The definitive resource for Agent Skills - modular capabilities revolutionizing AI agent architecture

Find zero-days while you sleep. DeepZero is an automated vulnerability research framework that parses, decompiles, and analyzes thousands of Windows kernel drivers for exploitable IOCTLs natively using AI agents.

Build your own LLM-native WIKI (knowledge library). Search, extract, summarize, Q&A with contextual RAG, layered knowledge graph, and reinforced memory. Importantly use selected context to automatically generate skills, empowered by Claude subagents + CodeAct pipeline and gated by human review. Try Live Demo: https://byo-wiki-demo.onrender.com

Hands-on crash course for Claude Code with branch-based projects on MCP, subagents, hooks, and automation.

This repository contains hands-on projects, code examples, and deployment workflows. Explore multi-agent systems, LangChain, LangGraph, AutoGen, CrewAI, RAG, MCP, automation with n8n, and scalable agent deployment using Docker, AWS, and BentoML.

Deep Competitive Analyst is a 'deep agent' style LLM assistant built to automate the creation of company profiles and competitive analyses

Creating 'deep agents' to encourage LLM's to complete long horizon tasks.

Stop building AI agents from scratch. Bootstrap starter Agent app with LangGraph, CopilotKit, and beautiful Generative UIs.

interrupt 2026 deepagents workshop

deepclaw，一个开源的 agent/rag 脚手架

A demonstration of with a DeepAgent that generates CSV and PDF reports in custom React components.

deepagents-showcase is a hands-on project demonstrating how to build hierarchical, multi-step AI systems using LangChain’s Deep Agents. It showcases task decomposition, planning, sub-agent delegation, and long-running workflows in a practical, real-world setup.

Experimental AI system for financial applications

Turn a topic into a real, verified course — an agentic course-synthesis engine with deterministic prerequisite-ordering and claim-grounding moats.

A structured integration layer around LangChain's Deep Agents

A hands-on session where you'll build a production AI agent from scratch using the Deep Agents framework, test and deploy it, and walk through LangSmith tracing and evaluations so you can observe and improve what you've shipped.

An Azure blob storage backend for LangChain Deep Agents

A starting point for building custom agentic LLM applications using Open Source tooling and models. Incorporates Ollama, Open WebUI, Langchain, Streamlit, Chroma, & PGVector using Docker and Docker Compose and optionally Codespaces.