agentrq/agentrq

Name: agentrq/agentrq
Author: agentrq

Desktop桌面端 ⭐ 1,137 AGPL-3.0 Models & Routing模型与路由 Clients & Launchers客户端与启动器 Productivity & Tasks效率与任务

AgentRQ：面向AI Agent的人机协同实时对话任务管理器。支持自托管！无论身在何处，都能通过移动端、网页端和桌面端掌控您的Agent。专为与您自己的Claude订阅及任意框架无缝协作而设计。

Project Overview项目介绍

Edited by Zhiwei Agent (zhiwei)由知微Agent(zhiwei)编辑

AgentRQ is a modern high-performance human-AI collaboration platform that uses the Model Context Protocol (MCP) to let AI agents interact directly with your workspace task management system. It officially provides integration extensions for three major AI agent clients: Claude Code, Gemini CLI, and DeepSeek Harness. The platform ships with core features including a visual task board, task scheduling, event-triggered workflows, tool call history tracking, on-device speech-to-text, and auto-title generation, all accessible from any device. It creates a shared workspace where humans and AI can work together seamlessly on complex goals.

The typical workflow starts when you break down complex projects into smaller, manageable tasks, then configure scheduling rules or event triggers via the drag-and-drop visual interface, and assign tasks to your connected AI agents. Because agents can see the full workspace state through MCP, they can autonomously pull assigned tasks, update progress, request approval for sensitive actions, and sync all changes in real time across the platform. It is designed for developers, product managers, content creators, and anyone who regularly collaborates with AI agents on long-term complex projects.

AgentRQ is released under the open-source Apache 2.0 license. All on-device features, including speech-to-text via browser-based Whisper and auto-title generation, process data locally so no user data leaves your local machine. To install the AgentRQ extension for DeepSeek Harness, you run a simple npx command to add the plugin, then configure one DSH profile per workspace to enable integration. The project welcomes community contributions via GitHub issues and written feature proposals.

AgentRQ是一个面向人机协作的现代高性能平台，基于Model Context Protocol（MCP）让AI agents直接对接工作区的任务管理系统，官方提供了对Claude Code、Gemini CLI和DeepSeek Harness三个主流AI客户端的集成扩展。它核心包含可视化任务看板、任务调度、事件流编排等功能，支持跨设备访问，让人和AI可以在共享工作空间协同推进复杂目标。

典型工作流是用户将复杂目标拆解为可管理的任务，通过可视化界面配置任务调度规则或工作流事件触发逻辑，再将任务分配给对应AI agent。AI可以通过MCP感知工作区状态，自动拉取分配给自己的任务、更新状态、申请敏感操作权限，所有同步都实时完成。这个平台适合经常需要和AI协作处理复杂长期项目的开发者、产品经理和内容创作者使用。

AgentRQ采用Apache 2.0开源许可，所有客户端侧的功能，比如语音转文字使用浏览器端Whisper模型，自动标题生成也在浏览器完成，数据不会离开本地设备。安装需要对应支持MCP的AI客户端，DSH下安装通过npx命令添加插件，每个DSH配置对应一个工作区，切换工作区只需切换配置文件。目前项目开放贡献，欢迎用户提交问题和功能提案。

View on GitHub在 GitHub 查看

Send to your DSH发给你的 DSH 安装

请帮我安装这个 DSH 插件。安装前先完成【兼容性检查 + 安全性检查】，检查通过再动手。

插件：agentrq（agentrq/agentrq）
仓库：https://github.com/agentrq/agentrq
本站详情页：https://www.yhbd.top/plugins/agentrq-agentrq/
本站登记：类型 client · 归类 多平台兼容工具（非 DSH 原生） · 许可证 AGPL-3.0 · ⭐ 1137 · 最近提交 2026-10-03 · 主语言 Go · 未检测到 DSH 插件清单

按下面顺序执行，每步先把结论告诉我，再进入下一步：

【1 兼容性检查】
① 我这边：DSH 版本、Node 版本、操作系统、当前 profile（web / desktop）。
② 读它的 README、package.json、插件 manifest，列出它要求的 DSH 版本 / Node 版本 / 操作系统 / 外部依赖 / 需要另外先装的运行时。
③ 逐条比对，结论只写「满足 / 不满足 / 未知」三种；不满足的给出可行替代方案。
④ 检查是否和我已装的插件冲突：命令名重复、skill / tool 重名、端口占用、重复注册的 MCP server。

【2 安全性检查】
① 仓库可信度：和上面「本站登记」是否一致；star / fork 数、创建时间、最近提交，是否归档或长期停更。
② 安装脚本：逐行看 package.json 的 preinstall / install / postinstall，以及 install.sh、setup.ps1 之类脚本。出现 curl|bash、下载后直接执行、混淆代码、访问与插件功能无关的域名，立刻停下来告诉我，不要继续装。
③ 依赖：列出新增依赖，标出无人维护、或与知名包拼写近似的可疑包（typosquatting）。
④ 权限与副作用：它会读写哪些目录、访问哪些域名、需要哪些 DSH 权限（filesystem / network / shell / clipboard 等），以及怎么卸载和回滚。
⑤ 如果它要求 sudo / 管理员权限，或权限明显超出功能所需，先停下来问我。

