Yuxi-Know and agent-craft

These two tools are ecosystem siblings, as both offer comprehensive educational resources and platforms for building AI agents, with the first providing a ready-made platform integrating a knowledge graph and LightRAG, while the second focuses on systematic full-stack practical code examples and tutorials for various frameworks including LangChain, RAG, and LangGraph.

Yuxi-Know
70
Verified
agent-craft
53
Established
Maintenance 22/25
Adoption 10/25
Maturity 16/25
Community 22/25
Maintenance 10/25
Adoption 10/25
Maturity 13/25
Community 20/25
Stars: 4,533
Forks: 596
Downloads:
Commits (30d): 246
Language: Python
License: MIT
Stars: 126
Forks: 25
Downloads:
Commits (30d): 0
Language: Python
License: MIT
No Package No Dependents
No Package No Dependents

About Yuxi-Know

xerrors/Yuxi-Know

结合LightRAG 知识库的知识图谱智能体平台。 An agent platform that integrates a LightRAG knowledge base and knowledge graphs. Build with LangChain v1 + Vue + FastAPI, support DeepAgents、MinerU PDF、Neo4j 、MCP.

This platform helps organizations build powerful AI assistants that can understand and reason using your internal documents and structured knowledge. You input various documents like PDFs, Word files, or images, along with optionally defining relationships between concepts, and it outputs an intelligent agent capable of answering complex questions or generating reports based on this deep understanding. It's ideal for knowledge managers, researchers, or business analysts who need to leverage vast amounts of internal information effectively.

knowledge-management business-intelligence research-automation document-analysis organizational-intelligence

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

This project is a systematic guide for developers looking to build sophisticated AI agents from scratch using Python. It takes you from basic large language model (LLM) calls to integrating advanced features like external tools and knowledge bases. You'll learn to create intelligent systems that can understand, reason, and act, ultimately deploying them as functional applications.

AI-agent-development LLM-engineering LangChain-development RAG-implementation AI-application-deployment

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