AI Agents Made Simple reached #1 in Software Development, Software Engineering and Software Design Tools on Amazon.com's Kindle Store.
A decision guide to building AI agents that work: 28 chapters, 148 illustrations, no code.
Get the book on Amazon →
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.
AI Agents Made Simple - the decision guide to building agents that work: 28 chapters on what each pattern does, what breaks, what it costs, and when it is overkill. No code.
Recently added: Thinking Budgets From Scratch, Reflection From Scratch, Plan Then Execute From Scratch, Skills From Scratch, Designing Tools for Agents | 64 tutorials and growing
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 |
🚀 Cutting-edge Updates |
💡 Expert Insights |
🎯 Top 0.1% Content |
Join over 50,000 AI enthusiasts getting unique cutting-edge insights and free tutorials! Plus, subscribers get exclusive early access and special 33% discounts on my books!
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 |
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.
|
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 my 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.
Contributions make this better - propose ideas, share techniques, or give feedback via CONTRIBUTING.md.
r/EducationalAI · Discord · LinkedIn
- 🎓 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
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.
Updated 2026-10-31: most of the use-case notebooks below were contributed in late 2024. They now import cleanly on LangChain 1.x and LangGraph 1.x, and model IDs that providers have retired or scheduled for retirement (gpt-3.5-turbo, gpt-4, gpt-4o, gpt-4o-mini, DALL-E 3, Gemini 1.5, Claude 3, older Groq Llama models) were replaced with current ones. Each notebook still needs its own API keys, and not every notebook was run end to end against live services.
New to agents, or coming back after the 2024 notebooks? These teach one pattern each, in the order a build needs them, and most are plain Python plus one SDK, so they keep running when frameworks change. The newest ones score every run against planted answers and report what the pattern did not fix as well as what it did. The chapter numbers are from AI Agents Made Simple, which explains the decisions behind each pattern without code.
| Ch. | Pattern | Notebook |
|---|---|---|
| 1 | Do you need an agent? Route first | One-Step Decision Router |
| 2 | The agent loop | Agent From Scratch: The While Loop |
| 3 | Workflows before agents | Workflows First: Gated Contract Review |
| 5 | Tools designed for a model | Designing Tools for Agents |
| 10 | Skills loaded on demand | Skills From Scratch |
| 11 | Context engineering | Context Engineering From Scratch |
| 12 | Evals from traces | Trace-Based Agent Evaluation |
| 15 | Humans in the loop | Human-in-the-Loop Approval Agent |
| 16 | What an answer costs | Read vs Write: What One AI Answer Costs |
| 18 | Plan, then execute | Plan Then Execute From Scratch |
| 19 | Reflection with a real check | Reflection From Scratch |
| 20 | Thinking budgets and search | Thinking Budgets From Scratch |
Chapter 27, from a while loop to a harness, is planned as a community notebook (#160).
| ⭐ |  |
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 | Agentic ML pipeline, preprocessing to evaluation |
| 48 | 🎨 Creative | Art Tourguide with LightRAG | LightRAG + LangGraph | Knowledge-graph RAG, interactive art exploration |
| 49 | 🎓 Educational | Gutenberg Sage | LangGraph + Ollama | Local LLM RAG, NER-enhanced retrieval |
| 50 | 💼 Business | Contextual Quoting System | LangGraph | Multi-agent quoting, RAG + structured data |
| 51 | 📊 Analysis | Document Intake Agent | LangGraph | Office docs to LLM-ready markdown, conversion as a tool call |
| 52 | 🎨 Creative | Social Media Publishing Agent | LangGraph | Per-platform generation, self-review loop, publishing via Publora API |
| 53 | 🔍 QA | Human-in-the-Loop Approval Agent | LangGraph | Risk-based approval, in-process checkpoints, auditable tool execution |
| 54 | 🔍 QA | Trace-Based Agent Evaluation | Python | Deterministic trace scoring, case diagnostics, regression quality gates |
| 55 | 🌱 Beginner | Agent From Scratch: The While Loop | Pure Python | The minimal agent loop, tool calls, the retry trap, where a rule must live |
| 56 | 🔍 QA | One-Step Decision Router | Transformers | Decision from one forward pass, calibration check, temperature scaling, confidence gate |
| 57 | 🎓 Educational | Read vs Write: What One AI Answer Costs | Transformers | Prefill vs decode timing, memory-bandwidth check, KV-cache growth, batching, optional power metering |
| 58 | 🎓 Educational | Context Engineering From Scratch | Pure Python + OpenAI | Tokens per layer, clearing used tool results, the prompt-cache trade-off, a scored before/after |
| 59 | 🎓 Educational | Workflows First: Gated Contract Review | Pure Python + OpenAI | Gated workflow vs agent, planted broken files, cost and run-to-run spread, scored exactly |
| 60 | 🎓 Educational | Designing Tools for Agents | Pure Python + OpenAI | Thin API wrapper vs model-designed tools: planted refund tickets, idempotent writes, instructive errors |
| 61 | 🎓 Educational | Skills From Scratch | Pure Python + OpenAI | Skill folders, progressive disclosure vs stuffed prompt at 3/10/30 procedures, trigger tests, poisoned skill |
| 62 | 🎓 Educational | Plan Then Execute From Scratch | Pure Python + OpenAI | Planner, plan checks, assumption-triggered replans; injection test; step-by-step comparison with measured costs |
| 63 | 🎓 Educational | Reflection From Scratch | Pure Python + OpenAI | Self-review vs skeptical reviewer vs failing tests, evidence gate, fixes vs regressions, hidden tests |
| 64 | 🎓 Educational | Thinking Budgets From Scratch | Pure Python + OpenAI | Reasoning effort none-to-high vs checked retries, vote, picker and cascade on scored SQL |
Explore our extensive list of GenAI agent implementations, sorted by categories:
-
Simple Conversational Agent
A context-aware conversational AI maintains information across interactions, enabling more natural dialogues.
Integrates a language model, prompt template, and history manager to generate contextual responses and track conversation sessions.
-
Simple Question Answering Agent
Answering (QA) agent using LangChain and OpenAI's language model understands user queries and provides relevant, concise answers.
Combines OpenAI's GPT model, a prompt template, and an LLMChain to process user questions and generate AI-driven responses in a streamlined manner.
-
Simple Data Analysis Agent
An AI-powered data analysis agent interprets and answers questions about datasets using natural language, combining language models with data manipulation tools for intuitive data exploration.
Integrates a language model, data manipulation framework, and agent framework to process natural language queries and perform data analysis on a synthetic dataset, enabling accessible insights for non-technical users.
-
Introduction to LangGraph: Building Modular AI Workflows
This tutorial introduces LangGraph, a powerful framework for creati