原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

Claude Flow

作者:ruvnet版本 2.0.0-alpha.107i★ –i在你的电脑上运行
注册名称io.github.ruvnet/claude-flow

安装

概览

作者提供的说明(英文原文)

AI orchestration with hive-mind swarms, neural networks, and 87 MCP tools for enterprise dev.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 其他
  • 你需要填写:ANTHROPIC_API_KEY, GITHUB_TOKEN, FLOW_NEXUS_API_KEY

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 需要你自己填写: ANTHROPIC_API_KEY, CLAUDE_FLOW_MODE, CLAUDE_FLOW_MEMORY_PATH, CLAUDE_FLOW_MAX_AGENTS, CLAUDE_FLOW_PORT, GITHUB_TOKEN, FLOW_NEXUS_API_KEY

标为机密的值是你账户的钥匙。千万不要分享给别人,也不要粘贴到聊天里。

你需要

  • npm已安装 Node.js(用于 npx 命令)

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "claude-flow": {
      "command": "npx",
      "args": [
        "-y",
        "claude-flow"
      ],
      "env": {
        "ANTHROPIC_API_KEY": "your-value",
        "CLAUDE_FLOW_MODE": "your-value",
        "CLAUDE_FLOW_MEMORY_PATH": "your-value",
        "CLAUDE_FLOW_MAX_AGENTS": "your-value",
        "CLAUDE_FLOW_PORT": "your-value",
        "GITHUB_TOKEN": "your-value",
        "FLOW_NEXUS_API_KEY": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add claude-flow --env ANTHROPIC_API_KEY=your-value --env CLAUDE_FLOW_MODE=your-value --env CLAUDE_FLOW_MEMORY_PATH=your-value --env CLAUDE_FLOW_MAX_AGENTS=your-value --env CLAUDE_FLOW_PORT=your-value --env GITHUB_TOKEN=your-value --env FLOW_NEXUS_API_KEY=your-value -- npx -y claude-flow

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "claude-flow": {
      "command": "npx",
      "args": [
        "-y",
        "claude-flow"
      ],
      "env": {
        "ANTHROPIC_API_KEY": "your-value",
        "CLAUDE_FLOW_MODE": "your-value",
        "CLAUDE_FLOW_MEMORY_PATH": "your-value",
        "CLAUDE_FLOW_MAX_AGENTS": "your-value",
        "CLAUDE_FLOW_PORT": "your-value",
        "GITHUB_TOKEN": "your-value",
        "FLOW_NEXUS_API_KEY": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "claude-flow": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "claude-flow"
      ],
      "env": {
        "ANTHROPIC_API_KEY": "your-value",
        "CLAUDE_FLOW_MODE": "your-value",
        "CLAUDE_FLOW_MEMORY_PATH": "your-value",
        "CLAUDE_FLOW_MAX_AGENTS": "your-value",
        "CLAUDE_FLOW_PORT": "your-value",
        "GITHUB_TOKEN": "your-value",
        "FLOW_NEXUS_API_KEY": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上,无需账户。

需要你填写的值

  • ANTHROPIC_API_KEY 机密 env
    Anthropic API key for Claude AI models
  • CLAUDE_FLOW_MODE env
    Operation mode: development, production, or test
  • CLAUDE_FLOW_MEMORY_PATH env
    Path for persistent memory storage
  • CLAUDE_FLOW_MAX_AGENTS env
    Maximum number of concurrent agents
  • CLAUDE_FLOW_PORT env
    MCP server port
  • GITHUB_TOKEN 机密 env
    GitHub personal access token for repository operations
  • FLOW_NEXUS_API_KEY 机密 env
    Flow Nexus cloud platform API key

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。

它能做什么

它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 ruvnet 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    它在你的电脑上运行;我们只能读取互联网端点的工具列表

与“其他”中的其他服务器比较i

  • 星标数多于此分类中其他 32572 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 32572 个服务器的 0% 更新
  • 基础检查结果等于或优于此分类中其他 32572 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。

常见问题

Claude Flow 安全吗?

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。

它需要哪些密钥或代码?

你需要填写:ANTHROPIC_API_KEY, GITHUB_TOKEN, FLOW_NEXUS_API_KEY

它在我的电脑上运行还是在云端?

它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(2.0.0-alpha.107)于14个月前进入注册中心。

哪些应用可以使用它?

支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。

有什么可以替代它?

同一分类中有:Spec Driven Development, Remote Desktop Commander, AvaHR

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Spec Driven Development

作者:formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

在你的电脑上运行★ 440MIT14个月前更新
安装

Remote Desktop Commander

作者:desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

在云端运行★ 211昨天更新
安装

AvaHR

作者:avahr.com

Read hiring data and create inactive assessment and questionnaire drafts for human review.

在云端运行今天更新
安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

An agent meta-harness for Claude Code and Codex.