【3 安装】
上面两步没有「不满足」和「高危项」时才执行；用官方推荐方式安装，不要自行提权。

【4 汇报】
用表格输出：检查项 / 结论 / 依据 / 是否需要我决策。拿不准的一律写「未知」并说明要我怎么确认——不要猜，也不要替我决定。

Send this message to DSH in your current session: it verifies compatibility and security first (answering met / not met / unknown item by item) and only installs once everything checks out — it will stop and ask you if it finds a high-risk item. The box scrolls; the copy is the full prompt. CLI install commands may not be accurate across systems, so DSH is the safer route.把上面这条消息直接发给当前会话里的 DSH：它会先核对兼容性与安全性（逐条给「满足 / 不满足 / 未知」），确认没问题再安装，有高危项会停下来问你。框内可滚动，复制到的是完整提示词；安装命令不一定准确，发给 DSH 更稳。

Pre-install check安装前体检Compatibility · Security兼容性 · 安全性 2 warnings2 项注意

No DSH plugin manifest detected - it may only carry the dsh-plugin topic, so the install method must be confirmed on the spot未检测到 DSH 插件清单：可能只是打了 dsh-plugin 话题，安装方式要现场确认
Not DSH-native: a multi-platform tool that may require Node / Electron or another runtime first非 DSH 原生，是多平台兼容工具：可能要先装 Node / Electron 等运行时
Desktop client: installation downloads an executable - verify the publisher and checksums桌面客户端：安装会下载可执行文件，请核对发布者与校验和

DSH walks through these 9 checksDSH 会逐条核对这 9 项

Compatibility兼容性

DSH, Node, OS and profile requirementsDSH 版本 / Node 版本 / 操作系统 / profile 是否满足要求
External dependencies and runtimes (Electron / Python / Docker, ...)外部依赖与运行时（Electron / Python / Docker 等）是否齐备
Conflicts with installed plugins: command names, skill / tool names, ports, duplicate MCP registration与已装插件是否冲突：命令名、skill / tool 重名、端口占用、重复 MCP 注册

Security安全性

Repo matches the facts registered here; archived or abandoned?仓库是否与页面登记一致，是否归档或长期停更
Safety of preinstall / install / postinstall and install.sh / setup.ps1preinstall / install / postinstall 与 install.sh、setup.ps1 是否安全
curl|bash, download-then-execute, obfuscation, unrelated domains → stop immediatelycurl|bash、下载即执行、混淆代码、无关域名 → 立刻停止
Typosquatting or unmaintained packages among the new dependencies新增依赖里有没有 typosquatting 或无人维护的包
Requested permissions vs. what the feature actually needs申请了哪些权限、是否超出功能所需（filesystem / network / shell / clipboard）
Any sudo / admin requirement, plus uninstall and rollback是否要求 sudo / 管理员权限，以及卸载与回滚方式

Anything uncertain must be marked unknown with a note on how to confirm it. This site's signal screen is a static snapshot, not a security audit.拿不准的必须标「未知」并说明要我怎么确认。本站的信号筛查是静态快照，不能替代安全审计。

Or use CLI install (for developers)或使用命令行安装（适合开发者）

CLI Install命令行安装

npx @deepseek-ai/dsh plugin --profile web add @agentrq/dsh-plugin-agentrq

把 agentrq/agentrq 加入你的 DSH 配置（web profile）即可启用。

READMEREADME

35.4 KB · 缓存于 2026-10-03 22:09:00 · 35.4 KB

AgentRQ ── Agent-Human Collaboration Platform

简体中文

AgentRQ is a modern, high-performance platform designed for seamless collaboration between human operators and AI agents. It leverages the Model Context Protocol (MCP) to allow AI models (like Claude) to interact directly with your workspace's task management system.

🚀 Overview

Think of AgentRQ as a shared workspace where humans and AI agents work together seamlessly. You can break down complex goals into manageable tasks, and delegate work directly to your AI agents.

Because agents "see" the workspace state via MCP, they can autonomously pull their assigned tasks, update statuses, request permissions for sensitive actions, and communicate with you—all synchronized instantly across the platform in real-time.

✨ Features

Real captures from the running app — no mockups.

Visual Task Board

Every task Claude creates appears instantly on your board. See what it's working on, what it needs, and what it just finished — all from a clean, fast dashboard you can open on any device, as a list or a Kanban.

AgentRQ active tasks dashboard and kanban board

AgentRQ scheduled task auto-spawning into the dashboard

Task Scheduling

Give any task a launch date, or a recurring cadence — every 15 minutes, hourly, daily, weekly, custom days. A background poller ticks every minute and spawns the task the instant it's due, no server or agent needing to stay awake and wait.

Showing the opening section of the README — the full document lives in the repository以上为 README 开头摘要，完整文档在仓库内 · View the full README on GitHub →在 GitHub 查看完整 README →

渲染基于仓库 agentrq/agentrq 的 README.md。在 GitHub 查看原文 →