👉 Click here to follow our complete step-by-step tutorial and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.

Automatically optimize your agent skills using a multi-agent system built with Google ADK (Agent Development Kit) and Gemini. Upload a skill, let the agents generate test scenarios and evaluation criteria, then watch as three specialized ADK agents collaborate to improve your skill through iterative

Function tools are custom Python functions that you create and integrate into your agents. This is the most flexible and commonly used approach for adding specific capabilities to your agents.

A basic realtime voice agent example using OpenAI's Realtime API. This demonstrates the core components for ultra-low latency voice conversations with minimal setup.

According to the ADK docs, Parallel Agents execute their sub-agents concurrently. Each child runs on its own invocation branch but shares the same session.state.

Your journey, perfectly crafted with intelligence.

Welcome to The Magician IA Reader! This project presents a unique application combining the power of Artificial Intelligence with the mystique of tarot reading.

A real-time voice interaction example using the OpenAI Agents SDK with continuous audio streaming. This demonstrates advanced voice pipeline capabilities with live speech detection, real-time processing, and turn-based conversation management.

👉 Click here to follow our complete step-by-step tutorial and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.

https://github.com/user-attachments/assets/a01e09fa-131b-479a-8df3-2d1a61fd80f3

Welcome to the Model Context Protocol (MCP) integration guide! This example demonstrates how to connect your ADK agents with external data sources and tools through the standardized MCP protocol.

↗ external - Open-source agent that operates a real browser

An intelligent meal planning agent built with Agno that helps you discover recipes, analyze nutrition, estimate costs, and create weekly meal plans based on your ingredients and dietary preferences.

👉 Click here to follow our complete step-by-step tutorial and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.

↗ external - Open-source dictation that types where you talk

A complete voice interaction example using the OpenAI Agents SDK with pre-recorded audio input. This demonstrates the basic voice pipeline workflow with speech-to-text, agent processing, and text-to-speech capabilities.

Ramble at your agent by voice. Audit what it heard before it acts.

Master voice-enabled AI agents with the OpenAI Agents SDK! This tutorial demonstrates how to build conversational voice agents using speech-to-text, text-to-speech, and intelligent agent workflows for natural voice interactions.

👉 Click here to follow our complete step-by-step tutorial and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.

👉 Click here to follow our complete step-by-step tutorial and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.

A tutorial demonstrating how to implement a structured customer support ticketing system using Google's ADK (Agent Development Kit) framework. This example shows how to create type-safe, structured support tickets with priority levels, categories, and resolution estimates using Pydantic schemas and

An interactive Tic-Tac-Toe game where two AI agents powered by different language models compete against each other built on Agno Agent Framework and Streamlit as UI.

👉 Click here to follow our complete step-by-step tutorial and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.

Watch two AI agents battle it out in an epic used car negotiation! Built with Google ADK for the backend agents and AG-UI + CopilotKit for a jaw-dropping reactive frontend.

A collection of tools and techniques to optimize your LLM applications - reduce costs, improve performance, and maximize efficiency.

Reduce LLM API costs by 30-60% using TOON (Token-Oriented Object Notation) format for structured data serialization.

Welcome to your first step in the Google ADK journey! This tutorial introduces you to the fundamental concept of creating a simple AI agent using Google's Agent Development Kit.

Welcome to your first step in the OpenAI Agents SDK journey! This tutorial introduces you to the fundamental concept of creating a simple AI agent using OpenAI's Agents SDK.

Learn how to create agents that work with different AI models using OpenRouter. This example shows how ADK can use OpenAI and Anthropic models through separate agent implementations.

Learn how to create agents that return type-safe, structured data using Pydantic models. This tutorial teaches you how to ensure your agents return consistent, validated JSON responses that your applications can reliably process.

Welcome to structured output! This tutorial teaches you how to create agents that return type-safe, structured data instead of plain text. This is crucial for building reliable applications that need predictable data formats.

Welcome to the world of tools! This tutorial teaches you how to create agents that can use custom functions and built-in tools to perform specific tasks. This is where your agents become truly powerful and capable of real-world actions.

Welcome to the world of tools! This tutorial teaches you how to create agents that can use different types of tools to perform specific tasks. This is where your agents become truly powerful and capable of real-world actions.

Tool execution callbacks allow you to monitor when agents use tools, track their execution lifecycle, and analyze the results. This provides visibility into how agents interact with external systems and APIs.

Master automatic conversation memory with Sessions! This tutorial teaches you how to use the OpenAI Agents SDK's built-in session memory to maintain conversation history across multiple agent runs without manual memory management.

A coordinator LlmAgent orchestrates three specialized agents in a sequential workflow: Research → Summarize → Critique. Each agent contributes to building a comprehensive research report.

