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![Stages 0–2 split into CLI and Agent paths, sharing Stages 5 and 8; choose role paths as needed](resources/diagrams/banner.en.svg) # awesome-agentic-ai-zh **🤖 One map from “what is an AI agent?” to “I can build a reliable system.”** **Pick a route, then walk it one step at a time. Concepts, exercises, and resources are already in order.** [![License](https://img.shields.io/badge/license-MIT-blue?style=flat)](LICENSE) [![zh-TW](https://img.shields.io/badge/語言-繁體中文-red?style=flat)](README.md) [![zh-Hans](https://img.shields.io/badge/語言-简体中文-orange?style=flat)](README.zh-Hans.md) [![EN](https://img.shields.io/badge/lang-English-blue?style=flat)](README.en.md) ![GitHub stars](https://img.shields.io/github/stars/WenyuChiou/awesome-agentic-ai-zh?style=flat&logo=github) [![Docs site](https://img.shields.io/badge/線上閱讀-立即開始-2ea44f?style=flat)](https://wenyuchiou.github.io/awesome-agentic-ai-zh/) [![Star History global rank](https://api.star-history.com/badge?repo=WenyuChiou/awesome-agentic-ai-zh&type=rank)](https://www.star-history.com/wenyuchiou/awesome-agentic-ai-zh)
> 📱 On a phone, read the [docs site](https://wenyuchiou.github.io/awesome-agentic-ai-zh/). ## 🎯 What does this map help you do? An **AI Agent** is an AI system that can decide what to do next and take action toward a person's goal. It reads the situation, chooses the next step, and uses tools when needed; based on the result, it continues, corrects course, stops, or hands control back. It can do work automatically, but only within a person's rules and permissions. A one-shot chatbot or fixed script is not necessarily an Agent. The map covers three things in order: 1. **Get the basics**: what an LLM (Large Language Model, reads and writes language), a prompt, an API (Application Programming Interface, programs request a service), and a token are. 2. **Then build something**: let a model call tools, run an agent loop, read documents, remember things. 3. **Then make it reliable**: add permissions, Eval, human approval, observability, and failure recovery. This repo is **a learning roadmap + curated resources + small runnable examples**. For chapter-length depth we point to the official docs, [Datawhale Hello-Agents](https://github.com/datawhalechina/hello-agents), or a cookbook rather than rewriting an encyclopedia. When a model connection is needed, each exercise explains the cloud or local path. Chapters explain important terms in plain language at first use. Forgot one? See the [glossary](resources/glossary.en.md). ## 🚀 Start now 1. **Never written code**: start at [Stage 0: Foundations](stages/00-foundations.en.md). If APIs or CLI Agents are new, use the [zero-to-setup guide](resources/setup-guide.en.md) beside it. 2. **Already know Python, Git, and APIs**: start at [Stage 1: LLM fundamentals](stages/01-llm-basics.en.md). 3. **Not sure which route fits**: read the Track A / Track B table below. Before Track A or Track B, check Stage 0–2. If you only want everyday AI use, go straight to the role guide.
What do you want to do?RouteRoute entrance
Get work done with a CLI agent — Claude Code, Codex, OpenCodeTrack A — CLI Power UserA1: Pick a CLI agent
Write your own agent, tool loop, workflow, and serviceTrack B — Agent BuilderStage 3: First agent loop
Use AI safely in daily life, no coding for nowEveryday user routeEveryday user guide
💻 Expand: clone it to your machine ```powershell git clone https://github.com/WenyuChiou/awesome-agentic-ai-zh.git cd awesome-agentic-ai-zh ``` Then open `stages/00-foundations.en.md`, or jump to your first stop above.
## Stage 0 through Stage 8, plus the Stage 7.5 reading stop ![AI Agent learning map](resources/diagrams/learning-map.en.png) The map has **8 topic stages + the Stage 0 readiness check + the Stage 7.5 advanced reading stop**: **10 learning stops** in total. Track A / B readers first check the **shared Stage 0–2 foundations**; skip Stage 0 if you already know Python, Git, and APIs. Everyday users can go straight to the role guide. ### Shared foundations: Stages 0–2
StageWhat it settlesWhat you can do after
0 · FoundationsMachine and tools ready?Call a public API in Python, read JSON (JavaScript Object Notation, a text data-exchange format), save with Git
1 · LLM fundamentalsWhat are LLM, token, context; how do models differ?Call an LLM and pick a cloud or local model
2 · Prompt designHow to state goal, data, rules, output clearly?Compare Zero-Shot, One-Shot, Few-Shot, and the boundary of CoT (Chain-of-Thought, reason through intermediate steps)
### Track A: get work done with a CLI agent Route: `A1 → A2 → Stage 5 → A3 → Stage 8`. Stage 8 is recommended, not required for Track A Capstone.
