Awesome Agentic AI Zh Learning

awesome-agentic-ai-zh Learning Skill

Skill by ara.so — AI Agent Skills collection.

Overview

awesome-agentic-ai-zh is a comprehensive, structured learning roadmap for AI Agent development that takes you from LLM basics to building multi-agent systems. It provides:

• Two learning tracks: Track A (CLI Power User) and Track B (Agent Builder)
• 8 core stages with 145+ curated projects and resources
• 27 hands-on exercises with working code examples
• Bilingual content (Traditional Chinese, Simplified Chinese, English)
• 5 specialized branches for researchers, developers, teachers, knowledge workers, and everyday users

The project is particularly valuable for understanding the Claude Code ecosystem (MCP, Skills, Plugins, Subagents) and modern agent interfaces (Computer Use, Browser Use, Code Sandbox).

Installation & Setup

# Clone the repository
git clone https://github.com/WenyuChiou/awesome-agentic-ai-zh.git
cd awesome-agentic-ai-zh

# No additional dependencies for reading the roadmap
# Individual exercises may require Python and specific libraries

For complete setup (first-time learners):

# Read the setup guide first
cat resources/setup-guide.md

# Install Python 3.8+ if needed
python --version

# Install common dependencies for exercises
pip install anthropic openai langchain chromadb

Learning Path Structure

Shared Foundation (Stage 0-2)

Stage 0: Foundations (stages/00-foundations.md)

• Python, CLI, git, API basics, JSON
• Duration: 1-2 weeks

Stage 1: LLM Basics (stages/01-llm-basics.md)

• Token concepts, API usage, LLM comparison, local LLM (Ollama)
• Duration: 1 week

Stage 2: Prompt Engineering (stages/02-prompt-engineering.md)

• System prompts, few-shot learning, Chain-of-Thought
• Duration: 1-2 weeks

Track A: CLI Power User

# Navigate Track A
cat tracks/cli/A1-cli-intro.md           # CLI agent comparison & setup
cat tracks/cli/A2-cli-workflow.md        # Workflow patterns
cat tracks/cli/A3-cli-production.md      # Production integration

# Key resource
cat resources/cli-agents-guide.md

Total duration: 8-10 weeks (including shared foundation)

Track B: Agent Builder

# Navigate Track B
cat stages/03-tool-use-and-hello-agent.md    # Function calling, ReAct
cat stages/04-agent-frameworks.md             # LangGraph, AutoGen, CrewAI
cat stages/05-claude-code-ecosystem.md        # MCP, Skills, Plugins (SHARED HUB)
cat stages/06-memory-rag.md                   # Context engineering, RAG
cat stages/07-multi-agent-production.md       # Multi-agent orchestration
cat stages/07.5-advanced-agentic-concepts.md  # Advanced concepts (reading)
cat stages/08-agent-interfaces.md             # Computer Use, Browser Use (SHARED HUB)

Total duration: 16-22 weeks minimum, 5-7 months realistically (5-8 hrs/week)

Key Commands & Navigation

Finding Resources

# List all stage files
ls stages/

# View glossary of terms
cat resources/glossary.md

# Check CLI agents comparison
cat resources/cli-agents-guide.md

# Browse exercises
ls exercises/stage-*/

Running Exercises

Each stage has 1-5 exercises in exercises/stage-X/:

# Example: Stage 1 - First LLM API call
# exercises/stage-1/01-first-llm-call/main.py

import os
from anthropic import Anthropic

def main():
    client = Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))
    
    message = client.messages.create(
        model="claude-3-5-sonnet-20241022",
        max_tokens=1024,
        messages=[
            {"role": "user", "content": "Explain AI agents in one sentence."}
        ]
    )
    
    print(message.content[0].text)

if __name__ == "__main__":
    main()

# Set up environment
export ANTHROPIC_API_KEY="your-key-here"

# Run exercise
python exercises/stage-1/01-first-llm-call/main.py

Dual-Path SDK Examples

Most exercises provide both Anthropic SDK and Ollama (local) implementations:

# Using Anthropic Claude
from anthropic import Anthropic

client = Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))
response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello"}]
)

# Using Ollama (local)
import ollama

response = ollama.chat(
    model="llama3.2",
    messages=[{"role": "user", "content": "Hello"}]
)

