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NirDiamant/GenAI_Agents
50+ tutorials and implementations for Generative AI Agent techniques, from basic conversational bots to complex multi-agent systems.
About NirDiamant/GenAI_Agents
NirDiamant/GenAI_Agents is an open-source project on GitHub, mainly written in Jupyter Notebook. 50+ tutorials and implementations for Generative AI Agent techniques, from basic conversational bots to complex multi-agent systems. It currently holds 24,550 stars and 4,142 forks with 0 open issues, and was last pushed on an unknown date (repository created unknown).
Project Overview
AI Homed tracks it on the Today's Trending board, currently at rank #39 with 32 new stars today.
GitHub Repository Details
README
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.
🎓 From demo agent to deployed product
Prompt to Production - my full course on building software with AI the way professionals do: the methods and paradigms behind reliable, efficient, modular production systems, taught systematically. 17 modules, each pairing a video lecture with a hands-on lab, from your first structured prompt to a working production system.
The course is live. Every module is out, lecture and lab.
🎁 Try it on your own repo, free
Your coding agent starts every session knowing nothing about your project, so it guesses. Paste one line into the agent you already have open, and about fifteen minutes later your repository has a docs layer written from the code itself, plus a card scoring what your agent knew before and after.
We ran it on six repositories you already depend on. Each one was asked five questions about itself, cold, then again after the layer was written. The last column counts statements in that project's own documentation that its own code disproves:
| repo | before | after | own docs its code disproves | |---|---|---|---| | fastapi | 2 of 5 | 5 of 5 | 2 | | flask | 3 of 5 | 5 of 5 | 6 | | django | 3 of 5 | 4 of 5 | 1 | | express | 2 of 5 | 4 of 5 | 4 | | requests | 2 of 5 | 4 of 5 | 0 | | langchain | 5 of 5 | 4 of 5 | 5 |
Flask's six include four documentation examples that raise TypeError when you run them. Langchain scored lower afterwards, because it already ships a 380-line agent instruction file and the cold read was grading theirs; that row is in the table anyway.
Clone any of those repos, paste the same line, and check the number yourself. No signup.
👉 Get the full course
🏆 Sponsors
Recently added: Read vs Write: What One AI Answer Costs, Trace-Based Agent Evaluation, Human-in-the-Loop Approval Agent, Document Intake Agent, HR AI Assistant | 57 tutorials and growing
📫 Stay Updated!
The AI That Knows the Answer Before It Speaks [Jev Explained] a chatbot that is 100% sure and wrong, and the model that tells you when it isn't sure — run it |
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Join over 50,000 AI enthusiasts getting unique cutting-edge insights and free tutorials! Plus, subscribers get exclusive early access and special 33% discounts to my book and the upcoming RAG Techniques course!
🎬 Prefer video?
I break these ideas down into short, one-idea-per-episode explainers on YouTube.
🆕 Why AI Uses So Much Energy (It's Not the Thinking)
a power meter on an AI: reading a whole story is cheap, writing the answer is where the energy goes — measure it on your machine
AI Agents Are Just While Loops. That's the Scary Part. the smallest real agent, the trap it builds for itself, and where a rule has to live — run it |
How LLMs Actually Work (and Why AI Makes Things Up) recalling a fact and inventing one are literally the same move |
Context Is the New Code the shift from writing the code to shaping what the model sees |
Stop Thinking Claude Code Is Magic. Here's How It Works what the agent loop is actually doing on every turn |
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 AgentYour 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.
Related Projects
🔍 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.
r/EducationalAI · Discord · LinkedIn
Key Features
- 🎓 Learn to build GenAI agents from beginner to advanced levels
- 🧠 Explore a wide range of agent architectures and applications
- 📚 Step-by-step tutorials and comprehensive documentation
- 🛠️ Practical, ready-to-use agent implementations
- 🌟 Regular updates with the latest advancements in GenAI
- 🤝 Share your own agent creations with the community
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.
| ⭐ | 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 | LangGraph | Web scraping, decision support | | 40 | 🎯 Task Management | Taskifier | LangGraph | Work style analysis, task breakdown | | 41 | 🎯 Task Management | Grocery Management | CrewAI | Inventory tracking, recipe suggestions | | 42 | 🔍 QA | LangGraph Inspector | LangGraph | System testing, vulnerability detection | | 43 | 🔍 QA | EU Green Deal Bot | LangGraph | Regulatory compliance, FAQ system | | 44 | 🔍 QA | Systematic Review | LangGraph | Academic paper processing, draft generation | | 45 | 🌟 Advanced | Controllable RAG Agent | Custom | Complex question answering, deterministic graph | | 46 | 💼 Business | HR AI Assistant | LangGraph | Recruitment pipeline, JD generation, CV analysis | | 47 | 📊 Analysis | ML and Data Science Assistant | LangGraph | Agenti
GitHub Stars & Activity
24,550Stars
4,142Forks
0Open issues
Jupyter NotebookLanguage
GitHub Popularity
GitHub stars24,550
Forks4,142
Open issues0
Primary languageJupyter Notebook
License-
Stars gained today32
Created-
Last pushed-
Trending History
Daily boardrank #39 · ▲ 32 stars
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