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nirdiamant/genai_agents

Analysis updated 2026-05-18

★ 21,801Jupyter NotebookAudience · developerComplexity · 3/5Setup · easy

TLDR

A collection of 50+ Jupyter Notebook tutorials teaching how to build AI agents, systems where AI models plan, use tools, and complete multi-step tasks, from beginner to advanced patterns.

Mindmap

mindmap
  root((repo))
    What it does
      AI agent tutorials
      Multi-step task solving
      Tool integration
    Agent patterns
      Simple conversational bots
      Multi-agent systems
      Memory and planning
    Use cases
      HR assistants
      Tour guides
      ML assistants
    Tech stack
      Python
      LangChain
      LangGraph
    Learning path
      Beginner basics
      Advanced patterns
      Working examples
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Code map

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What do people build with it?

USE CASE 1

Learn how to build AI agents that plan and execute multi-step tasks using tools like web search.

USE CASE 2

Build a specialized AI assistant (HR, tour guide, ML expert) that remembers context and corrects its own errors.

USE CASE 3

Study multi-agent systems where different AI models collaborate on complex problems.

USE CASE 4

Reference working code examples when implementing agent patterns in your own projects.

What is it built with?

PythonJupyter NotebookLangChainLangGraph

How does it compare?

nirdiamant/genai_agentskarpathy/nn-zero-to-herozergtant/pytorch-handbook
Stars21,80121,73021,628
LanguageJupyter NotebookJupyter NotebookJupyter Notebook
Setup difficultyeasyeasymoderate
Complexity3/52/52/5
Audiencedeveloperdeveloperresearcher

Figures from each repo's GitHub metadata at analysis time.

How do you get 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

GenAI_Agents is a tutorial collection by Nir Diamant that teaches people how to build AI agents. An AI agent here means a small program that uses a large language model, such as one of the OpenAI or Anthropic models, to plan steps, call tools, and respond to a user. The repository contains more than fifty worked examples as Jupyter notebooks, ordered from simple conversational bots up to systems where several agents talk to each other to finish a task. The content is aimed at a wide range of readers. Total beginners can start with a single-agent notebook and a short blog post called Your First AI Agent: Simpler Than You Think, which the author links to as a step-by-step walkthrough. More experienced builders can move on to agents that handle hiring tasks, museum-style tour guides, contextual quoting, machine learning helpers, and a Gutenberg literature assistant. Recently added tutorials include an HR AI Assistant, an art tour guide using LightRAG, a contextual quoting system, and an ML and data science assistant. The code uses popular agent frameworks such as LangChain and LangGraph, which are tools that help wire a language model up to memory, search, and external services. The author also points to two sibling repositories: RAG_Techniques, which covers methods for letting a model look things up in your own documents, and Prompt_Engineering, which collects ways to write better instructions for a model. There is also a related project called Agents Towards Production with patterns for moving an agent from a notebook into a real deployed service. Beyond the code, the project runs as a small community. There is a Discord server, a subreddit called EducationalAI, and a newsletter on Substack with more than fifty thousand subscribers. The README invites people to send pull requests with their own agent designs, and it points contributors to a CONTRIBUTING file for the rules. The repository is offered as a shared reference where readers can both learn from existing examples and add new ones of their own.

Copy-paste prompts

Prompt 1
Show me how to build a simple AI agent using LangChain that can search the web and summarize results.
Prompt 2
I want to create a multi-agent system where one AI handles planning and another handles execution. Which notebook should I start with?
Prompt 3
How do I add memory to an AI agent so it remembers previous conversations and learns from past mistakes?
Prompt 4
Build me an HR assistant agent that can answer employee questions, look up policies, and escalate complex issues.
Prompt 5
Explain the difference between a simple chatbot and a multi-step planning agent using code examples from this repo.

Frequently asked questions

What is genai_agents?

A collection of 50+ Jupyter Notebook tutorials teaching how to build AI agents, systems where AI models plan, use tools, and complete multi-step tasks, from beginner to advanced patterns.

What language is genai_agents written in?

Mainly Jupyter Notebook. The stack also includes Python, Jupyter Notebook, LangChain.

What license does genai_agents use?

License could not be detected automatically. Check the repository's LICENSE file before use.

How hard is genai_agents to set up?

Setup difficulty is rated easy, with roughly 5min to a first successful run.

Who is genai_agents for?

Mainly developer.

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