OrderWhat it settlesWhat you can do after
A1 · Pick a CLI agentWhat are OpenRouter, OpenCode, Pi, Ollama?Pick a tool and finish one small task
A2 · Build a repeatable processHow to keep rules and steps for next time?Write project instructions, a Skill, a reusable workflow
5 · Claude Code ecosystemHow do MCP, Skills, Plugins, Hooks, Subagents differ?Read core 5.1–5.4; choose 5.5–5.8 only when your work needs them
A3 · Plug into real workHow to safely connect tools, CI, and team process?Integrate with least privilege, human checks, a record
8 · Agent interfacesHow does an agent drive a browser, screen, sandbox?Decide if a task needs CLI, browser, Computer Use, or API
### Track B: build an agent from scratch
OrderWhat it settlesWhat you can do after
3 · Tool Use & Your First Agent LoopHow does a model call tools safely and continue?Build an agent loop with a turn limit and validated arguments
4 · Workflow Graphs & Agent FrameworksHow to draw several steps as one map?Choose between workflow, agent, graph, framework
5 · Claude Code ecosystemHow do MCP, Skills, Plugins, Hooks, Subagents work together?Combine tools, rules, reusable capabilities
6 · Memory · RAG (Retrieval-Augmented Generation, retrieve information before answering)How does an agent search, save, and get back what matters?Build a minimal RAG, long-term memory, and contextual retrieval flow
7 · Agent Production Engineering: Testable, Observable, Stoppable, and RecoverableHow does an agent stay stable in production?Add Eval, observability, budget, Human-in-the-loop approval, recovery
7.5 · Advanced agentic concept mapWhich advanced patterns are worth knowing?Pick what you need from 12 concepts such as PAR loop and agent-as-judge
8 · Agent interfacesHow does an agent work beyond the API?Choose Computer Use, Browser Use, or a code sandbox
Stage 4 first explains the **Workflow Graph**, then uses a framework to build it. Stage 7 adds Eval, observability, approval, and recovery for reliability. > 🔭 **Learning order**: Stage 2 Prompt → Stage 3 **Agent Loop** → Stage 4 **Workflow Graph** / framework → Stage 5 tools and rules → Stage 6 **Context Engineering** → Stage 7 production. Prompt, Context, Harness, Loop, and Graph work together; they are not five layers or product generations that replace one another. Start the [Capstone](CAPSTONE.en.md) after A3 (Track A) or Stage 8, including 7.5 (Track B). [Track progress](PROGRESS.en.md).
⏱️ View time estimates (planning aid, not a deadline) - **Track A**: about 8–10 weeks, using an existing CLI agent to get work done. - **Track B**: main path about 16–22 weeks; at 5–8 hours a week, usually 5–7 months. - **Stage 5** is the tools-and-rules hub: Track A uses them, Track B combines them. - **Stage 8** is the interface hub: Track A delegates, Track B builds them in. The timeline is a planning aid. Finish the step in front of you; no need to read the whole map at once.
### Keep going by who you are ![Research, development, teaching, knowledge work, and everyday use are five options; choose what you need, not every path](resources/diagrams/branch-decision-tree.en.svg)
RouteWho it fitsWhat you will handle
🔬 ResearcherGrad students, postdocs, PIsLiterature evidence, reproducible pipelines, multi-agent review
💻 DeveloperSoftware engineersCLI delegation, code review, tests and rollback
🎓 TeacherTeachers, instructorsLesson prep, feedback, privacy, teaching prompts
📊 Knowledge workerConsultants, PMs, analystsEmail, meeting, reporting workflows
👥 Everyday userAI users who may not codeWriting, learning, privacy, safe use
## 💡 How do you learn without getting stuck? 1. **One Stage at a time**: answer that chapter's core question first. 2. **Read the core terms and required reading first**: the exercises use them directly. 3. **Copy the first command as-is**: run the offline test first, don't retype a blank file. 4. **Change one thing at a time**: rerun the test right after, so you know what caused the result. 5. **Meet the completion check before moving on**: understanding is not the same as doing. ## 📚 Learning entrances to bookmark Only the most-used entrances are here; the full list is in [RESOURCES.en.md](RESOURCES.en.md). Stars mean **learning priority**, not a ranking.
