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agent-craft MCP Server

Annyfee/agent-craft

Score: 91.6 → 0% Rank #248 Agent Framework
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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

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Overview

Annyfee/agent-craft is a Python tool licensed under MIT. 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 Topics: beginner-guide, langchain, multi-agent, rag, tutorial, ai-agent, langgraph, mcp, artificial-intelligence, chatbot, deepseek, openai, python.

Ranked #248 out of 24846 indexed tools.

In the top 1% of all indexed tools.

Ecosystem

Python MIT
beginner-guidelangchainmulti-agentragtutorialai-agentlanggraphmcpartificial-intelligencechatbotdeepseekopenaipython

Score Breakdown

StarsFreshnessIssue HealthContributorsDependents
Stars 15% 509

509 stars → growing community

Freshness 25% 8d ago

Last commit 8d ago → recently updated

Issue Health 25% 100%

4/4 issues closed → responsive maintainer

Contributors 10% 5

5 contributors → small team

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A
PyPI Downloads N/A
Forks 72
Description Good
License MIT

Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →

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From the README

# 🤖 Agent Craft —— 从零构建全栈AI智能体

> ✅ **系统性开源教学项目**,手把手带你用 Python 构建可运行的 AI Agent,涵盖 Prompt、LLM、LangChain、RAG、LangGraph、MCP 到部署发布全流程。

## 📘 项目简介

如果你正在学习 AI 智能体开发,却卡在“知道概念但不会动手”或“会调 API 但不懂原理”的阶段——  
**Agent Craft 就是为你设计的学习路径。**

我们从最基础的 LLM 调用开始,一步步带你构建完整的 Agent 能力栈:  
- 让模型调用外部工具(Function Calling)  
- 接入私有知识库实现问答(RAG)  
- 用 LangGraph 实现带状态、可调试的决策流程  
- 整合记忆、工具与检索,打造真正可用的智能体  

所有内容围绕两个原则展开:  
- 💻 **代码可运行**:每个模块独立、注释清晰、本地一键复现  
- 📖 **原理可理解**:配套 CSDN 博客详解设计思路与关键机制  

> 💡 **我们不重复造轮子,但也不止于调用框架**——  
> 而是在 LangChain、LangGraph 等主流工具中,看清 Agent 是如何思考、决策与行动的。

---

## 🧭 模块总览(15 模块)

> 💡 每个模块对应独立目录(含代码、说明与示例),可独立运行与学习。  
> 🔥 已更新至 13 Streamlit 快速入门,持续更新中!

|     章节      | 模块                                                                                                       |                                  博客                                  | 核心关键词                                                        |  难度   |
|:-----------:|:---------------------------------------------------------
Read full README on GitHub →

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