According to the ADK workflow agents documentation, Sequential Agents execute sub-agents one after another, in sequence. This tutorial demonstrates a Business Implementation Plan Generator that combines web search capabilities with sequential analysis:

Master complex multi-agent workflows! This tutorial teaches you how to coordinate multiple agents using parallel execution, agents-as-tools patterns, and advanced orchestration techniques for building sophisticated AI systems.

👉 Click here to follow our complete step-by-step tutorial and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.

👉 Click here to follow our complete step-by-step tutorial and learn how to build this AI SEO Audit Team from scratch with detailed code walkthroughs, explanations, and best practices.

git blame tells you who last changed a line. Commit Archaeologist reconstructs why the line exists.

👉 Click here to follow our complete step-by-step tutorial and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.

This RAG Application demonstrates how to build a powerful Retrieval-Augmented Generation (RAG) system using locally running Qwen 3 and Gemma 3 models via Ollama. It combines document processing, vector search, and web search capabilities to provide accurate, context-aware responses to user queries.

👉 Click here to follow our complete step-by-step tutorial and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.

A powerful document Q&A application that leverages Hybrid Search (RAG) and Claude's advanced language capabilities to provide comprehensive answers. Built with RAGLite for robust document processing and retrieval, and Streamlit for an intuitive chat interface, this system seamlessly combines documen

👉 Click here to follow our complete step-by-step tutorial and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.

👉 Click here to follow our complete step-by-step tutorial and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.

A multi-agent AI pipeline that generates competitive sales battle cards in real-time, built with Google ADK and Gemini 3.

👉 Click here to follow our complete step-by-step tutorial and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.

An AI Powered Streamlit application that serves as your personal coding assistant, powered by multiple Agents built on the new o3-mini model. You can also upload an image of a coding problem or describe it in text, and the AI agent will analyze, generate an optimal solution, and execute it in a sand

A Streamlit application that simulates a full-service recruitment team using multiple AI agents to automate and streamline the hiring process. Each agent represents a different recruitment specialist role - from resume analysis and candidate evaluation to interview scheduling and communication - wor

This example demonstrates how to connect an ADK agent to a filesystem MCP server using the MCPToolset. The agent can perform file operations like reading, writing, and listing files through the Model Context Protocol.

This app allows you to upload a Resume and a Job Description, then uses an LLM to:

👉 Click here to follow our complete step-by-step tutorial and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.

👉 Click here to follow our complete step-by-step tutorial and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.

A multi-agent AI pipeline for startup investment analysis, built with Google ADK, Gemini 3 Pro, Gemini 3 Flash and Nano Banana Pro.

Callbacks are functions that get executed at specific points during agent execution, allowing you to monitor, log, and control the agent's behavior without modifying the core logic.

👉 Click here to follow our complete step-by-step tutorial and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.

👉 Click here to follow our complete step-by-step tutorial and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.

An investor-grade earnings call companion that turns any YouTube earnings call into a playback-synced analyst workspace. Paste a call URL, watch the video, and let ADK agents surface the numbers, tone shifts, filing context, and market-moving surprises that are easy to miss in a live call.

Release Radar is an always-on dependency briefing agent built with Google ADK. It reads requirements.txt or package.json, checks mapped dependencies against GitHub releases, and reports only changes that need attention: breaking changes, deprecations, security fixes, yanked releases, and major-versi

A tutorial demonstrating how to implement structured output using Google's ADK (Agent Development Kit) framework. This example uses an email generator agent to show how to create type-safe, structured responses with Pydantic schemas and Gemini 3 Flash model.

AgentScout is an always-on Hacker News briefing agent built with Google ADK. It scans Hacker News for high-signal stories about AI agents, MCP, coding agents, workflow automation, and LLM apps, then turns the best links into a concise engineering brief.

According to the ADK workflow agents documentation, LoopAgent repeats a set of sub-agents while sharing the same context/state across iterations. This tutorial demonstrates an Iterative Plan Refiner that improves a plan over multiple iterations and stops when a condition is met.

A sophisticated Retrieval-Augmented Generation (RAG) system that implements a corrective multi-stage workflow using LangGraph. This system combines document retrieval, relevance grading, query transformation, and web search to provide comprehensive and accurate responses.

A Streamlit app demonstrating the "Automatic Critique + Improvement Loop" pattern using GPT-OSS via Groq.

Plugins in Google ADK are custom code modules that can be executed at various stages of an agent workflow lifecycle using callback hooks. Unlike regular callbacks that are configured on individual agents or tools, plugins are registered once on the Runner and apply globally to every agent, tool, and

👉 Click here to follow our complete step-by-step tutorial and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.

