Hello Agents Tutorial 2026: Build Intelligent Agents from Scratch

What is Hello Agents?

Hello Agents is an open-source educational framework designed to teach developers how to build intelligent agents from first principles. Created by DataWhale, this project bridges the gap between LLM theory and practical agent implementation, providing structured tutorials and code examples that guide you through building agents capable of reasoning, planning, and taking actions autonomously.

The core problem it solves: most developers understand LLMs and RAG independently, but struggle to integrate them into coherent agent systems that can handle complex, multi-step tasks. Hello Agents demystifies this process.

What is Hello Agents?

Hello Agents is a comprehensive, open-source tutorial project focused on agent architecture and implementation. It combines theoretical foundations with practical code examples, making it ideal for developers who want to understand how modern AI agents work before building production systems.

The project emphasizes learning through doing—each module includes explanations of core concepts followed by executable Python code. Rather than abstracting away complexity, Hello Agents shows you exactly how agents think, plan, and execute tasks.

Key Features

• Progressive Learning Path: Structured curriculum from agent basics to advanced patterns like tool use and memory management
• RAG Integration: Real examples of combining retrieval-augmented generation with agent decision-making
• Production Patterns: Code examples demonstrate industry practices for error handling, logging, and agent monitoring
• LLM Agnostic: Examples work with multiple LLM providers, not locked to a single API
• Active Maintenance: Regular updates and community contributions keep examples aligned with current best practices
• Chinese & English Support: Comprehensive documentation in both languages

Getting Started

Setting up Hello Agents takes just a few minutes. You'll need Python 3.8 or later and pip.

Installation

1. Clone the repository:

   git clone https://github.com/datawhalechina/hello-agents.git
   cd hello-agents

2. Create a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

Your First Agent

After installation, explore the tutorials directory. Most examples follow a similar structure: initialize an LLM client, define agent tools, create an agent instance, and execute tasks. Here's a minimal agent pattern:

from agent import Agent
from tools import Calculator, WebSearch

# Initialize agent with tools
tools = [Calculator(), WebSearch()]
agent = Agent(model="gpt-4", tools=tools)

# Execute a task
result = agent.run("What is 25 * 4? Then search for interesting facts about the number 100.")
print(result)

This snippet demonstrates the fundamental agent loop: the LLM receives a task, decides which tools to use, executes them, and iterates until complete.

Exploring the Modules

Start with the basics folder to understand agent fundamentals. Then progress to:

• RAG Agents: Learn how to augment agents with document retrieval
• Multi-step Reasoning: Build agents that decompose complex problems
• Tool Design: Create robust tool definitions and error handling
• Memory Patterns: Implement agent memory for context continuity

When to Use Hello Agents

Use Case 1: Learning Agent Architecture

If you're new to agents or transitioning from traditional ML, Hello Agents provides a structured curriculum. Rather than piecing together information from multiple sources, you get a cohesive learning path with working code at each step.

Use Case 2: Prototyping Custom Agents

Founders and teams building agent-based products can use Hello Agents as a foundation. The patterns and code examples accelerate initial development. You'll understand design decisions rather than blindly copying framework abstractions.

Use Case 3: Teaching and Training

Instructors and team leads can adapt Hello Agents' materials for internal training. The progressive structure works well for onboarding teams to agent concepts before implementing company-specific systems.

Best for: Python developers, AI teams building internal agents, educators, and anyone who prefers learning fundamentals before using high-level frameworks.

Next Steps

After completing the tutorials, you'll be ready to either:

• Build custom agents for specific domains (customer support, data analysis, research)
• Evaluate and select higher-level agent frameworks with informed perspectives
• Contribute to the Hello Agents project by sharing improvements or additional examples

Final Thoughts

Hello Agents fills an important gap in AI education. Most tutorials either oversimplify agents or dive straight into complex framework APIs. This project strikes a balance—rigorous enough to teach real principles, approachable enough for beginners. Whether you're building your first agent or architecting a multi-agent system, the foundational understanding from Hello Agents will serve you well.