rohitg00/ai-engineering-from-scratchGitHub
If you prefer an English, MIT-licensed hands-on engineering course over the bilingual Datawhale book.
Learn it. Build it. Ship it for others.
- 54.5k
- Python
- MIT
active1mo ago
datawhalechina/hello-agents Alternatives
AI agent education / tutorial
《从零开始构建智能体》(Hello-Agents) is a free bilingual tutorial teaching readers to understand and build AI-native agents from first principles, with code examples and practical projects.
Drop-in peers
Same problem, same approach. Swapping one for another is a config change, not a rewrite.
bojieli/ai-agent-bookGitHub
If you want a formal Apache-2.0 open book with companion code and a more published-style textbook.
《深入理解 AI Agent：设计原理与工程实践》（李博杰 著）开源主仓库：全书正文、编译版 PDF 与按章配套代码
- 40.8k
- Python
- Apache-2.0
active2mo ago
didilili/ai-agents-from-zeroGitHub
If you want a single roadmap covering LangChain/LangGraph/Coze/Dify/MCP alongside agent basics.
🚀 2026 最系统的 AI Agent 速成指南｜智能体实战教程 · 完整学习路径 + 实战项目 + 面试题库 · 对标大模型应用开发工程师岗位 · 覆盖LangChain / LangGraph / Coze / Dify / MCP / skills / LLM / RAG / 提示词 · 企业级部署与微调 · 从0到企业级落地 + 从学习到上线项目 + 面试准备一体化
- 4.6k
- Python
- MIT
active1mo ago
Snailclimb/AIGuideGitHub
If you need a briefer, interview-oriented AI application guide rather than a deep from-scratch tutorial.
AI 应用开发、AI 编程实战与面试指南，涵盖 LLM、Agent、RAG、MCP、Claude Code、Codex 等核心技术与工程实践。
- 611
- none
active3mo ago
Haozhe-Xing/agent_learningGitHub
If you prefer an organized single-page curriculum with explicit sections on multi-agent and RAG.
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
- 520
- HTML
- MIT
active29d ago
Listed as an alternative to
These projects were analysed and named hello-agents among their alternatives. The relationship is not symmetric — how hello-agents rates them is a separate judgement, made when hello-agents is analysed in its own right.
didilili/ai-agents-from-zeroGitHub
calls hello-agents “Pick it when you want the Datawhale community's from-zero agent construction notes; the seed bundles more production project code and interview material.”
🚀 2026 最系统的 AI Agent 速成指南｜智能体实战教程 · 完整学习路径 + 实战项目 + 面试题库 · 对标大模型应用开发工程师岗位 · 覆盖LangChain / LangGraph / Coze / Dify / MCP / skills / LLM / RAG / 提示词 · 企业级部署与微调 · 从0到企业级落地 + 从学习到上线项目 + 面试准备一体化
4.6kPythonactive
bryanyzhu/agentic-ai-system-courseGitHub
calls hello-agents “choose when you want a Chinese from-zero agent-building course with runnable examples instead of a framework-agnostic skeleton”
Use agent to learn agent - A skeleton course on how to design, build, and operate production AI agents
611JavaScriptactive
Snailclimb/AIGuideGitHub
calls hello-agents “Use it when you prefer building agents from scratch over the seed's applied 'demo-to-production' framing.”
AI 应用开发、AI 编程实战与面试指南，涵盖 LLM、Agent、RAG、MCP、Claude Code、Codex 等核心技术与工程实践。
611active
Haozhe-Xing/agent_learningGitHub
calls hello-agents “Choose it if you want a Chinese from-scratch agent-building tutorial with a different explanation style.”
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
520HTMLactive
adongwanai/AgentGuideGitHub
calls hello-agents “Choose when you want Datawhale's practical agent-building tutorial instead of a job-oriented resource hub.”
https://adongwanai.github.io/AgentGuide | AI Agent开发指南 | LangGraph实战 | 高级RAG | 转行大模型 | 大模型面试 | 算法工程师 | 面试题库 | 强化学习｜数据合成
9.6kMDXactive
AccumulateMore/CVGitHub
calls hello-agents “when you want a focused, hands-on Chinese tutorial for building agents from scratch.”
✅（已完结）超级全面的 深度学习 笔记【土堆 Pytorch】【李沐 动手学深度学习】【吴恩达 深度学习】【大飞 大模型Agent】
23.7kJupyter Notebookactive
luhengshiwo/LLMForEverybodyGitHub
calls hello-agents “Choose when you need an in-depth agents-only tutorial; it lacks the seed's broader LLM and interview content.”
每个人都能看懂的大模型知识分享，LLMs春/秋招大模型面试前必看，让你和面试官侃侃而谈
7.4kJupyter Notebookactive