GenAI Agents (NirDiamant)

50+ Jupyter tutorials for building GenAI agents, from a simple chatbot to multi-agent systems.

Free· Free and open source (Jupyter notebooks). Author sells an optional paid course at diamant-ai.com and runs a free newsletter; the repository itself has no paywall.AgentsFramework-agnostic; most notebooks default to OpenAI GPT-4 class models, with Ollama and local model examples included.

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Best for

Developers, ML engineers, and technical founders who want fork-ready reference implementations of agent patterns across LangGraph, LangChain, CrewAI, AutoGen, MCP, and PydanticAI.

Skip if

Non-technical users looking for a hosted no-code agent builder, or teams that need a supported product with SLAs rather than tutorial code.

GenAI Agents is a large open-source GitHub repository maintained by Nir Diamant that collects 50+ hands-on Jupyter notebook tutorials for building Generative AI agents, ranging from a simple conversational bot to complex multi-agent systems. Each notebook is a self-contained walkthrough that covers the concept, the code, and the reasoning behind it, using popular Python frameworks including LangChain, LangGraph, PydanticAI, AutoGen, OpenAI Swarm, CrewAI, and the Model Context Protocol (MCP).

The catalogue is organised by difficulty and domain: beginner (conversational agents, question answering, data analysis), framework introductions (LangGraph, MCP), educational and research agents (literature review, Feynman-style tutoring), business agents (customer support, contract analysis, HR assistant, project management, E2E testing, quoting systems), creative agents (music composition, meme generation, murder-mystery game master), analysis agents (self-healing code, memory-enhanced email, sales-call analyser, DataScribe database explorer), news and content agents (podcast generator, journalism assistant, blog-writer swarm), shopping and task-management agents, and QA and advanced patterns such as a controllable RAG agent.

Workflows are consistent across notebooks: install dependencies, wire an LLM (usually OpenAI, occasionally Ollama for local models or LightRAG for knowledge-graph RAG), define agent nodes and state, then run the compiled graph on sample inputs. It is aimed at developers, ML engineers, students, and technical founders who want a working reference implementation to fork rather than starting from a blank file. With more than 23,000 GitHub stars and 3,900 forks it is one of the most-referenced open GenAI agent learning resources.

Editor's take

One of the most useful free learning resources for anyone actually building agents in Python. The value is in the breadth of worked examples across frameworks; treat it as a cookbook you steal patterns from, not a library you import.

— The AI Tool Bible editorial team

Pros

✅ 50+ working notebooks covering an unusually broad range of agent patterns and business use cases
✅ Multiple frameworks side by side (LangChain, LangGraph, PydanticAI, AutoGen, CrewAI, MCP, OpenAI Swarm) rather than being tied to one
✅ MIT-style openness and free to fork, with a permissive contribution workflow
✅ Actively maintained with regular additions such as MCP, LightRAG, and document-intake tutorials
✅ Sits alongside sibling repos (RAG_Techniques, Agent_Memory_Techniques, Agents Towards Production) that share the same didactic style
✅ Large community around it: 23k+ stars, Discord, subreddit, and newsletter feedback loop

Cons

⚠️ It is a tutorial collection, not a runnable product; you have to read code and wire your own keys
⚠️ Notebooks assume Python fluency and comfort with LLM API costs (mostly OpenAI)
⚠️ Heavy LangGraph bias means non-LangGraph users must translate patterns themselves
⚠️ Quality varies across contributed notebooks; some are polished, others are proof-of-concept
⚠️ README is cluttered with course, newsletter, and YouTube CTAs that can drown out the tutorial index

Use cases

LangGraph multi-agent workflowCustomer support agentContract clause analysisScientific literature reviewHR recruitment assistantSales call analysisSelf-healing code agentPodcast generation pipelineMemory-enhanced email triageMCP tool integration tutorial