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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🆕 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 the agent from the video
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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.
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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 | 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 |
Explore our extensive list of GenAI agent implementations, sorted by categories:
🌱 Beginner-Friendly Agents
-
Simple Conversational Agent
Overview 🔎
A context-aware conversational AI maintains information across interactions, enabling more natural dialogues.
Implementation 🛠️
Integrates a language model, prompt template, and history manager to generate contextual responses and track conversation sessions.
-
Simple Question Answering Agent
Overview 🔎
Answering (QA) agent using LangChain and OpenAI's language model understands user queries and provides relevant, concise answers.
Implementation 🛠️
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
Overview 🔎
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.
Implementation 🛠️
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.
🔧 Framework Tutorial
-
Introduction to LangGraph: Building Modular AI Workflows
Overview 🔎
This tutorial introduces LangGraph, a powerful framework for creating modular, graph-based AI workflows. Learn how to leverage LangGraph to build more complex and flexible AI agents that can handle multi-step processes efficiently.
Implementation 🛠️
Step-by-step guide on using LangGraph to create a StateGraph workflow. The tutorial covers key concepts such as state management, node creation, and graph compilation. It demonstrates these principles by constructing a simple text analysis pipeline, serving as a foundation for more advanced agent architectures.
Additional Resources 📚
-
Model Context Protocol (MCP): Seamless Integration of AI and External Resources
Overview 🔎
This tutorial introduces the Model Context Protocol (MCP), an open standard for connecting AI models with external data sources and tools. Learn how MCP serves as a universal bridge between GenAI agents and the wider digital ecosystem, enabling more capable and context-aware AI applications.
Implementation 🛠️
Provides a hands-on guide to implementing MCP servers and clients, demonstrating how to connect language models with external tools and data sources. The tutorial covers server setup, tool definition, and integration with AI clients, with practical examples of building useful agent capabilities through the protocol.
Additional Resources 📚
🎓 Educational and Research Agents
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ATLAS: Academic Task and Learning Agent System
Overview 🔎
ATLAS demonstrates how to build an intelligent multi-agent system that transforms academic support through AI-powered assistance. The system leverages LangGraph's workflow framework to coordinate multiple specialized agents that provide personalized academic planning, note-taking, and advisory support.
Implementation 🛠️
Implements a state-managed multi-agent architecture using four specialized agents (Coordinator, Planner, Notewriter, and Advisor) working in concert through LangGraph's workflow framework. The system features sophisticated workflows for profile analysis and academic support, with continuous adaptation based on student performance and feedback.
Additional Resources 📚
-
Scientific Paper Agent - Literature Review
Overview 🔎
An intelligent research assistant that helps users navigate, understand, and analyze scientific literature through an orchestrated workflow. The system combines academic APIs with sophisticated paper processing techniques to automate literature review tasks, enabling researchers to efficiently extract insights from academic papers while maintaining research rigor and quality control.
Implementation 🛠️
Leverages LangGraph to create a five-node workflow system including decision making, planning, tool execution, and quality validation nodes. The system integrates the CORE API for paper access, PDFplumber for document processing, and advanced language models for analysis. Key features include a retry mechanism for robust paper downloads, structured data handling through Pydantic models, and quality-focused improvement cycles with human-in-the-loop validation options.
Additional Resources 📚
README 内容已截断, 请前往 GitHub 查看完整内容。