A small, framework-agnostic RAG failure diagnostics clinic.

A RAG Agentic system built with Cohere's new model Command-r7b-12-2024, Qdrant for vector storage, Langchain for RAG and LangGraph for orchestration. This application allows users to upload documents, ask questions about them, and get AI-powered responses with fallback to web search when needed.

This is a multimodal RAG app built with Gemini Embedding 2 and Google ADK. Add text, URLs, PDFs, images, audio, or video; ask a question; and get a grounded answer with clear citations.

A Streamlit app that integrates Contextual AI's managed RAG platform. Create a datastore, ingest documents, spin up an agent, and chat grounded on your data.

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

A powerful visual Retrieval-Augmented Generation (RAG) system that utilizes Cohere's state-of-the-art Embed-4 model for multimodal embedding and Google's efficient Gemini 2.5 Flash model for answering questions about images and PDF pages.

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

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

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.

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

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

👉 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.

👉 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.

This Streamlit app answers questions from uploaded PDFs or a documentation URL. Every response is a validated Answer object with exact source quotes, chunk IDs, a confidence score, and an answered decision. If retrieval is too weak, the app

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AI Blog Search is an Agentic RAG application designed to enhance information retrieval from AI-related blog posts. This system leverages LangChain, LangGraph, and Google's Gemini model to fetch, process, and analyze blog content, providing users with accurate and contextually relevant answers.

A Streamlit application that helps users estimate the amount of term life insurance they may need and surfaces currently available policy options. The app is powered by the Agno agent framework, uses OpenAI GPT-5 as the LLM, the E2B sandbox for deterministic coverage calculations, and Firecrawl for

Streamlit app that allows you to chat with any webpage using local Llama-3.1 and Retrieval Augmented Generation (RAG). This app runs entirely on your computer, making it 100% free and without the need for an internet connection.

Build and deploy a production-ready Retrieval-Augmented Generation (RAG) service using Claude 3.5 Sonnet and Ragie.ai. This implementation allows you to create a document querying system with a user-friendly Streamlit interface in less than 50 lines of Python code.

👉 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.

👉 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.

PharmaQuery is an advanced Pharmaceutical Insight Retrieval System designed to help users gain meaningful insights from research papers and documents in the pharmaceutical domain.

A multi-agent research application built with OpenAI's Agents SDK and Streamlit. This application enables users to conduct comprehensive research on any topic by leveraging multiple specialized AI agents.

QA engineer specialized in test strategy, test writing, and coverage analysis. Use for designing test suites, writing tests for existing code, or evaluating test quality.

Manages deprecation and migration. Use when removing old systems, APIs, or features. Use when migrating users from one implementation to another. Use when deciding whether to maintain or sunset existing code.

Discovers and invokes agent skills. Use when starting a session or when you need to discover which skill applies to the current task. This is the meta-skill that governs how all other skills are discovered and invoked.

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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 outputs.

This is the agent-skills project — a collection of production-grade engineering skills for AI coding agents.

This file provides guidance to AI coding agents (Claude Code, Cursor, Copilot, Antigravity, etc.) when working with code in this repository.

Crypto trading platform. Purple-accented dark UI, data-dense dashboards

Productivity launcher. Sleek dark chrome, vibrant gradient accents

AI agent framework. Void-black canvas, emerald accent, terminal-native

Luxury automotive. Chiaroscuro black-white editorial, Ferrari Red with extreme sparseness

Luxury automotive. True black cathedral, gold accent, LamboType custom Neo-Grotesk

Demonstrates manual conversation threading with to_input_list() and automatic management with Sessions.

Demonstrates advanced session memory operations including item manipulation, conversation corrections, and session management.

Demonstrates running multiple agents simultaneously using asyncio.gather() for improved performance and quality through diversity.

This directory contains AI coding assistant skills for building MCP servers with the mcp-use framework.

Demonstrates advanced tracing patterns including custom traces, spans, and workflow organization for complex multi-agent systems.

Demonstrates the built-in automatic tracing system that captures all agent workflow events without any setup required.

Your journey, perfectly crafted with intelligence.