PurposeEntranceWhen to use itPriority
StartZero-to-setup guideFirst install and first run⭐⭐⭐⭐⭐
How to use this materialBefore your first hands-on exercise⭐⭐⭐⭐⭐
Progress trackerWant the next step, or to log what you finished⭐⭐⭐⭐
LearnCore glossaryYou hit an unfamiliar word: token, RAG, MCP⭐⭐⭐⭐⭐
Runnable examplesWant to run offline tests and small cases⭐⭐⭐⭐⭐
Hands-on cookbookBuilding a Skill, MCP, Office, Zotero, or local LLM⭐⭐⭐⭐
Look it upResource toolboxNot sure if you need a guide, catalog, or cookbook⭐⭐⭐⭐⭐
Full resource listOfficial docs, courses, communities, further reading⭐⭐⭐⭐
CLI agent selection guideStarting Track A, or comparing CLI tools⭐⭐⭐⭐
Course and certification mapSeparates completion certificates, skill badges, and certification exams⭐⭐⭐⭐
## 🤝 Help improve this map - Wrong content, broken links, or stale information: open an [Issue](https://github.com/WenyuChiou/awesome-agentic-ai-zh/issues). - Adding a project or learning resource: say which Stage it teaches, and what. - Sending a PR: read [CONTRIBUTING.en.md](CONTRIBUTING.en.md) and the [style guide](resources/style-guide.en.md) first. - Recent changes: see [CHANGELOG.md](CHANGELOG.md).
🧰 Expand: all the ways to contribute, and the automated checks You can fix wording, fill in a missing trilingual mirror, report a missing topic, or maintain a Stage or role route long term. For a new GitHub project link, the automated check shows archive status, license, and last update; inclusion stays a maintainer call based on learning value. Full roles and rules are in [CONTRIBUTORS.md](CONTRIBUTORS.md).
## 🙏 Key inspirations and related projects - [**Datawhale Hello-Agents**](https://github.com/datawhalechina/hello-agents) — for readers who want full chapters and deep hands-on work. - [**Datawhale community**](https://github.com/datawhalechina) — a Chinese-language machine learning study community with many reliable entrances. - [**liyupi/ai-guide**](https://github.com/liyupi/ai-guide) — a breadth-first resource hub; this repo handles the learning order instead.
📖 Expand: contributors and citation format [![Contributors](https://contrib.rocks/image?repo=WenyuChiou/awesome-agentic-ai-zh)](https://github.com/WenyuChiou/awesome-agentic-ai-zh/graphs/contributors) ```bibtex @misc{awesome_agentic_ai_zh_2026, title = {awesome-agentic-ai-zh: A Structured Learning Roadmap for Agentic AI}, author = {Chiou, Wenyu}, year = {2026}, url = {https://github.com/WenyuChiou/awesome-agentic-ai-zh} } ```
## ☕ Support and contact This learning map is MIT-licensed and stays free and public. Use an Issue for questions; for private contact, email [wenyuchiou12@gmail.com](mailto:wenyuchiou12@gmail.com). If this map helped you, a ⭐ Star is welcome, or [buy the author a coffee](https://www.buymeacoffee.com/wenyuchiou). [![Greptile: The War on Bugs](https://www.greptile.com/badge.svg)](https://www.greptile.com/?utm_source=oss_badge&utm_medium=readme&utm_campaign=greptile_for_open_source) ## ⭐ Star History [![GitHub star history for this project](https://api.star-history.com/svg?repos=WenyuChiou/awesome-agentic-ai-zh&type=Date)](https://www.star-history.com/#WenyuChiou/awesome-agentic-ai-zh&Date) ## License MIT. Maintained by [@WenyuChiou](https://github.com/WenyuChiou).