目前已知的有低代码的 coze dify ，基于 python 的 langchian langgraph autogen
仅面向求职，研究哪个工具比较好？
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TimePPT 1 天前 via Android
运营还是开发？应用开发小公司 dify ，langchain 会问。大公司即使做 LLM 应用开发，但如果不是专门做 Agent 平台开发的基本不会问这块，因为生产环境基本不用这些。
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nbndco 1 天前
你是说需要学习如何用 ai 编程？还是要开发 ai agent 平台？
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TimePPT 1 天前 via Android
@code0611 大公司做业务 LLM 部署和应用部署都是微服务多些，业务逻辑层如果需要配置单抽出来做配置中心多些，dify coze 这种低代码可视化配置都是纯运营托管的场景才用的多，但大多数场景根本不会有专有运营天天整 workflow
这个也不是我说的，基本上是业界共识，Anthropic 官方 Agent 指南里也提到了类似观点 Building effective agents https://www.anthropic.com/research/building-effective-agents When and how to use frameworks There are many frameworks that make agentic systems easier to implement, including: – LangGraph from LangChain; – Amazon Bedrock's AI Agent framework; – Rivet, a drag and drop GUI LLM workflow builder; and – Vellum, another GUI tool for building and testing complex workflows. These frameworks make it easy to get started by simplifying standard low-level tasks like calling LLMs, defining and parsing tools, and chaining calls together. However, they often create extra layers of abstraction that can obscure the underlying prompts and responses, making them harder to debug. They can also make it tempting to add complexity when a simpler setup would suffice. We suggest that developers start by using LLM APIs directly: many patterns can be implemented in a few lines of code. If you do use a framework, ensure you understand the underlying code. Incorrect assumptions about what's under the hood are a common source of customer error. See our cookbook for some sample implementations. |
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