8 分钟上下文退化:输入令牌增加如何影响LLM性能
理解长上下文在存在歧义和干扰项时如何失效,并围绕这种风险设计测试。
aiwithbrandon. 这是同类构建教程,但采用CrewAI的“角色—目标—背景故事”风格:把智能体视为团队成员,把任务视为交付物,由框架隐藏执行循环。建议看完LangGraph课程后立即观看;两者在框架替你做多少决定方面的鲜明对比,正是文章要求你权衡的重点。
CrewAI迭代很快,教程代码也很快过时。用本视频了解“角色—目标—背景故事”模型及其与LangGraph的差异,然后在复制任何代码前查看CrewAI的当前文档。
你可以用“角色—目标—背景故事”风格搭建CrewAI团队,并判断其隐藏的执行循环与显式图结构相比如何。
同样需要Python基础;最好紧接着LangGraph课程观看,以便形成对比。
最后审核:2026年5月18日
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Microsoft Copilot Studio team
The deeper, production-minded counterpart to our beginner no-code pick: a free, open-source, rank-based curriculum that takes you from zero Copilot Studio experience through MCP integrations and multi-agent orchestration, all without writing traditional code. It's the no-code answer to 'now I want to go further than a quick-start,' which our catalog didn't have.
Anthropic Academy
MCP正在悄然取代AI工具生态中为单一用途开发的工具集成。请直接向标准的创建者学习。学完后,你将构建并部署自己的MCP服务器,把一个LLM客户端连接到该服务器,并理解为何这项标准堪称该领域最接近USB-C的方案。
João Moura (Founder, CrewAI)
Doubles as our sales and customer-support vertical pick and a genuinely practical agent-building course: you build an agentic sales pipeline (lead scoring, personalized outreach) and a customer-support data-insights pipeline as two of the five hands-on projects, taught by CrewAI's own founder. Requires basic Python, so it sits with our other builder-track courses rather than the no-code picks.