Get started with Ruflo

Start in Claude Code with core tools, the visual console and runtime mods. Requires Claude Code 2.1.287 or later. Run inside Claude Code:

/plugin marketplace add ruvnet/ruflo

/plugin install ruflo-core@ruflo /plugin install ruflo-console@ruflo /plugin install ruflo-mods@ruflo /reload-plugins /ruflo

From a terminal on Claude Code 2.1.292 or later, one command adds the marketplace and installs a plugin: claude plugin install ruflo-core@ruflo --marketplace ruvnet/ruflo.

Core provides foundation tools. Console opens the agent cockpit. Mods add routing and policy enforcement inside Claude Code. Mods run with your account's permissions; review their code before installing.

Using Codex or another MCP enabled tool? Use NPX for Ruflo project setup and connect your client to the Ruflo MCP server. Run the setup wizard in your project terminal:

npx ruflo@latest init wizard

For clients that support local stdio MCP servers, configure command npx and arguments ["-y", "ruflo@latest", "mcp", "start"]. The equivalent server command is:

npx -y ruflo@latest mcp start

MCP exposes Ruflo tools to your client. The console and mods above are Claude Code integrations; hook support depends on the client.

Prefer a plain terminal? Optional. The console also runs on its own, with no Claude Code: npx ruflo tui (add --plain to print one frame for scripts). It shows Overview, Cost, Swarm, Events and Missions, plus an optional AI page. Terminal UI guide

Compare install paths · Open the console · User guide

Optional: ruOS Desktop

Connect ruOS to ChatGPT or Claude via MCP:

https://ruos.cognitum.one/mcp
npx ruvector

Ruflo

English · 简体中文

[📖 RuFlo Explained — Build an AI Team That Plans, Remembers, Tests, and Improves](docs/ruflo-explained.md) A 14-chapter guide: from the basic idea to a first useful task, then memory, agent teams, plugins, cost and verification.

Agent = Model + Harness. The model writes; the harness gives it tools, memory, loops, sandboxes, and controls so it can actually work. Ruflo is the harness — the execution layer around Claude Code and Codex that adds 100+ specialized agents, coordinated swarms, self-learning memory, federated comms across machines, and enterprise security guardrails. So agents don't just run, they collaborate.

One npx ruflo init gives Claude Code a nervous system: agents self-organize into swarms, learn from every task, remember across sessions, and — with federation — securely talk to agents on other machines without leaking data. You keep writing code. Ruflo handles the coordination.

Conceptual learning loop. Local vector retrieval and contrastive updates can run without an LLM; embeddings and configured learning modules are still required. See the RuVector training integration and trajectory contrastive learning .

Architecture in text

User → Ruflo (CLI/MCP) → Router → Swarm → Agents → Memory → LLM providers. Memory feeds useful experience back into routing. This is a conceptual flow, not a live execution trace.

New to Ruflo? You don't need to learn 406 MCP tools or 61 CLI commands. After init, just use Claude Code normally — the hooks system automatically routes tasks, learns from successful patterns, and coordinates agents in the background.

An origin you can inspect

Built by [Reuven Cohen (rUv)](https://github.com/ruvnet), originally as Claude Flow. The first commit, June 2, 2025, describes a code orchestration layer for recursive agent cycles: Claude writes, edits, tests and improves. The June 10 source snapshot adds orchestration, task scheduling, memory and terminal modules.

A harness is the workbench around the model: it organizes tasks, carries context between steps and helps check results. This establishes Ruflo's lineage, not a universal first across every Claude Code tool. See the illustrated explainer, exact commits and earlier ecosystem context.

Grok Build: npx ruflo init --grok writes the project host surface (.grok/, the team bus, and the operator guide) and nothing outside the project; add --dry-run to preview. Setup, the spawn_subagent contract, the optional status row, and what to re-check when Grok updates: the Grok operator guide.

📖 Background — where the name comes from

Claude Flow is now Ruflo — named by rUv, who loves Rust, flow states, and building things that feel inevitable. The "Ru" is the rUv. The "flo" is working until 3am. Underneath, powered by Cognitum.One agentic architecture, running a supercharged Rust-based AI engine, embeddings, memory, and plugin system.


Open animated demo · Theme Studio guide · Workflow demo

Your agent cockpit. Track workflows, agents, models, tokens and cost beside Claude Code. The 38 second animation shows Orbital and Theme Studio through actual renderer frames, including boot checks, six palettes, customization, saved themes, JSON import and resizing. This compact 3.84 MB SVG retains all 456 frames. Native host and AI provider execution are not demonstrated.

Inspect any run. Open agent logs and results, search, triage failures, replay and compare. Start with npx ruflo init, restart Claude Code, then /ruflo. Full console tour.

Let Claude drive. You set the limits. Enable Settings → Claude control to navigate and run console actions. Choose read, write, manage or full, with ask or auto approval. The current default is read + ask; raising the level is your choice. Actions above your permission level are refused, and network, spending and destructive actions require confirmation even in auto. Review the live action log or press Take back control to stop Claude control. Details.

One task: create a mission with Claude

Watch the recorded console session below. Claude creates a mission, opens the Learning and Security pages, and reports its actions in Overview.

  1. Set the boundary. Install the console using the commands below, open /ruflo, then choose Settings → Claude control → write + ask.
  2. Give a concrete request. Try: “Create a mission to add a

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