Explore open-source architectures utilizing Python SDK / OpenAI API for multi-agent delegation, autonomous reasoning loops, and structured tool routing.
A powerful business consultant powered by Google's Agent Development Kit that provides comprehensive market analysis, strategic planning, and actionable business recommendations with real-time web research.
A multi-agent system built with Google ADK that analyzes photos of your space, creates personalized renovation plans, and generates photorealistic renderings using Gemini 3 Flash and Gemini 3 Pro's multimodal capabilities.
The AI SEO Audit Team is an autonomous, multi-agent workflow built with Google ADK. It takes a webpage URL, crawls the live page, researches real-time SERP competition, and produces a polished, prioritized SEO optimization report.
A multi-agent AI pipeline that generates competitive sales battle cards in real-time, built with Google ADK and Gemini.
A sophisticated multi-agent system built with Google ADK that analyzes landing page designs, provides expert UI/UX feedback, and automatically generates improved versions using Gemini 2.5 Flash's multimodal capabilities.
A multi-agent app built on EvoAgentX that turns a single natural-language goal into a working program. It automatically generates a.
Strategic Thinking Assistant with Local LLM Integration Guided Responses Chatbot .
cd advancedaiagents/multiagentapps/agentteams/aifinanceagentteam 4. Run the team of AI Agents.
A open-source template for building local_ai_legal_agent_team within the Advanced AI Agents category, featuring modular AI integration and clean tool routing.
An open-source autonomous agent for controlling Windows desktop applications and GUI workflows using computer-use models.
One model is a bottleneck. A team with one brain, twenty hands, and a board advisor is not.
git blame tells you who last changed a line. Commit Archaeologist reconstructs finds the introducing commit, orders later modifications, classifies commit.
Dependency Doctor inspects one dependency manifest for surface-level, direct-manifest footguns. It catches unpinned versions, standard-library.
Every developer has the folder. Twenty-something dead projects, each abandoned for reasons nobody wrote down.
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.
How this repo checks that its skills actually work — before they ship and on every change after. Layout: one folder per skill, evals/<skill-name>/.
Ramble at your agent by voice. Audit what it heard before it acts.
Demonstrates advanced handoff configuration including callbacks, structured inputs, and custom tool naming. cp ../env.example .env from agent import main.
Demonstrates advanced orchestration patterns where agents are used as tools by other agents. cp ../env.example .env from agents import Runner.
Demonstrates advanced orchestration patterns where specialized agents are used as function tools by orchestrator agents. cp ../env.example .env.
Demonstrates fundamental agent-to-agent task delegation using the OpenAI Agents SDK handoff system. cp ../env.example .env from agent import main.
Demonstrates fundamental session memory management with SQLiteSession for automatic conversation history. cp ../env.example .env from agent import inmemorysessionexample, persistentsessionexample.
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.
Demonstrates using OpenAI Agents SDK built-in tools like WebSearchTool and CodeInterpreterTool. cp ../env.example .env from agents import Runner.
Demonstrates manual conversation threading with toinputlist() and automatic management with Sessions. cp ../env.example .env from agent import manualconversationexample, sessionconversationexample.
Demonstrates advanced tracing patterns including custom traces, spans, and workflow organization for complex multi-agent systems. cp ../env.example .env.
cd 31customersupportticketagent 4. The response will be a structured JSON with all ticket details.
Demonstrates the built-in automatic tracing system that captures all agent workflow events without any setup required. cp ../env.example .env.
4. The response will be a structured JSON with subject and body fields.
Demonstrates the three execution methods available in the OpenAI Agents SDK: sync, async, and streaming. cp ../env.example .env from agents import Runner.
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.
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).
def calculatecompoundinterest(principal: float, rate: float, years: int) -> dict: Calculate compound interest for an investment. Use this function when users ask about investment growth.
Demonstrates custom function tools creation using the @functiontool decorator. cp ../env.example .env from agents import Runner from agent import rootagent.
Demonstrates advanced session memory operations including item manipulation, conversation corrections, and session management. cp ../env.example .env.
Demonstrates managing multiple concurrent sessions for different users, contexts, and conversation types. cp ../env.example .env from agent import multiusersessions, contextbasedsessions.
A sophisticated multi-agent system built with Google ADK that uses Firecrawl MCP tools for web scraping and coordinates between specialized research and summary agents.
Demonstrates running multiple agents simultaneously using asyncio.gather() for improved performance and quality through diversity. cp ../env.example .env.
A basic personal assistant agent demonstrating the fundamental concepts of agent creation with the OpenAI Agents SDK. cp ../env.example .env from agents import Runner.
A complex structured output agent demonstrating advanced Pydantic schemas for product review analysis. cp ../env.example .env from agents import Runner.
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.
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.
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...
A structured output agent demonstrating Pydantic schema-based responses for customer support ticket creation. cp ../env.example .env from agents import Runner.
Voice agents combine the power of AI language models with speech processing to create natural conversational interfaces. Think of voice agents as AI assistants you can talk to naturally that.
An ADK agent is a programmable AI assistant that can: Think of it as creating a smart function that uses AI to handle complex tasks. The main building block for creating AI agents in ADK.
Context management allows you to pass custom data structures to your agents that persist throughout the entire agent execution. Think of context as a shared state container that.
Guardrails are automated safety mechanisms that validate inputs and outputs to ensure AI agents operate within acceptable boundaries. Think of guardrails as safety checkpoints that.
A coordinator LlmAgent orchestrates three specialized agents in a sequential workflow: Research → Summarize → Critique. Each agent contributes to building a comprehensive research report.
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.
A minimal example demonstrating real-time AI streaming and conversation state management using the Motia framework. streaming-ai-chatbot/.
https://github.com/user-attachments/assets/9201d528-573f-43cc-9d31-571c362318a7 An agent that populates live charts, metrics, and real-time data into a Canvas dashboard instead of just streaming text. Built with CopilotKit, AG-UI, and...
A multi-agent financial coach that analyzes your budget, plans your savings, and builds debt-payoff strategies — rendered as interactive UI cards in a separate report tab. Built with CopilotKit, AG-UI, and Google's ADK on top of Next.js.
Interactive 3D scene renderer using Three.js. Demonstrates streaming code preview and full MCP App integration.
https://github.com/user-attachments/assets/48eeab8d-7845-4d06-83ef-d518a807da03 Book flights, reserve hotels, manage portfolios, and run a kanban board — all inside the chat. Built with CopilotKit, AG-UI, and MCP Apps, showcasing the...
This is an MCP server project bootstrapped with create-mcp-use-app. First, run the development server.
A open-source template for building name within the Generative UI & Frontends category, featuring modular AI integration and clean tool routing.
This script demonstrates how to finetune the Llama 3.2 model using the Unsloth library, which makes the process easy and fast. You can run this example to finetune Llama 3.1 1B and 3B models for free in Google Colab.
Minimal example to finetune Google's Gemma 3 Instruct models with Unsloth using 4-bit loading + LoRA. Small, readable, and runnable on a CUDA GPU.
A terminal-based Notion Agent for interacting with your Notion pages using natural language through the Notion MCP (Model Context Protocol) server.
Learn how to connect a plain OpenAI function-calling loop to a hosted Streamable HTTP MCP server—without an agent framework. The script keeps the complete bridge visible.
The AI Reasoning Agent leverages advanced AI models to provide insightful reasoning and decision-making capabilities. This agent is designed to assist users in various analytical tasks by processing information and generating structured...
