面向求职编程，目前国内使用最广泛的 AI AGENT 是哪些？

[ReinerShir]

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？

[TimePPT]

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。

[code0611]

@TimePPT 那大公司用啥

[nbndco]

你是说需要学习如何用 ai 编程？还是要开发 ai agent 平台？

[TimePPT]

@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.