Google ADK meets the AG-UI protocol.

You can now easily connect your ADK AI Agents with interactive frontends using AG-UI in minutes.

100% Opensource. You can build generative UI for your agents with ADK as the agent backend and AG-UI as the communication layer between agent and the UI.

Generative UI goes beyond text and let your agent generate and render UI components directly in the chat.

Build a Multi-agent app with MCP using Google ADK without writing a single line of Python Code, in simple YAML.

All of this in ~5 mins. Step-by-step tutorial with opensource code: 1. Install the @Google ADK Python Package and @firecrawl_dev for MCP

China's Alibaba just dropped a Python framework for building multi-agent apps.

AgentScope lets you build AI agents visually with MCP tools, memory, rag, and reasoning capabilities.

Works with any LLM and supports real-time steering.

100% Opensource. 100+ free step-by-step tutorials with code covering:

🚀 AI Agents
📀 RAG Systems
🗣️ Voice AI Agents
🌐 MCP AI Agents
🤝 Multi-agent Teams
🎮 Autonomous Game Playing Agents

P.S: Don't forget to subscribe for FREE to access future tutorials.

theunwindai.com

JSON prompting for LLMs, explained with examples: What is JSON prompting?

JSON prompting is a way of asking an LLM using a clear, structured format (with keys and values) and expecting the response in the same structured style.

Text prompts → inconsistent, messy outputs
JSON prompt → consistent, parseable data

Let's build a local RAG Agent using Google's EmbeddingGemma and Ollama running locally on your machine.

100% Opensource and works without internet. You can follow along this tutorials with step-by-step code instructions on @unwind_ai_

theunwindai.com/p/build-a-loca…

Stop building AI agents that ignore your instructions.

This Python framework guarantees LLM Agents follow your rules in production. Every single time.

100% Opensource. Traditional approach: Write 47-rule system prompts and pray the LLM follows them.

Parlant (@EmcieCo) approach: Define clear guidelines that are contextually matched and enforced.

No more "roll of the dice" conversations.

RAG everything locally!

Index millions of documents on your laptop using 97% less storage than traditional vector databases.

Also work as a drop-in semantic search search MCP for claude Code.

100% Opensource and free with Ollama. 100+ free step-by-step tutorials with code covering:

🚀 AI Agents
📀 RAG Systems
🗣️ Voice AI Agents
🌐 MCP AI Agents
🤝 Multi-agent Teams
🎮 Autonomous Game Playing Agents

P.S: Don't forget to subscribe for FREE to access future tutorials.

theunwindai.com

Learn to build AI Coding Agents from scratch.

Similar to Claude code, Cursor, Windsurf, Amp, Cline and OpenCode.

100% free workshop. GitHub Repo: github.com/ghuntley/how-t…

Build AI agent teams that actually work together.

Learn about multi-agent patterns in Google ADK with step-by-step code tutorials.

100% Opensource and free. 1. Sequential Agent

Opensource code tutorial 👉
github.com/Shubhamsaboo/a…

Microsoft released a No-code AI data analysis tool.

It combines drag-and-drop with natural language to transform data and build visualizations without writing a single line of Python code.

100% opensource. 100+ free step-by-step tutorials with code covering:

🚀 AI Agents
📀 RAG Systems
🗣️ Voice AI Agents
🌐 MCP AI Agents
🤝 Multi-agent Teams
🎮 Autonomous Game Playing Agents

P.S: Don't forget to subscribe for FREE to access future tutorials.

theunwindai.com

I started building Opensource AI Agents and RAG Apps 8 months ago.

It turned out to be the fastest way to learn AI engineering.

If you want to start today, here’s a step-by-step roadmap: 1. Python Programming for AI

• Harvard CS50's Introduction to AI with Python
pll.harvard.edu/course/cs50s-i…

• DeepLearning AI's AI Python for Beginners
deeplearning.ai/short-courses/… DeepLearning.AI

LangChain literally reverse-engineered Claude Code and Manus AI to build Deep Agents.