The login page began returning HTTP 500 after yesterday's deployment. The request reaches the authentication callback, then fails before a session cookie is written. There is no confirmed root cause yet. The user asked for help

Hardens code against vulnerabilities. Use when handling user input, authentication, data storage, or external integrations. Use when building any feature that accepts untrusted data, manages user sessions, or interacts with third-party services.

Demonstrates advanced orchestration patterns where agents are used as tools by other agents.

A complex structured output agent demonstrating advanced Pydantic schemas for product review analysis.

A structured output agent demonstrating Pydantic schema-based responses for customer support ticket creation.

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

Production-grade engineering skills for AI coding agents.

Demonstrates advanced orchestration patterns where specialized agents are used as function tools by orchestrator agents.

A basic personal assistant agent demonstrating the fundamental concepts of agent creation with the OpenAI Agents SDK.

Website builder. Bold black and blue, motion-first, design-forward

A Streamlit app that demonstrates the multi-agent + MCP pattern: specialized AI agents that each connect to different MCP servers to handle domain-specific tasks.

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.

This tutorial demonstrates how to use before_agent_callback and after_agent_callback to monitor agent execution lifecycle.

This tutorial demonstrates how to use before_model_callback and after_model_callback to monitor LLM requests and responses.

A Streamlit app that blends agent teamwork with agent-enabled routing and fallback, built entirely on AG2.

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

Agents that render UI — not just text.

👉 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.

👉 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.

Reference catalog of agent orchestration patterns this repo endorses, plus anti-patterns to avoid. Read this before adding a new slash command that coordinates multiple personas, or before introducing a new persona that "wraps" existing ones.

This Streamlit application implements a fully local ChatGPT-like experience using Llama 3.1, featuring personalized memory storage for each user. All components, including the language model, embeddings, and vector store, run locally without requiring external API keys.

A multi-agent app built on EvoAgentX that turns a single natural-language goal into a working program. It automatically generates a multi-agent workflow, executes it to produce code, then verifies and repairs that code

AI Speech Trainer is an AI-powered multi-agent, multimodal public speaking coach that listens to how you speak, watches how you express, and evaluates what you say - helping you become a confident public speaker.

An AI application that simulates a full-service digital agency using multiple AI agents to analyze and plan software projects. Each agent represents a different role in the project lifecycle, from strategic planning to technical implementation.

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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.

A chat-driven kanban board where you and the agent work the same task list. Built with CopilotKit, AG-UI, and LangGraph on top of Next.js. Also doubles as a starter for declarative gen UI via A2UI (flight-search example included).

Tech editorial media. Acid-mint and ultraviolet accents, Manuka display type

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

This Streamlit app enables you to engage in interactive conversations with arXiv, a vast repository of scholarly articles, using GPT-4o. With this RAG application, you can easily access and explore the wealth of knowledge contained within arXiv.

Streamlit app that allows you to chat with a Substack newsletter using OpenAI's API and the Embedchain library. This app leverages GPT-4 to provide accurate answers to questions based on the content of the specified Substack newsletter.

A deep research assistant that plans, searches the web, writes to a virtual filesystem, and renders each tool call as a live card in a workspace pane. Built with CopilotKit, Deep Agents, AG-UI, and Tavily on top of Next.js + LangGraph (Python).

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

LLM app with RAG to chat with GitHub Repo in just 30 lines of Python Code. The app uses Retrieval Augmented Generation (RAG) to provide accurate answers to questions based on the content of the specified GitHub repository.

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.

👉 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.

👉 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.

👉 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.

👉 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.

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.

TripCraft AI uses a sophisticated multi-agent system powered by Agno to create personalized travel experiences. This document explains the different agents and their roles in the system.

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.

Build a multi-agent research pipeline where every AI agent must pass a trust verification before participating, and every action is recorded in a hash-chained audit trail that is independently verifiable.

A comprehensive tutorial series for learning Google's Agent Development Kit (ADK) from basics to advanced concepts. This crash course is designed to take you from zero to hero in building AI agents with Google ADK.

Master the complete OpenAI Agents SDK execution system! This tutorial covers all aspects of running agents including execution methods, streaming, the agent loop, exception handling, and advanced configuration based on the official running agents documentation.

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.

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

This repository serves as both a showcase and template for building AI agents with CopilotKit and LangGraph. It demonstrates how CopilotKit can drive interactive UI beyond just chat, using a collaborative todo list as the primary example.