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adongwanai/agentguide

4,740HTMLAudience · developerComplexity · 3/5Setup · moderate

TLDR

A Chinese-language structured study guide and job-search roadmap for developers wanting to learn AI agent development using frameworks like LangChain, AutoGen, and CrewAI.

Mindmap

mindmap
  root((AgentGuide))
    What it covers
      AI agent frameworks
      RAG systems
      Vector databases
      Model fine-tuning
    Frameworks
      LangChain
      LangGraph
      AutoGen
      CrewAI
    Job search
      Role selection
      Resume writing
      LinkedIn outreach
      Interview prep
    Resources
      1000 interview questions
      Practice projects
      Open-source tools
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Things people build with this

USE CASE 1

Follow the six-step job-search roadmap to land an AI engineering role

USE CASE 2

Learn to build RAG systems that answer questions from your own documents using vector databases

USE CASE 3

Study multi-agent system design using LangGraph and CrewAI

USE CASE 4

Practice interview prep with over 1,000 curated AI engineering questions

Tech stack

PythonLangChainLangGraphAutoGenCrewAIMilvusChroma

Getting it running

Difficulty · moderate Time to first run · 1h+

Content is primarily in Chinese, requires familiarity with that language to fully use the guide.

License not specified in the explanation.

In plain English

AgentGuide is a Chinese-language study guide aimed at developers who want to learn AI agent development and land a job in that field. The README describes it as a structured, job-search-oriented resource rather than a simple link dump. The content is written primarily in Chinese, with some English technical terms throughout. The guide covers several technical areas: building AI agents using frameworks like LangChain, LangGraph, AutoGen, CrewAI, and Swarm, building RAG (retrieval-augmented generation) systems that let language models answer questions using external documents, setting up vector databases such as Milvus and Chroma, and training or fine-tuning language models using techniques like LoRA, reinforcement learning from human feedback, and similar approaches. It also covers multi-agent systems where multiple AI components collaborate on a task. Alongside the technical content, the guide includes a job search roadmap. This covers how to choose between an algorithm-research role and an engineering-development role, how to frame your resume around what you built rather than what you studied, how to find hiring managers on LinkedIn and reach out directly, and how to approach the interview process for AI-focused positions. The guide describes a six-step path from deciding on a target job type all the way to preparing for offers. The project also curates links to open-source tools, practice projects, and a question bank of over 1,000 interview questions. The author describes themselves as a working large-model algorithm engineer, and positions the guide as distilling practical field experience rather than academic theory. The full README is longer than what was shown.

Copy-paste prompts

Prompt 1
Using the AgentGuide approach, help me build a RAG system with LangChain that answers questions from my company's internal documents stored in a Chroma vector database
Prompt 2
Based on AgentGuide's resume advice, help me write three bullet points describing an AI agent I built with CrewAI for my job application
Prompt 3
Walk me through setting up a multi-agent workflow using AutoGen where one agent researches a topic and another summarizes the findings
Prompt 4
Help me implement LoRA fine-tuning on a small language model following the AgentGuide curriculum
Prompt 5
Using AgentGuide's interview prep approach, quiz me on 10 AI agent architecture questions and give feedback on my answers
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