Self-evolving memory OS for LLM & AI Agents: ultra-persistent memory, hybrid-retrieval, and cross-task skill reuse, with 35.24% token savings

openma - open-source, self-hosted implementation of Claude's Managed Agents API. Drop-in compatible. Runs on Cloudflare Workers + Durable Objects or Node.js. Apache 2.0.

Where agents co-create, humans are welcome too.

"OpenHarness: Open Agent Harness"

原汁原昧 Claude Code 可运行版; Typescript 类型全修复;企业级可靠性; 安全无毒, lock 文件保真, 可直接 bun i; bun run dev 启动

An open-source SuperAgent harness that researches, codes, and creates. With the help of sandboxes, memories, tools, skills and subagents, it handles different levels of tasks that could take minute…

ClawTeam: Agent Swarm Intelligence (One Command → Full Automation)

Agent harness built with LangChain and LangGraph. Equipped with a planning tool, a filesystem backend, and the ability to spawn subagents - well-equipped to handle complex agentic tasks.

"🐈 nanobot: The Ultra-Lightweight Clawdbot"

视频中分享的方法论代码参考

OpenViking is an open-source context database designed specifically for AI Agents. OpenViking unifies the management of context (memory, resources, and skills) that Agents need through a file syste…

⭐AI-driven public opinion & trend monitor with multi-platform aggregation, RSS, and smart alerts.🎯 告别信息过载，你的 AI 舆情监控助手与热点筛选工具！聚合多平台热点 + RSS 订阅，支持关键词精准筛选。AI 翻译 + AI 分析简报直推手机，也支持接入 MCP 架构，赋能 AI 自然语言对…

Python - 100天从新手到大师

An open-source AI visualization tool that transforms natural language into Mind Maps, Mermaid diagrams, and Echarts. Turn your ideas into structured visuals instantly.

All-in-One Sandbox for AI Agents that combines Browser, Shell, File, MCP and VSCode Server in a single Docker container.

"RAG-Anything: All-in-One RAG Framework"

📚 《从零开始构建智能体》——从零开始的智能体原理与实践教程

《开源大模型食用指南》针对中国宝宝量身打造的基于Linux环境快速微调（全参数/Lora）、部署国内外开源大模型（LLM）/多模态大模型（MLLM）教程

本项目是一个面向小白开发者的大模型应用开发教程，在线阅读地址：https://datawhalechina.github.io/llm-universe/

Educational framework exploring ergonomic, lightweight multi-agent orchestration. Managed by OpenAI Solution team.

手把手带你实战 Huggingface Transformers 课程视频同步更新在B站与YouTube

😏 LeetCode solutions in any programming language | 多种编程语言实现 LeetCode、《剑指 Offer（第 2 版）》、《程序员面试金典（第 6 版）》题解

mall学习教程，架构、业务、技术要点全方位解析。mall项目（50k+star）是一套电商系统，使用现阶段主流技术实现。涵盖了SpringBoot 2.3.0、MyBatis 3.4.6、Elasticsearch 7.6.2、RabbitMQ 3.7.15、Redis 5.0、MongoDB 4.2.5、Mysql5.7等技术，采用Docker容器化部署。

😮 Core Interview Questions & Answers For Experienced Java(Backend) Developers | 互联网 Java 工程师进阶知识完全扫盲：涵盖高并发、分布式、高可用、微服务、海量数据处理等领域知识

mall项目是一套电商系统，包括前台商城系统及后台管理系统，基于SpringBoot+MyBatis实现，采用Docker容器化部署。 前台商城系统包含首页门户、商品推荐、商品搜索、商品展示、购物车、订单流程、会员中心、客户服务、帮助中心等模块。 后台管理系统包含商品管理、订单管理、会员管理、促销管理、运营管理、内容管理、统计报表、财务管理、权限管理、设置等模块。