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AgentGuide
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AgentGuide

Active·★ 11.1k·Updated 2026-09-15
★ Most Popular

AgentGuide is a Chinese-language guide for learning AI agent development and preparing for jobs in the field, modelled on the JavaGuide study guide. Its stated principle is to link to good existing courses, tutorials and papers rather than rewrite them, organise them into a systematic path, and mark every topic with how it is asked in interviews and how to put it on a résumé.

The README sets out a six-step path from choosing a target role to landing an offer. It separates an algorithm-engineer track from a development track, with study roadmaps of 10 to 15 weeks and 8 to 12 weeks respectively, then covers hands-on projects for the résumé, technical tutorials (LangChain and LangGraph, AutoGen, CrewAI, Swarm, advanced RAG, context engineering, supervised fine-tuning and reinforcement learning) and an interview question bank of 1,000+ questions with system-design and coding questions. Example projects include a paper-retrieval agent and a multi-agent travel planner.

This write-up is based on the project's README and public repository data. We have not hand-tested the tool. Data as of 2026-10-17.

License
Not detected by GitHub
Latest release
No GitHub releases
Last commit
2026-09-15
Contributors
6
Forks
1,026
Open issues and PRs
9
First published
2025-11-03

Source: GitHub, npm and PyPI public APIs.

#AI Agent#RAG#LLM#LangChain#Multi-Agent#Coding#Data Analysis
$ Install
$ git clone https://github.com/adongwanai/AgentGuide.git
↗ Visit site★ GitHub
01

Features

01Systematic learning path from zero to job offer
02100% career-oriented with interview focus
03Multiple resume-level practical projects
04Covers both algorithm and development tracks
05Fully open-source and continuously updated
02

Why choose it

+Gives a step-by-step path from picking a role to interview preparation, with separate algorithm and development tracks.
+Covers a broad stack: agent frameworks, advanced RAG, multi-agent patterns, context engineering and fine-tuning.
+Ties each topic to interview questions and résumé wording, with a bank of 1,000+ questions.
+Links to existing courses and papers instead of duplicating them.
03

Trade-offs

!It is a curated reading and interview-prep list, not software, so there is nothing to install and the value depends on the linked resources staying current.
!It is written in Chinese and aimed at the Chinese job market, and GitHub shows no licence.
!As a resource guide it relies on third-party material and does not itself provide original courses.
05

Maintenance check

ActiveChecked: 2026-10-18
Last commit
2026-09-15
Commits, last 90 days
33
Releases, last 90 days
0
Latest release
—
Open issues
9
Recent issues with at least one comment
23/30
Commits per week, last 12 weeks
Commits per week, last 12 weeks: 5, 0, 0, 4, 5, 0, 1, 0, 0, 0, 0, 0

Oldest week on the left; the latest four weeks are orange. Source: GitHub commit activity.

Status rule: archived on GitHub = Archived; last commit within 90 days = Active; 91 to 365 days = Slowing; older = Stale. All figures come from the GitHub API on the date shown.

Most discussed open issues

  • #169 建议在外部学习资源旁标注最近核验时间0 comments · 2026-09-23
  • #168 docs: FAQ 的根目录依赖安装命令指向不存在的 requirements.txt0 comments · 2026-09-18

Issue titles are quoted from GitHub. Open the link to check whether a problem still applies to the version you use.

04

Who it fits

Pick it if
  • You are an engineer or student looking for a structured, career-focused learning roadmap for AI Agent and LLM development in Chinese.
  • You need real-world LangGraph and Advanced RAG project tutorials along with a 1,000+ question interview bank.
Look elsewhere if
  • You are looking for an executable Python library or framework SDK rather than a tutorial/curated guide repository.
  • You require English-only documentation and guides.
06

How it differs

agentscope logovs agentscope★ 33.6k

AgentGuide is an educational roadmap and interview preparation resource. AgentScope is an active production Python framework for building and running multi-agent applications.

Full comparison →
07

Compatibility

LangGraph
Supported
Verified via docs
AutoGen
Supported
Verified via docs
CrewAI
Supported
Verified via docs
Dify
Supported
Verified via docs
Milvus
Supported
Verified via docs
PyTorch
Supported
Verified via docs
08

Quick start

1
$ git clone https://github.com/adongwanai/AgentGuide.git
09

Use cases

↳Developing advanced AI Agent applications for automation or research
↳Building and optimizing Retrieval-Augmented Generation (RAG) systems
↳Designing and implementing Multi-Agent collaboration systems
10

Alternatives

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An autonomous agent that conducts deep research on any data using any LLM provider, producing detailed reports with citations from web and local sources.
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On this page
01Features02Why choose it03Trade-offs05Maintenance check04Who it fits06How it differs07Compatibility08Quick start09Use cases10Alternatives
adongwanai /
AgentGuide
↗

Chinese study guide that links AI agent learning resources to a job-search path

11.1k1.0k
Last commit1mo ago
StatusActive
CategoryRAG / Knowledge Base
Contributors6
Links
Documentation↗Discussion↗Issues↗Releases↗
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