It's a Python library to turn any LLM into a deep thinking agent with MCP tools.

100% opensource. 100+ free step-by-step tutorials with code covering:

🚀 AI Agents
📀 RAG Systems
🗣️ Voice AI Agents
🌐 MCP AI Agents
🤝 Multi-agent Teams
🎮 Autonomous Game Playing Agents

P.S: Don't forget to subscribe for FREE to access future tutorials.

theunwindai.com

Google just released LangExtract Python library.

It can extract structured data from unstructured docs with precise sources in just a few lines of code.

100% Opensource. 100+ free step-by-step tutorials with code covering:

🚀 AI Agents
📀 RAG Systems
🗣️ Voice AI Agents
🌐 MCP AI Agents
🤝 Multi-agent Teams
🎮 Autonomous Game Playing Agents

P.S: Don't forget to subscribe for FREE to access future tutorials.

theunwindai.com

Let's compare Kimi k2, Qwen3 Coder, DeepSeek R1, GLM-4.5 and Claude Sonnet 4 for AI code generation.

1. Kimi K2 2. Qwen3 Coder by @AlibabaGroup

3D grid of particles animated by a passing gravitational wave.

This new Claude 4 level coding model from China outperforms DeepSeek R1, Kimi k2, Gemini 2.5 Pro and OpenAI GPT-4.1.

Meet Qwen3-Coder - a 480B MoE model with 35B active parameters that excels in both coding and agentic tasks.

And it's 100% Opensource. Let that sink in. Plus, you can use this with the newly launched opensource Qwen Code.

A terminal based AI workflow tool inspired by Gemini CLI.

GitHub Repo: github.com/QwenLM/qwen-co…

After DeepSeek R1, there's new Claude 4 level model from China that outperforms DeepSeek v3, Qwen and OpenAI GPT-4.1

Meet Kimi k2 - 1 trillion parameter model purpose-built for agentic workflows with native MCP integration.

100% Opensource and FREE to try. Let that sink in. Kimi k2 by @Kimi_Moonshot is a SOTA AI coding model that gives amazing 1 shot results at coding prompts.

Grok 4 builds an AI Investment Agent team just by going through the documentation.

Replit lets me run, test and deploy that agent team directly from the browser.

All of this in less than 2 mins. I have created 100+ AI Agents and RAG tutorials, 100% free and opensource.

P.S: Don't forget to star the repo to show your support 🌟
github.com/Shubhamsaboo/a…

Build a Customer Support Ticket Agent with Structured output using Google Agent Development Kit.

100% Opensource code with step-by-step tutorial: 1. Install the necessary Python Libraries

Run the following commands to install the required libraries:

Build an AI Agent with Structured Output using Google ADK (step-by-step instructions): What Are Structured Outputs?

Think of structured outputs as a strict dress code for your AI agent's responses.

Instead of getting random, unpredictable text, you get perfectly formatted JSON every single time.

Let's build our AI Agent with structured output 👇

This Chinese AI model just changed document OCR forever.

It can parse complex documents with text, tables, formulas and figures in parallel simultaneously using task-specific prompts.

100% opensource. Dolphin uses a smart two-stage parsing approach.

Stage 1: Analyzes page layout and generates element sequence in natural reading order.

Stage 2: Parses all document elements simultaneously using task-specific prompts.

This is crazy!

Meet the world's first fully Agentic Download Agent and AI Drive.

It can literally search, download, and organize any files instantly in the AI drive from just a single prompt.

Works with PDFs, images, videos, music, and Office documents.

3 wild examples: 1. Batch Download Images Instantly:

The Download Agent searches trusted platforms like Pixabay and Unsplash, gathers your images, and places them in a dedicated folder in your AI Drive.

One prompt does it all—no more tedious clicking or sorting.