Configuration Patterns

API Key Management

# Set environment variables (recommended)
export ANTHROPIC_API_KEY="sk-ant-..."
export OPENAI_API_KEY="sk-..."

# Or use .env file
cat > .env << EOF
ANTHROPIC_API_KEY=sk-ant-...
OPENAI_API_KEY=sk-...
EOF

# Load in Python
from dotenv import load_dotenv
load_dotenv()

Local LLM Setup (Ollama)

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull a model
ollama pull llama3.2

# Test
ollama run llama3.2 "Explain what an AI agent is"

Common Usage Patterns

Pattern 1: Following the Learning Path

# For complete beginners
cat stages/00-foundations.md
# → Complete exercises in exercises/stage-0/

# Then proceed sequentially
cat stages/01-llm-basics.md
cat stages/02-prompt-engineering.md

# Choose your track
cat tracks/cli/A1-cli-intro.md    # OR
cat stages/03-tool-use-and-hello-agent.md

Pattern 2: Quick Reference for Specific Topics

# Need MCP information?
cat stages/05-claude-code-ecosystem.md

# Need multi-agent patterns?
cat stages/07-multi-agent-production.md

# Need Computer Use examples?
cat stages/08-agent-interfaces.md

Pattern 3: Building Your First Agent

Follow the comprehensive walkthrough:

cat walkthroughs/build-first-agent-in-7-steps.md

Example from the walkthrough (Stage 3: Tool Use):

# exercises/stage-3/02-function-calling/main.py
import os
import json
from anthropic import Anthropic

def get_weather(city: str) -> dict:
    """Mock weather API - returns fake data"""
    return {
        "city": city,
        "temperature": 22,
        "condition": "sunny"
    }

def main():
    client = Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))
    
    tools = [{
        "name": "get_weather",
        "description": "Get current weather for a city",
        "input_schema": {
            "type": "object",
            "properties": {
                "city": {"type": "string", "description": "City name"}
            },
            "required": ["city"]
        }
    }]
    
    response = client.messages.create(
        model="claude-3-5-sonnet-20241022",
        max_tokens=1024,
        tools=tools,
        messages=[
            {"role": "user", "content": "What's the weather in Tokyo?"}
        ]
    )
    
    # Handle tool use
    if response.stop_reason == "tool_use":
        tool_use = next(block for block in response.content if block.type == "tool_use")
        
        if tool_use.name == "get_weather":
            result = get_weather(**tool_use.input)
            
            # Send result back
            response = client.messages.create(
                model="claude-3-5-sonnet-20241022",
                max_tokens=1024,
                tools=tools,
                messages=[
                    {"role": "user", "content": "What's the weather in Tokyo?"},
                    {"role": "assistant", "content": response.content},
                    {
                        "role": "user",
                        "content": [{
                            "type": "tool_result",
                            "tool_use_id": tool_use.id,
                            "content": json.dumps(result)
                        }]
                    }
                ]
            )
    
    print(response.content[0].text)

if __name__ == "__main__":
    main()

Pattern 4: ReAct Agent Implementation

# exercises/stage-3/04-react-agent/main.py
import os
from anthropic import Anthropic

def search_papers(query: str) -> list:
    """Mock paper search"""
    return [
        {"title": "Attention Is All You Need", "year": 2017},
        {"title": "BERT: Pre-training of Deep Bidirectional Transformers", "year": 2018}
    ]

def summarize_paper(title: str) -> str:
    """Mock paper summarizer"""
    return f"Summary of '{title}': A foundational paper in NLP..."

def react_loop(user_query: str, max_iterations: int = 5):
    client = Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))
    
    tools = [
        {
            "name": "search_papers",
            "description": "Search academic papers by query",
            "input_schema": {
                "type": "object",
                "properties": {
                    "query": {"type": "string"}
                },
                "required": ["query"]
            }
        },
        {
            "name": "summarize_paper",
            "description": "Get summary of a paper by title",
            "input_schema": {
                "type": "object",
                "properties": {
                    "title": {"type": "string"}
                },
                "required": ["title"]
            }
        }
    ]
    
    messages = [{"role": "user", "content": user_query}]
    
    for i in range(max_iterations):
        response = client.messages.create(
            model="claude-3-5-sonnet-20241022",
            max_tokens=2048,
            tools=tools,
            messages=messages
        )
        
        messages.append({"role": "assistant", "content": response.content})
        
        if response.stop_reason == "end_turn":
            return response.content[0].text
        
