GenAI_Agents README

The project's README from github.com/NirDiamant/GenAI_Agents. As one markdown file: README.md.

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GenAI Agents: Comprehensive Repository for Development and Implementation 🚀

Welcome to one of the most extensive and dynamic collections of Generative AI (GenAI) agent tutorials and implementations available today. This repository serves as a comprehensive resource for learning, building, and sharing GenAI agents, ranging from simple conversational bots to complex, multi-agent systems.

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Introduction

Generative AI agents are at the forefront of artificial intelligence, revolutionizing the way we interact with and leverage AI technologies. This repository is designed to guide you through the development journey, from basic agent implementations to advanced, cutting-edge systems.

📚 Learn to Build Your First AI Agent

Your First AI Agent: Simpler Than You Think

This detailed blog post complements the repository by providing a complete A-Z walkthrough with in-depth explanations of core concepts, step-by-step implementation, and the theory behind AI agents. It's designed to be incredibly simple to follow while covering everything you need to know to build your first working agent from scratch.

💡 Plus: Subscribe to the newsletter for exclusive early access to tutorials and special discounts on upcoming courses and books!

Our goal is to provide a valuable resource for everyone - from beginners taking their first steps in AI to seasoned practitioners pushing the boundaries of what's possible. By offering a range of examples from foundational to complex, we aim to facilitate learning, experimentation, and innovation in the rapidly evolving field of GenAI agents.

Furthermore, this repository serves as a platform for showcasing innovative agent creations. Whether you've developed a novel agent architecture or found an innovative application for existing techniques, we encourage you to share your work with the community.

🔍 RAG Techniques - 40+ notebooks on retrieval-augmented generation.

🚀 Agents Towards Production - code-first tutorials for shipping production-grade agents.

🖋️ Prompt Engineering Techniques - prompting strategies from basics to advanced.

🧠 Agent Memory Techniques - 30 notebooks on agent memory: vector stores, graphs, Mem0, Zep.

Join the community

Contributions make this better - propose ideas, share techniques, or give feedback via CONTRIBUTING.md.

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Key Features

GenAI Agent Implementations

Below is a comprehensive overview of our GenAI agent implementations, organized by category and functionality. Each implementation is designed to showcase different aspects of AI agent development, from basic conversational agents to complex multi-agent systems.

⭐ Featured tutorial Document Intake Agent LangGraph Office docs to LLM-ready markdown, conversion as a tool call, grounded answers
# Category Agent Name Framework Key Features
1 🌱 Beginner Simple Conversational Agent LangChain/PydanticAI Context-aware conversations, history management
2 🌱 Beginner Simple Question Answering LangChain Query understanding, concise answers
3 🌱 Beginner Simple Data Analysis LangChain/PydanticAI Dataset interpretation, natural language queries
4 🔧 Framework Introduction to LangGraph LangGraph Modular AI workflows, state management
5 🔧 Framework Model Context Protocol (MCP) MCP AI-external resource integration
6 🎓 Educational ATLAS: Academic Task System LangGraph Multi-agent academic planning, note-taking
7 🎓 Educational Scientific Paper Agent LangGraph Literature review automation
8 🎓 Educational Chiron - Feynman Learning LangGraph Adaptive learning, checkpoint system
9 💼 Business Customer Support Agent LangGraph Query categorization, sentiment analysis
10 💼 Business Essay Grading Agent LangGraph Automated grading, multiple criteria
11 💼 Business Travel Planning Agent LangGraph Personalized itineraries
12 💼 Business GenAI Career Assistant LangGraph Career guidance, learning paths
13 💼 Business Project Manager Assistant LangGraph Task generation, risk assessment
14 💼 Business Contract Analysis Assistant LangGraph Clause analysis, compliance checking
15 💼 Business E2E Testing Agent LangGraph Test automation, browser control
16 🎨 Creative GIF Animation Generator LangGraph Text-to-animation pipeline
17 🎨 Creative TTS Poem Generator LangGraph Text classification, speech synthesis
18 🎨 Creative Music Compositor LangGraph AI music composition
19 🎨 Creative Content Intelligence LangGraph Multi-platform content generation
20 🎨 Creative Business Meme Generator LangGraph Brand-aligned meme creation
21 🎨 Creative Murder Mystery Game LangGraph Procedural story generation
22 📊 Analysis Memory-Enhanced Conversational LangChain Short/long-term memory integration
23 📊 Analysis Multi-Agent Collaboration LangChain Historical research, data analysis
24 📊 Analysis Self-Improving Agent LangChain Learning from interactions
25 📊 Analysis Task-Oriented Agent LangChain Text summarization, translation
26 📊 Analysis Internet Search Agent LangChain Web research, summarization
27 📊 Analysis Research Team - Autogen AutoGen Multi-agent research collaboration
28 📊 Analysis Sales Call Analyzer LangGraph Audio transcription, NLP analysis
29 📊 Analysis Weather Emergency System LangGraph Real-time data processing
30 📊 Analysis Self-Healing Codebase LangGraph Error detection, automated fixes
31 📊 Analysis DataScribe LangGraph Database exploration, query planning
32 📊 Analysis Memory-Enhanced Email LangGraph Email triage, response generation
33 📰 News News TL;DR LangGraph News summarization, API integration
34 📰 News AInsight LangGraph AI/ML news aggregation
35 📰 News Journalism Assistant LangGraph Fact-checking, bias detection
36 📰 News Blog Writer OpenAI Swarm Collaborative content creation
37 📰 News Podcast Generator LangGraph Content search, audio generation
38 🛍️ Shopping ShopGenie LangGraph Product comparison, recommendations
39 🛍️ Shopping Car Buyer Agent

