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datawhalechina/hello-agents

🔥 Hot50,850PythonAudience · developerComplexity · 3/5ActiveSetup · easy

TLDR

A Chinese-language tutorial for building AI agents from scratch, covering LLMs, prompt engineering, tool use, and multi-agent systems with Python code examples.

Mindmap

mindmap
  root((hello-agents))
    What it teaches
      LLM fundamentals
      Prompt engineering
      Agent architectures
      Multi-agent systems
    Key concepts
      Function calling
      Tool use
      Retrieval-augmented generation
      Model Context Protocol
    Learning path
      Basics first
      Progressive difficulty
      Hands-on examples
      Real-world patterns
    Tech stack
      Python
      Large language models
    Audience
      Chinese speakers
      Beginners
      Students

Things people build with this

USE CASE 1

Learn how to build an AI agent that can plan tasks, call external tools, and iterate toward goals.

USE CASE 2

Build a multi-agent system where specialized agents collaborate to solve complex problems.

USE CASE 3

Implement retrieval-augmented generation so your agent can query external knowledge bases.

USE CASE 4

Understand prompt engineering and function calling techniques for working with large language models.

Tech stack

PythonLarge Language Models

Getting it running

Difficulty · easy Time to first run · 5min
License could not be detected automatically. Check the repository's LICENSE file before use.

In plain English

hello-agents is a Chinese-language tutorial repository for learning how to build AI agents from the ground up. It is produced by Datawhale, a Chinese open-source education organization, and is structured as a progressive course that takes a learner from basic large language model concepts all the way through building sophisticated multi-agent systems. The curriculum begins with the fundamentals of how large language models work, covering topics like prompt engineering, function calling, and tool use. It then introduces agent architectures, explaining how a model can plan actions, call external tools, observe results, and iterate toward a goal. Later modules cover retrieval-augmented generation so agents can query external knowledge bases, and the course closes with multi-agent coordination patterns where multiple specialized agents collaborate to complete complex tasks. The Model Context Protocol is covered as a way to standardize how agents interact with tools and data sources. You would use this resource if you are a Chinese-speaking developer who wants a structured, beginner-friendly introduction to building AI agents in Python. It is especially useful for students and practitioners who find English-language resources difficult to work with, or for those who want a curated learning path rather than piecing together documentation and blog posts on their own. The course is hands-on, with Python code examples throughout. The entire course is written in Chinese and the code examples use Python. It is maintained as an open-source educational project and welcomes contributions from the community.

Copy-paste prompts

Prompt 1
Show me how to build a simple AI agent in Python that can call external tools and observe results, using the patterns from hello-agents.
Prompt 2
How do I implement retrieval-augmented generation for an agent so it can query a knowledge base, based on hello-agents examples?
Prompt 3
Walk me through setting up a multi-agent system where two specialized agents collaborate, using the coordination patterns from hello-agents.
Prompt 4
Explain how the Model Context Protocol standardizes agent-tool interactions, and show me a Python example.
Prompt 5
Help me understand prompt engineering and function calling for LLMs using code examples from hello-agents.
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