        # Execute tools
        tool_results = []
        for block in response.content:
            if block.type == "tool_use":
                if block.name == "search_papers":
                    result = search_papers(**block.input)
                elif block.name == "summarize_paper":
                    result = summarize_paper(**block.input)
                
                tool_results.append({
                    "type": "tool_result",
                    "tool_use_id": block.id,
                    "content": str(result)
                })
        
        messages.append({"role": "user", "content": tool_results})
    
    return "Max iterations reached"

# Usage
result = react_loop("Find papers about transformers and summarize the most important one")
print(result)

MCP (Model Context Protocol) Integration

Stage 5 covers the Claude Code ecosystem. Key MCP concepts:

# Example MCP server structure
# See stages/05-claude-code-ecosystem.md for details

from mcp.server import Server
from mcp.types import Tool, TextContent

app = Server("my-mcp-server")

@app.tool()
async def get_document(doc_id: str) -> str:
    """Fetch document by ID"""
    # Your implementation
    return f"Document content for {doc_id}"

@app.tool()
async def search_database(query: str) -> list:
    """Search internal database"""
    # Your implementation
    return [{"id": "1", "title": "Result"}]

Using MCP with Claude Desktop

// ~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "my-server": {
      "command": "python",
      "args": ["/path/to/your/mcp_server.py"]
    }
  }
}

Specialized Branches

For Researchers

cat branches/for-researcher.md

Focus: Literature review automation, paper writing assistance, multi-agent review systems

For Developers

cat branches/for-developer.md

Focus: Cursor/Aider integration, CLI delegation, code review agents

For Everyday Users

cat branches/for-everyday-users.md

Focus: Using ChatGPT/Claude.ai effectively, privacy scenarios, CLI agent introduction (no coding required)

Troubleshooting

Common Issues

Issue: "API key not found"

# Verify environment variable
echo $ANTHROPIC_API_KEY

# Set it if missing
export ANTHROPIC_API_KEY="sk-ant-..."

Issue: "Module not found"

# Install required package
pip install anthropic

# For exercises requiring multiple packages
pip install -r requirements.txt

Issue: "Ollama connection refused"

# Check if Ollama is running
curl http://localhost:11434/api/tags

# Start Ollama if needed
ollama serve

Issue: "Which stage should I start from?"

• Have Python/git basics? → Start Stage 1
• Complete beginner? → Start Stage 0
• Want to use CLI agents without coding? → Go directly to Track A (A1-cli-intro.md)

Stage-Specific Help

# Each stage has a glossary
cat resources/glossary.md

# Setup troubleshooting
cat resources/setup-guide.md

# CLI-specific issues
cat resources/cli-agents-guide.md

Best Practices

1. Follow the path sequentially — Each stage builds on previous knowledge
2. Complete the exercises — 27 hands-on exercises are designed for learning by doing
3. Use dual-path approach — Try both cloud APIs (Anthropic/OpenAI) and local models (Ollama)
4. Check the glossary — resources/glossary.md has all terminology in Chinese + English
5. Join the community — The project welcomes contributions and questions
6. Set realistic expectations — Track B takes 5-7 months part-time; Track A takes 8-10 weeks

Example: Complete Agent Build

See walkthroughs/build-first-agent-in-7-steps.md for a 350-line Paper Summary Bot that evolves from Stage 1 to Stage 7, demonstrating:

• LLM API basics (Stage 1)
• Prompt engineering (Stage 2)
• Tool use and ReAct (Stage 3)
• Framework integration with LangGraph (Stage 4)
• Memory and RAG (Stage 6)
• Multi-agent orchestration (Stage 7)

Additional Resources

# Full glossary
cat resources/glossary.md

# Diagrams
ls resources/diagrams/
# - learning-map.png
# - branch-decision-tree.png
# - banner.png

# Complete setup guide
cat resources/setup-guide.md

Project Metadata

• License: MIT
• Languages: Traditional Chinese (primary), Simplified Chinese, English
• Scope: 145+ curated projects, 27 exercises, 8 stages, 2 tracks, 5 branches
• Repository: https://github.com/WenyuChiou/awesome-agentic-ai-zh
• Stars: 1462+ (as of 2026-05-16)