][27] - It highlights the urgency for AI professionals to adapt their skills from prompt engineering to organizing and managing AI collaborations, as traditional skills may become obsolete [9][21][30] Group 1: Industry Trends - The AI landscape is evolving towards a multi-agent system where agents communicate and collaborate autonomously, moving away from reliance on human prompts [27][14] - The emergence of protocols like MCP (Multi-agent Communication Protocol) and A2A (Agent-to-Agent) is facilitating this transition, allowing for standardized communication between different AI systems [36][37] - Major companies like Alibaba, Tencent, and ByteDance are rapidly developing platforms that support these new protocols, enabling easier integration and deployment of AI agents [38][39] Group 2: Skills Transformation - AI professionals need to transition from being prompt engineers to "intent architects," focusing on defining task languages and collaboration protocols for agents [29][30] - The role of AI practitioners is shifting from using agents to organizing and managing multiple agents, requiring a new mindset akin to building a digital team [30][31] - There is a call for professionals to learn about agent frameworks, communication protocols, and how to register their tools as agent capabilities within larger networks [33][34] Group 3: Practical Applications - Various platforms and frameworks are emerging that allow AI professionals to practice and implement these new skills, such as LangGraph, AutoGen, and CrewAI [41] - The article emphasizes that the infrastructure for agent protocols is being established, providing opportunities for AI professionals to engage with these technologies [41][42] - The ongoing development of these systems is likened to the early days of TCP/IP, suggesting that those who adapt early will have a competitive advantage in the evolving AI landscape [42]

曲凯： 今天我们请来了国内强化学习 (RL) 领域的专家吴翼，吴翼目前是清华大学交叉信息研究院助理教授，他曾经在 OpenAI 工作过，算是国内最早研究强化学 习的人之一，我们今天就争取一起把 RL 这个话题给大家聊透。 首先吴翼能不能简单解释一下，到底什么是 RL？ 因此，RL 其实更通用一些，它的逻辑和我们在真实生活中解决问题的逻辑非常接近。比如我要去美国出差，只要最后能顺利往返，中间怎么去机场、选什么航 司、具体坐哪个航班都是开放的。 但 RL 很不一样。 RL 最早是用来打游戏的，而游戏的特点和分类问题有两大区别。 第一，游戏过程中有非常多的动作和决策。比如我们玩一个打乒乓球的游戏，发球、接球、回球，每一个动作都是非标的，而且不同的选择会直接影响最终的结 果。 第二，赢得一场游戏的方式可能有上万种，并没有唯一的标准答案。 所以 RL 是一套用于解决多步决策问题的算法框架。它要解决的问题没有标准答案，每一步的具体决策也不受约束，但当完成所有决策后，会有一个反馈机制来评 判它最终做得好还是不好。 吴翼： RL 是机器学习这个大概念下一类比较特殊的问题。 传统机器学习的本质是记住大量标注过正确答案的数据对。 ...

以下文章来源于硅星人Pro ，作者周一笑 本文来自微信公众号： 硅星人Pro （ID：gh_c0bb185caa8d） ，作者：周一笑，题图来自：AI生成 AI领域的互操作性标准之争正变得异常热闹。就在本周，Google DeepMind CEO Demis Hassabis宣布，将为其Gemini模型添加对Anthropic主导的模型 上下文协议MCP的支持，称其为 "一个优秀的协议，正迅速成为AI Agent时代的开放标准"。 这距离OpenAI CEO Sam Altman宣布支持MCP仅仅过去 几周时间。 几乎在同一时间段，Google Cloud也高调推出了自己主导的、旨在实现 AI Agent之间协作和工作流自动化的开放协议 ——Agent2Agent （A2A） ，并 联合了包括Salesforce、Box等在内的50多家技术伙伴。 这种看似"左右互搏"的举动——既拥抱竞争对手的标准，又力推自家的新协议，恰恰凸显了当前行业对于如何让AI模型、Agent更有效地连接外部数 据、工具乃至彼此协作的迫切需求与探索。尽管A2A的目标是Agent间的通信与协作，Google对MCP的明确支持，仍然被视为M ...