gitwtfhub

wtf is agent-study?

callous-0923/agent-study — explained in plain English

Analysis updated 2026-05-18

200HTMLAudience · developerComplexity · 3/5Setup · moderate

TL;DR

A 36-chapter Chinese-language course teaching AI agent engineering through runnable Python files, from core theory to production infrastructure.

Mindmap

mindmap
  root((agent-study))
    What it does
      36 chapter course
      Chinese language
      Runnable Python files
    Layers
      Fundamental theory
      Practical frameworks
      Production infra
      Advanced architectures
    Topics
      Multi-agent systems
      RAG
      MCP protocol
      Prompt injection defense
    Use cases
      Interview prep
      Production agent skills
    Audience
      Chinese-speaking developers

Code map

Detail Auto

An interactive map of this repo's files and how they connect — its source is parsed live in your browser. Click Visualize to build it.

filefunction / class

Why would anyone build with this?

REASON 1

Learn the ReAct reasoning-and-acting pattern by running a standalone lesson file

REASON 2

Study how to build multi-agent systems where several AI agents collaborate

REASON 3

Practice retrieval-augmented generation and the MCP tool-calling protocol

REASON 4

Prepare for AI agent engineering job interviews with structured, code-first material

What's in the stack?

Python

How it stacks up

callous-0923/agent-studythiago-code-lab/aws-certified-solutions-architect-associate-brasilgiovapanasiti/active_canvas
Stars200202204
LanguageHTMLHTMLHTML
Setup difficultymoderateeasymoderate
Complexity3/51/52/5
Audiencedevelopergeneraldeveloper

Figures from each repo's GitHub metadata at analysis time.

How do you spin it up?

Difficulty · moderate Time to first run · 30min

Requires Python 3.10 or later, most chapters run without an AI API key.

Wtf does this do

This is a 36-chapter course on building AI agents, written in Chinese and targeted at developers who want to learn AI agent engineering for job interviews and production use. An AI agent is a program that uses a language model not just to answer questions, but to plan, use tools, remember context across steps, and carry out multi-step tasks autonomously. The course progresses through seven layers of depth, starting with fundamental theory like the ReAct loop (a pattern where the AI reasons then acts, then reasons again based on what happened) and moving through practical frameworks, production infrastructure, advanced architectures, and expert-level topics. Each of the 36 chapters is a standalone runnable Python file that serves as both a lesson and working code you can execute and modify. Topics covered include how to build multi-agent systems where several AI agents collaborate, retrieval-augmented generation (giving agents access to searchable knowledge bases), the MCP protocol for standardizing how agents call external tools, agent memory systems, security against prompt injection attacks, how to monitor and trace agent behavior in production, and how to fine-tune models for tool use. You would use this course if you are a Chinese-speaking developer wanting a structured, code-first introduction to AI agent development that prepares you for technical interviews. Most chapters can run without an AI API key. Requires Python 3.10 or later.

Yoink these prompts

Prompt 1
Walk me through the ReAct loop chapter of this course and explain it with a simple example.
Prompt 2
Help me run the multi-agent collaboration chapter from this repo and explain what each part does.
Prompt 3
Show me how the MCP protocol chapter here lets an agent call external tools.
Prompt 4
Use this course's structure to quiz me on AI agent concepts for a job interview.

Frequently asked questions

wtf is agent-study?

A 36-chapter Chinese-language course teaching AI agent engineering through runnable Python files, from core theory to production infrastructure.

What language is agent-study written in?

Mainly HTML. The stack also includes Python.

How hard is agent-study to set up?

Setup difficulty is rated moderate, with roughly 30min to a first successful run.

Who is agent-study for?

Mainly developer.

View the repo → Decode another repo

This repo across BitVibe Labs

Don't trust strangers blindly. Verify against the repo.