ADW-19/build_a_product_agent

The definitive OpenAI, Claude, MCP, Harness, Evals, and Production Agent Systems learning roadmap.

Learn AI agents from scratch.

Use agent to learn agent - A skeleton course on how to design, build, and operate production AI agents

A systematic AI Agent development tutorial covering LLM agents, RAG, tool use, memory systems, multi-agent systems, LangChain, LangGraph, MCP, and agentic RL.｜从零开始学 AI Agent 开发 | 系统、全面、实战导向的 Agent 开发教程 | 每日自动追踪 arXiv 最新论文 | Learn AI Agent Development from Scratch

A practical AI agents handbook covering agent systems, agentic workflows, LangGraph, MCP/A2A, context engineering, agent memory, evaluation, observability, and multi-agent architecture. Current trend focus: Gemini Interactions API and managed agents, emerging agent runtimes, and production AI workflow patterns.

🤖 Building AI Agent Systems from Scratch — A comprehensive, practical tutorial from fundamentals to production-grade multi-agent applications

A curated collection of resources, frameworks, papers, and best practices for designing, evaluating, and deploying agentic AI systems—from architecture patterns and safety considerations to real-world applications.

An open-source, code-first toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.

A 7×6 framework for agent architecture. 28 patterns, each placed at a coordinate, runnable Python code with verified engineering slices from Claude Code, Aider, OpenHands, DeerFlow. Companion to Designing AI Agents (Manning) by Jia Huang.