👉 Click here to follow our complete step-by-step tutorial and learn how to build this AI SEO Audit Team from scratch with detailed code walkthroughs, explanations, and best practices.

Google ADK provides powerful pre-built tools that are optimized for performance and reliability. These tools integrate seamlessly with Gemini models and provide essential capabilities like web search and code execution.

A Streamlit application demonstrating how Knowledge Graph-based Retrieval-Augmented Generation (RAG) provides multi-hop reasoning with fully verifiable source attribution.

Master monitoring and debugging with built-in tracing! This tutorial teaches you how to use the OpenAI Agents SDK's comprehensive tracing system to visualize, debug, and monitor your agent workflows during development and production.

Third-party tools allow you to integrate existing tool ecosystems from frameworks like LangChain, CrewAI, and others. This dramatically expands your agent's capabilities by leveraging battle-tested tools from the broader AI community.

An advanced web extraction and analysis tool built using Firecrawl's FIRE-1 agent + extract v1 endpoint and the Agno Agent framework to get details of a new startup instantly! This application automatically extracts structured data from startup websites and provides AI-powered business analysis, mak

Welcome to the Firecrawl MCP Agent! This powerful agent demonstrates how to integrate Firecrawl's advanced web scraping capabilities with Google ADK through the Model Context Protocol (MCP).

A streamlined multi-phase research agent built with Google's Interactions API that demonstrates stateful conversations, model mixing, background execution, and automatic infographic generation.

Checks a working, staged, saved, or branch diff against a one-line intent. It flags unrelated paths, new dependencies, public API renames, config or CI edits, oversized hunks, formatting-only files, and subsystem spread. The output

👉 Click here to follow our complete step-by-step tutorial and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.

A powerful document Q&A application that leverages Hybrid Search (RAG) and local LLMs for comprehensive answers. Built with RAGLite for robust document processing and retrieval, and Streamlit for an intuitive chat interface, this system combines document-specific knowledge with local LLM capabilitie

Agents that render UI — not just text.

Welcome to persistent session management! This tutorial teaches you how to create an AI agent that can remember conversations across multiple sessions using DatabaseSessionService with SQLite.

A RAG Agentic system built with the new Gemini 2.0 Flash Thinking model and gemini-exp-1206, Qdrant for vector storage, and Agno (phidata prev) for agent orchestration. This application features intelligent query rewriting, document processing, and web search fallback capabilities to provide compreh

An Agno agentic system that provides expert software architecture analysis and recommendations using a dual-model approach combining DeepSeek R1's Reasoning and Claude. The system provides detailed technical analysis, implementation roadmaps, and architectural decisions for complex software systems.

🎓 FREE Step-by-Step Tutorial

👉 Click here to follow our complete step-by-step tutorial and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.

Learn how to build a trust layer for multi-agent systems that enables secure delegation, trust scoring, and policy enforcement between AI agents.

Master agent-to-agent task delegation! This tutorial teaches you how to use the OpenAI Agents SDK's handoff system to create specialized agents that can intelligently delegate tasks to each other, building powerful multi-agent workflows.

The AI Meme Generator Agent is a powerful browser automation tool that creates memes using AI agents. This app combines multi-LLM capabilities with automated browser interactions to generate memes based on text prompts through direct website manipulation.

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👉 Click here to follow our complete step-by-step tutorial and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.

One model is a bottleneck. A team with one brain, twenty hands, and a board advisor is not.

👉 Click here to follow our complete step-by-step tutorial and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.

A reference implementation demonstrating how to build a multi-agent pipeline that aggregates technical signals from multiple sources, scores them for relevance, assesses risks, and synthesizes an actionable intelligence digest.

Reduce LLM API costs by 50-90% through intelligent context compression. Tool outputs are 70-95% redundant boilerplate—Headroom compresses that away while preserving accuracy.

This project implements an Agentic-RAG architecture to simulate a math professor that solves JEE-level math questions with step-by-step explanations. The system smartly routes queries between a vector database and web search, applies input/output guardrails, and incorporates human feedback for conti

Master context-aware agent development with the OpenAI Agents SDK! This tutorial teaches you how to use RunContextWrapper to pass custom context objects that enable agents to access user data, session information, and state throughout their execution.

Welcome to the memory and session management tutorial! This tutorial teaches you how to create AI agents that can remember conversations, maintain context, and provide personalized experiences across multiple interactions.

Welcome to your first step into session management! This tutorial teaches you how to create an AI agent that can remember conversations within a single session using InMemorySessionService.

Drop-in skills for Claude Code, Codex, Cursor, OpenClaw, Hermes, Antigravity, and any SKILL.md-compatible agent.