Nir Diamant (DiamantAI) · on GitHub
A large, community-favourite collection of 64 runnable notebooks, from a beginner conversational agent to multi-agent research, business and QA systems, using LangGraph, PydanticAI, CrewAI, AutoGen, MCP and plain SDK code. It suits Python developers who learn best by reading and modifying working agent code across many frameworks.
Developers and builders. This is an intermediate course in AI Agents & Developer Tools, running about 64 notebook tutorials, self-paced.
Free. The notebooks are free on GitHub; the README also promotes the author's paid books, and running the examples needs LLM API keys. Price seen 2026-11-01.
An influential essay series, modelled on the 12-Factor App, arguing for owning your prompts, context window and control flow, treating tools as structured outputs, and keeping agents small, stateless and pausable. It suits engineers moving agents from demo to production who want design principles rather than another framework tutorial.
Google and Kaggle's intensive pairs a daily whitepaper with hands-on codelabs on foundation models, embeddings, agents, domain-specific LLMs, and MLOps using Gemini and the Agent Development Kit. For developers who want a dense, free bootcamp-style sprint through Google's GenAI stack.
Builds a healthcare multi-agent system from agents written in different frameworks, wraps each in an A2A server, builds A2A clients, and orchestrates them in sequential and hierarchical workflows using Google ADK, LangGraph, BeeAI and Microsoft Agent Framework. For developers who need agents from different teams or vendors to discover and talk to each other.