# Hello Agents — Build Intelligent Agents from Zero to Production

> A comprehensive, community-driven tutorial that walks you through building AI agents from first principles. Covers agent architecture, tool use, memory, RAG, and multi-agent orchestration with hands-on code examples.

## Install

Save as a script file and run:

# Hello Agents — Build Intelligent Agents from Zero to Production

## Quick Use
```bash
git clone https://github.com/datawhalechina/hello-agents.git
cd hello-agents
pip install -r requirements.txt
# Follow the chapter-by-chapter notebooks
```

## Introduction
Hello Agents is an open-source educational project by Datawhale that teaches you how to build AI agents from scratch. Rather than relying on high-level frameworks, it walks through the core primitives—tool calling, memory management, planning, and multi-agent coordination—so you understand what is actually happening under the hood.

## What Hello Agents Does
- Provides a structured curriculum covering agent fundamentals, tool integration, memory systems, and RAG pipelines
- Includes runnable Jupyter notebooks for every chapter so you can learn by doing
- Demonstrates multi-agent patterns including delegation, debate, and supervisor architectures
- Covers both Python SDK usage and the underlying prompt engineering that drives agent behavior
- Teaches how to evaluate agent performance and handle failure modes gracefully

## Architecture Overview
The project is organized as a series of progressive chapters, each building on the last. Early chapters cover the agent loop (observe → think → act → reflect), then introduce tool definitions and function calling, followed by retrieval-augmented generation and vector stores. Later chapters tackle multi-agent systems and production deployment patterns. All examples use standard Python libraries and can run with any major LLM provider.

## Self-Hosting & Configuration
- Clone the repo and install dependencies with pip; no special infrastructure required
- Each chapter is a standalone notebook—run them in Jupyter, VS Code, or Colab
- Configure your LLM API key via environment variables (supports OpenAI, Anthropic, and local models)
- Chapters on RAG require a vector database; examples default to Chroma or FAISS
- The project supports both cloud-hosted and fully local model setups via Ollama

## Key Features
- Zero-to-production curriculum designed for developers new to agent engineering
- Framework-agnostic approach that teaches principles rather than locking you into one SDK
- Bilingual documentation in Chinese and English
- Active community with regular updates as agent patterns evolve
- Covers advanced topics like self-evolving agents and human-in-the-loop workflows

## Comparison with Similar Tools
- **LangChain docs** — framework-specific tutorials; Hello Agents is framework-agnostic and teaches fundamentals
- **DeepLearning.AI courses** — video-first and paywalled; Hello Agents is text-first and fully open source
- **AutoGen tutorials** — focused on Microsoft's multi-agent framework; Hello Agents covers multiple approaches
- **OpenAI Cookbook** — recipe-oriented for OpenAI APIs; Hello Agents builds understanding from first principles
- **12-Factor Agents** — focuses on production principles; Hello Agents provides the step-by-step learning path to get there

## FAQ
**Q: Do I need ML experience to use this?**
A: Basic Python proficiency is sufficient. The curriculum starts from fundamentals and builds up progressively.

**Q: Which LLM providers are supported?**
A: The examples work with OpenAI, Anthropic, local models via Ollama, and most OpenAI-compatible APIs.

**Q: Is this a framework I install?**
A: No. It is a learning resource with runnable code. You take the patterns you learn and apply them in your own projects.

**Q: How often is the content updated?**
A: The project follows an active contribution model with regular chapter additions as the agent ecosystem evolves.

## Sources
- https://github.com/datawhalechina/hello-agents
- https://datawhalechina.github.io/hello-agents/

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Source: https://tokrepo.com/en/workflows/asset-763bf848
Author: Script Depot