Langchain

Langchain 1.0 On October 22, 2025, LangChain finally reached version 1.0. After three years, this milestone represents something significantly different both from previous versions of the framework and from other competitors, which have become quite numerous in the meantime, creating some confusion and bewilderment for those who find themselves defining the software architecture for a new project. To understand how volatile this market is, it’s worth noting that the framework developed by Microsoft called “AutoGen”, with 51k+ GitHub stars, recently entered maintenance mode, as Microsoft decided to focus its efforts on the Microsoft Agent Framework, which is obviously much more integrated with Microsoft’s GenAI services....

1. Introduction The quantity of frameworks emerging for GenAI application development is incredible and, in my opinion, is becoming excessive. Every time a new framework appears, it seems to do more or less the same things as the previous one. Perhaps some have better modularization capabilities or more robust design against long-term obsolescence, but they all seem pretty much the same to me and, although I enjoy experimenting, the study of new miraculous and promising GenAI frameworks is becoming less and less appealing....

Introduction During these scorching August days, I took the opportunity to thoroughly read Google’s A2A protocol specification and try to understand how to use its concepts to design an enterprise Agent architecture, possibly free from technological or platform constraints. What is A2A? The Agent2Agent (A2A) Protocol is an open standard designed and publicly shared by Google to facilitate communication and collaboration among AI agents. The standardization of AI interoperability model is a topic that has been discussed since the very first moments when we started talking about Agents, and there are several reasons for this:...

Abstract As AI developers, we’re always looking for ways to make data more accessible and queryable through natural language. While Retrieval-Augmented Generation (RAG) has revolutionized how we interact with unstructired textual documents, it falls short when dealing with structured data. The RAG approach is so powerful that users or even early stage AI developers may fall in the illusion that it can be applied to any kind of data, including structured data like Excel files....

Intro Last year, Gartner put Generative AI at the peak of inflated expectations in its AI Hype Cycle. Recently, big tech leaders compared the hype around GenAI to the dotcom bubble. Furthermore, according to some rumors, the main Cloud Providers are even giving instructions to their Sales Teams to slow down the enthusiasm towards customers regarding GenAI initiatives and promoting cost-vs-benefits awareness. Has the drop into the trough of disillusionment already begun?...