agentrq

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.

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.

Events

Events are named signals — qa_passed, deploy_finished, blog_published — that any task can fire when it completes. Wire one to a workspace and that workspace gets a new task automatically, no polling and no glue code.

Workflows

A Workflow is Events and workspaces arranged on a graph. Drag a workspace onto an event to subscribe it; drag an event onto a workspace to emit it on completion. No decision-tree DSL, no YAML — just the shape of your release process, visible.

Tool Call History

The task detail view's History tab lays out a lane-grouped timeline of every tool call and message in a run — Input, Agent, and Tools. Search it, click into any entry, and see exactly what ran, what it returned, and whether it was allowed or denied.

Auto-Title Generation

Write your task description, click the sparkle, and a small language model — downloaded once and cached by your browser — reads it and writes the title. No API call, no server, no data leaving your machine.

Speech-to-Text

Click the mic on any task description or reply and dictate it instead. Transcription runs on an in-browser Whisper model — your voice is processed on-device and never uploaded anywhere.

Message Send Delay

Give a workspace a countdown — 3s, 5s, 10s, 15s, 30s or 60s — and every chat message waits that long in the thread before it reaches the agent. Send Now delivers it early, Cancel pulls it back unsent and puts the exact text and attachments back in your composer. Off by default, per workspace.

Search & Keyboard Shortcuts

⌘K (Ctrl+K off macOS) opens a task finder that matches any word in a title or description, straight from the copy your device already saved — so it answers offline, and tells you how far it looked. Everything else is a bare letter: N for a new task, W and M to jump to a workspace or a machine, P for the Pano, C and T to flip between a task's chat and its trajectory, ? for the list. Nothing to configure, and nothing to memorise.

Machines

Install agentrqd on a computer you own, enrol it once with a single-use code typed on the machine itself, and it becomes somewhere agents can run — in your repositories, with your toolchain. Pick a workspace and what to run, and it starts in that workspace's folder on that machine. Each machine's page shows what it has left: CPU, memory, uptime, load and free space per filesystem.

Live Terminals

Open a running session from any browser and you are at the prompt. Keystrokes go straight through as the bytes your keys produced — Esc and Ctrl-C included — resizing reflows the program on the far end, and the session keeps running whether or not anybody is watching. Drop the network and the screen is still there when you come back.

Skills

Give a workspace's agents the playbooks your team already follows. A skill is a SKILL.md plus the files it points to, in the same format Claude Code uses. Paste a public GitHub link, such as obra/superpowers, and every skill in it is imported. Write your own, or let an agent save one. Skills belong to your account: turn each one on in the workspaces that need it, and they all read the same copy, so an edit reaches every one of them. Every agent that connects to the workspace is told to search its skills at the start of a task and load the ones that match, over MCP, whatever harness it runs in. How skills work →

Pano

A board of markdown docs that every workspace of your account shares. An agent posts a plan, a report or a chart, and hands its id to the agents in other workspaces that need it. They read it, comment on it and attach files. You see every doc as a post-it, edit it, comment on it, or write your own and turn it into a task. Each doc lives 30 days from its last save unless the agent asks for longer. How the Pano works →

Workspace Forks

Fork a workspace to have a second agent take some of its tasks in parallel. Right-click it in the sidebar to fork it, or choose Spin up on a task, which forks the workspace, moves the task in and launches an agent in one step. The fork has its own queue and agent. It shares the parent's memory, skills and settings, and runs in its own git worktree or copy of the parent's folder. When its tasks are done, merge it back: its agent is stopped and every task returns to the parent with its thread. How forks work →

See the full list at agentrq.com/features.

🏛 Architecture

AgentRQ follows a decoupled service-oriented architecture:

Backend (Go / Fiber)

• API Server: Fiber-based REST API for workspace and task management.
• MCP Server: Integrated mcp-go SSE server that exposes tools and resources to AI models.
• CoreMCP (Supervisor): A global MCP server that allows agents to manage all workspaces, tasks, and statistics across the entire platform.
• Data Layer: GORM with SQLite for persistent, user-scoped storage.
• Authentication: Google OAuth2 integration with JWT-based session management.
• Event Bus: Internal pub/sub system for real-time SSE notifications.

