agent-craft and AgentGuide

These are **complements** — Annyfee/agent-craft provides systematized runnable code examples for building agents with LangChain/RAG/LangGraph/MCP, while adongwanai/AgentGuide offers comprehensive development guidance, advanced RAG patterns, and interview preparation to understand the conceptual and practical foundations needed to effectively use those tools.

agent-craft
56
Established
AgentGuide
55
Established
Maintenance 13/25
Adoption 10/25
Maturity 13/25
Community 20/25
Maintenance 20/25
Adoption 10/25
Maturity 5/25
Community 20/25
Stars: 126
Forks: 25
Downloads:
Commits (30d): 0
Language: Python
License: MIT
Stars: 2,340
Forks: 244
Downloads:
Commits (30d): 10
Language: HTML
License:
No Package No Dependents
No License No Package No Dependents

About agent-craft

Annyfee/agent-craft

AI Agent 教学仓库 | 系统化 LangChain、RAG、LangGraph、MCP 全栈实战代码 | 万字博客详解 | 开源可运行示例 | 从零构建智能体

Implements modular agent capabilities through progressive scaffolding: LangChain fundamentals, RAG with vector stores (FAISS/Chroma) and reranking, LangGraph for stateful agentic workflows with LangSmith debugging, MCP server/client patterns using Stdio and HTTP transports, and multi-agent orchestration via Swarm protocol with Claude API. Each of 13+ modules features standalone runnable code with companion blogs explaining design patterns, plus Streamlit UI bindings and deployment examples (Ollama, LM Studio, LangServe).

About AgentGuide

adongwanai/AgentGuide

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

# Technical Summary Provides a systematic, job-market-oriented learning pathway covering the full AI Agent tech stack—from LangGraph/LangChain frameworks and advanced RAG techniques (GraphRAG, reranking) to multi-agent orchestration and context engineering strategies. Distinguishes between algorithm (research-focused, model optimization via SFT/LoRA/RLHF) and engineering (production systems, retrieval pipelines, deployment) career tracks, with curated open-source projects and 1000+ interview problems tied to job preparation. The guide integrates vector databases, embedding models, and workflow orchestration tools (Dify, n8n) while emphasizing portfolio-building through complete end-to-end systems—RAG agents, multi-agent workflows, web automation—rather than isolated demos, designed for both researchers publishing papers and engineers shipping production services.

Scores updated daily from GitHub, PyPI, and npm data. How scores work