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nirdiamant/genai_agents

📈 Trending★ 21,801Jupyter NotebookAudience · developerComplexity · 3/5ActiveSetup · 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

Things people build with this

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.

Tech stack

PythonJupyter NotebookLangChainLangGraph

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

GenAI_Agents is an educational collection of tutorials and runnable examples for building generative-AI agents, programs that use a large language model as their reasoning core to plan, call tools, and accomplish tasks. The repository advertises more than fifty tutorials and ranges from very simple conversational bots all the way up to multi-agent systems in which several specialised agents collaborate on a problem. Each tutorial is delivered as a Jupyter notebook so that you can read the explanation, run the code one cell at a time, and tweak it as you go. Notebooks are organised by category and functionality, and a large table in the README lists each agent with its framework and key features. Newly added entries include an HR AI Assistant, an Art Tourguide built with LightRAG, a Contextual Quoting System, an ML/DS Assistant, and an agent called Gutenberg Sage. The README's stated goal is to support the whole learning curve: beginners taking their first steps with AI agents can start from the simplest notebooks and read a companion blog post called Your First AI Agent: Simpler Than You Think, while more advanced practitioners can study novel agent architectures and contribute their own. The project is community-driven, with a Discord server and a subreddit linked from the README, and a CONTRIBUTING file for people who want to add tutorials. You would use this repository as a study resource, to understand how agents are structured, to copy a starting point for your own project, or to compare different agent frameworks side by side. The topics listed for the repo mention LangChain and LangGraph, indicating those frameworks appear among the tutorials. The full README is longer than what was provided.

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.
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