Category
Explore by categories
A free, lesson-based curriculum for building AI agents in Python from first principles: agentic frameworks, design patterns, tool use, RAG, planning, multi-agent systems, memory, and protocols like MCP and A2A. Hands-on, with Azure AI and Semantic Kernel.
Runnable starter projects for the Claude API you fork and adapt: a knowledge-base customer support agent, a financial analyst that charts results in chat, plus computer-use, browser-use, and autonomous-coding-agent reference implementations.
Awesome LLM Apps is a curated open-source repository collecting awesome LLM applications built with RAG, AI Agents, Multi-agent Teams, MCP, Voice Agents, and more, using models from OpenAI, Anthropic, Gemini, xAI, and open-source alternatives like Qwen or Llama that can run locally.
AI Engineering Hub is a comprehensive GitHub repository offering in-depth tutorials and 93+ production-ready projects on LLMs, RAGs, AI agents, and real-world AI applications for all skill levels.
Curated developer resources that demonstrate building RAG systems, multi-agent workflows, and memory-augmented AI using Oracle AI Database and OCI — includes end-to-end reference apps, notebooks, guides, and workshops for hands-on prototyping.
Curates 500+ open-source AI agent use cases, indexed two ways: by industry vertical (healthcare, finance, legal, retail, and more) and by framework (CrewAI, AutoGen, LangGraph, LlamaIndex, Agno). Each entry links a runnable repo.
Curates 80+ hands-on LLM-powered examples, tutorials and recipes for building agents, RAG systems, voice assistants, and agentic workflows. Includes starter templates, course playlists, and reference apps for rapid prototyping and learning.
Collects 40+ importable n8n workflows from the AI Agents A-Z YouTube channel, each tied to one video episode — spanning content generation, social-media posting, and short-video and narrated-story pipelines, plus companion Docker MCP/REST servers.
Twelve engineering principles for building production-grade LLM agents, modeled on the 12-Factor App. Argues the best agents are mostly deterministic software with a few well-placed LLM calls, not a prompt-and-tools loop.
A code-first collection of runnable tutorials for building production-ready generative-AI agents — step-by-step guides covering stateful workflows, vector memory, RAG, tool integrations, Docker/AWS/RunPod deployment, security guardrails, observability, and multi-agent patterns.
Teaches agent harness engineering — the permissions, memory, persistence, and coordination layer that lets an LLM act — across 20 progressive lessons, each adding one mechanism with standalone runnable code. Chinese-first, plus English and Japanese.
Official, runnable examples for Amazon Bedrock AgentCore, AWS's framework- and model-agnostic platform for deploying AI agents. Spans Runtime, Memory, Gateway, Identity, and Observability through notebooks, code, and infrastructure templates.