Nir Diamant is an AI researcher, educator, and author based in Israel. He is the founder of DiamantAI, author of the Amazon Bestseller 'RAG Made Simple' (ASIN B0D76734SZ, hit #1 in Generative AI at launch), and creator of four flagship open-source GenAI repositories with over 70,000 combined GitHub stars. His tutorials and writing reach 500,000+ developers every month.

DiamantAI is Nir Diamant's educational platform, providing 130+ free open-source GenAI tutorials on AI agents, RAG (Retrieval-Augmented Generation), prompt engineering, and production AI deployment. It includes a 25,000+ subscriber Substack newsletter, a 4,000+ member Discord community, and the 10,000+ member r/EducationalAI subreddit.

What is RAG Made Simple?

RAG Made Simple is Nir Diamant's book on Retrieval-Augmented Generation, published in April 2026. It covers 22 RAG techniques with intuition, side-by-side comparisons, and illustrations, expanding on his 27,000+ star RAG Techniques open-source repository. It hit #1 in Generative AI on Amazon in its first week and has sold 1,500+ copies with a 4.4-star average rating. Available on Kindle ($9.99), Paperback ($24.99), and Free with Kindle Unlimited. Kindle ASIN B0D76734SZ.

What topics do the tutorials cover?

The tutorials cover Generative AI, AI Agents, RAG (Retrieval-Augmented Generation) systems, Prompt Engineering, Large Language Models (LLMs), LangChain, LangGraph, Model Context Protocol (MCP), and practical AI development techniques including agentic workflows and multi-agent systems.

Are the GenAI tutorials free?

Yes, all 130+ GenAI tutorials by Nir Diamant are completely free and open-source, available on GitHub with runnable Jupyter notebooks and code files.

RAG (Retrieval-Augmented Generation) is a technique that enhances AI responses by retrieving relevant information from external knowledge sources before a language model generates an answer. This grounds model responses in factual data and reduces hallucinations. Nir Diamant's RAG Techniques repository and his book 'RAG Made Simple' cover 22 production RAG techniques in depth.

AI agents are autonomous systems that use language models to perceive inputs, reason about next steps, and take actions toward goals in a loop. Nir Diamant's 'GenAI Agents' (19,000+ stars) and 'Agents Towards Production' (17,000+ stars) repositories cover agent architectures, multi-agent systems, memory, tool use, and production deployment.

How can I sponsor DiamantAI?

DiamantAI offers sponsorship options including GitHub repository sponsorship, newsletter sponsorship (25,000+ subscribers), social media promotion, and webinar partnerships. Visit diamant-ai.com/sponsorship for rate cards and details.

What is Nir Diamant's newsletter about?

The DiamantAI Substack newsletter has 25,000+ subscribers and covers GenAI, AI agents, RAG systems, prompt engineering techniques, and practical AI development insights, usually with weekly deep-dive articles.

Does Nir Diamant offer AI advisory services?

Yes. Nir Diamant provides strategic AI advisory for companies building GenAI products, including GenAI strategy consultation, AI system architecture review, and implementation guidance. See diamant-ai.com/for-business for details.

Where can I find Nir Diamant's GitHub repositories?

All repositories are at github.com/NirDiamant. The four flagship repos are RAG_Techniques, Prompt_Engineering, GenAI_Agents, and agents-towards-production, with over 70,000 combined stars.

Google's Agent2Agent (A2A) Explained

November 18, 2025byNir Diamant

As AI agents become more specialized and numerous, they need a way to communicate and collaborate with each other, even when built by different teams using different frameworks. Google's Agent2Agent (A2A) protocol addresses this by providing a standardized communication layer for inter-agent collaboration, similar to how HTTP standardized communication between web servers.

A2A defines a common message format and interaction protocol that allows agents to discover each other's capabilities, negotiate tasks, share intermediate results, and coordinate complex workflows. Each agent publishes an "Agent Card", a machine-readable description of its capabilities, input/output formats, and authentication requirements. When one agent needs help from another, it queries available Agent Cards, finds a suitable collaborator, sends a structured task request, and receives results in a predictable format. This discovery-negotiate-execute pattern means agents can collaborate without being hard-coded to work together.

The protocol is complementary to Anthropic's MCP rather than competitive. While MCP standardizes how a single AI model connects to external tools and data sources, A2A standardizes how multiple AI agents coordinate with each other. In practice, an AI system might use MCP to connect individual agents to their tools, and A2A to orchestrate collaboration between those agents. The article explores real-world scenarios where A2A enables powerful multi-agent workflows: a research agent delegating fact-checking to a specialized verification agent, a coding agent requesting security review from an audit agent, or a customer service agent escalating to a domain expert agent. As the agent ecosystem grows, interoperability protocols like A2A will be essential infrastructure.

TL;DR

Google's new Agent2Agent protocol enables AI agents to communicate and collaborate across platforms. Here's how it works and why it matters.

Key Takeaways

A2A is Google's protocol for standardized inter-agent communication, agents discover, negotiate, and collaborate without hard-coded integrations.

Each agent publishes an 'Agent Card' describing its capabilities, enabling dynamic discovery and task delegation between agents.

A2A complements MCP, MCP connects agents to tools, while A2A coordinates collaboration between multiple agents.

As AI agents multiply and specialize, interoperability protocols like A2A become essential infrastructure for multi-agent systems.

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Google's Agent2Agent (A2A) Explained

TL;DR

Key Takeaways

Related Tutorials

Free Resources

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