GenAI_Agents (NirDiamant/GenAI_Agents)

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Что это такое?Подборка из более чем 50 учебных материалов и реализаций техник агентов на генеративном ИИ — от простых разговорных ботов до сложных мультиагентных систем, оформленных в виде практических Python-ноутбуков.
То есть это библиотека, которую нужно установить, или что я в итоге получу?Ни то, ни другое — это каталог самостоятельных учебных ноутбуков для Jupyter. Каждый ноутбук реализует одну технику агента на Python: его можно прочитать, запустить и адаптировать под себя.
С чего мне начать работу с одним из них?Выберите агента из тематической таблицы в README и откройте его ноутбук в Jupyter — каждый из них представляет собой пошаговый туториал со своим кодом и пояснениями.
Мне нужно заранее знать LangChain или LangGraph?Нет — трек начинается с агентов для новичков, построенных на LangChain и PydanticAI, и включает вводные материалы, например Introduction to LangGraph и Model Context Protocol.
Это можно куда-то развернуть или это чисто для обучения?Это учебный ресурс, а не продукт — здесь нечего устанавливать или разворачивать, потому что каждый ноутбук — самостоятельная техника. Для боевого применения автор отсылает к отдельному репозиторию Agents Towards Production.
Эти туториалы для новичков или для тех, кто уже делает агентов?И для тех, и для других — подборка варьируется от простого разговорного агента до продвинутых мультиагентных систем и рассчитана на всех: от тех, кто делает первые шаги в ИИ, до опытных практиков.
Каких агентов я могу научиться делать?Практичных, из самых разных категорий: бизнес-агенты (поддержка клиентов, планирование путешествий, анализ договоров), креативные (композитор музыки, генератор мемов, игра-детектив), аналитические (самовосстанавливающаяся кодовая база, анализатор звонков отдела продаж), а также новостные и шопинг-агенты вроде ShopGenie.
Какие фреймворки для агентов здесь разбираются?В основном LangChain и LangGraph, плюс отдельные туториалы по PydanticAI, AutoGen, OpenAI Swarm и Model Context Protocol (MCP) для подключения ИИ к внешним ресурсам.
Подборка ещё пополняется?Да — в ней уже 55 туториалов, и список растёт: среди недавних новинок Trace-Based Agent Evaluation, агент с согласованием действий человеком (Human-in-the-Loop Approval Agent), агент приёма документов (Document Intake Agent) и Art Tourguide на LightRAG.
У автора есть похожие репозитории, о которых стоит знать?Да — RAG Techniques с более чем 40 ноутбуками по retrieval-augmented generation, Agents Towards Production для агентов продакшн-уровня, Prompt Engineering Techniques, а также Agent Memory Techniques с 30 ноутбуками про векторные хранилища, графы, Mem0 и Zep.
Creator
NirDiamant
Size
259.1 MB
Stars
24.6K
Updated
CommitSep 28
Age
2y
Commits · 90 Days