Frontend (Vue.js 3 / Vite)

• Modern UI: Tailored with Vue 3, Pinia, and Tailwind CSS.
• Glassmorphism: A sleek, premium design language with smooth transitions and real-time updates.
• Reactive State: Synchronized with the backend via SSE events.

Desktop (Electron)

• Same application, native shell: the desktop app renders the *same* Vue components as the browser, so the two never diverge.
• Native notifications: agent activity reaches you while the window is in the background, with a dock or taskbar badge.
• Tray, global shortcut, deep links: Cmd/Ctrl+Shift+N from anywhere, and agentrq:// URLs that open the app at a specific task.
• Auto-updating: checks in the background and installs on restart.

💻 Desktop App

AgentRQ has a desktop app for macOS, Windows and Linux. It is a client — it connects to whichever AgentRQ server you run.

On macOS and Linux, one command installs it — and updates it later:

curl -fsSL https://agentrq.com/install.sh | sh -s -- --quit

Or download the latest release →

Platform · Download macOS · .dmg — Apple silicon and Intel Windows · .exe installer — x64 and arm64 Linux · .AppImage or .deb — x64 and arm64

Builds are currently unsigned, so a hand-downloaded build warns on first launch on macOS and Windows, and macOS cannot auto-update until signing certificates are in place — the install command above is the way around both. Connecting to a server and troubleshooting are covered in the Desktop Guide.

Extending the desktop app

Extensions add pages, actions, keyboard shortcuts and scheduled work. They are ordinary Node modules, discovered from GitHub repositories carrying the agentrq-extension topic, and installed from the desktop app.

Extensions are desktop-only, and deliberately so. An extension is code somebody else wrote, running with the privileges of the process it is in. On a self-hosted server that would mean a stranger's code next to your database and your other users; on the desktop it runs on the machine of the person who chose to install it. The server never loads extension code.

What AgentRQ does enforce is everything it owns: which surfaces an extension can contribute to, and which MCP tools it may call against which workspaces — the extension never holds a credential, it asks, and the app attaches the token on the way out. That is a real boundary around your AgentRQ data. It is not a sandbox around your machine, and the install screen says so on every install.

Three worked examples live in examples/extensions/, from one that asks for no permissions at all to one that runs a daily digest across every workspace. See the Extensions Guide.

🧭 Chrome Extension

Add AgentRQ to Chrome →

AgentRQ lives in your browser's toolbar: open your workspaces in a popup, or full size in a tab.

• Bridge any WebMCP site into your agents. When a website offers tools

through WebMCP, the toolbar icon lights up; share the site with a workspace and its agents can call those tools in your own signed-in Chrome. A tool the site does not mark read-only waits for your approval in the task first.

• Your agents' terminals, right inside your browser. Open the live terminal

of a Claude Code, Codex or Antigravity session running on one of your machines and work in it without leaving the tab you are in.

Installing from source and self-hosted servers are covered in the extension's README.

Driving AgentRQ from a browser agent

If your browser supports WebMCP, an AI agent you talk to there can use AgentRQ directly — list your workspaces, open a task, reply in it, build a workflow. Everything the interface can do is offered as a tool, including asking which page you are on, so "reply to this task" resolves to the task you have open.

The tools run in the page as you, with your session, so an agent gets exactly your permissions and nothing more, and they are withdrawn when you sign out. Nothing to install or configure; a browser without WebMCP simply sees no tools. See the WebMCP Guide.

Driving AgentRQ from the command line

Inside a workspace directory — one with the .mcp.json an agent works from — the same capabilities are a shell command away:

npx -y @agentrq/agentrq-ws@latest help

It reads that .mcp.json, so there is nothing to configure, and it covers every workspace tool: read and create tasks, reply, publish events, read and write the workspace memory, ask a human a question. Attachments are plain file paths in both directions — --attach ./run.log to send one, and a download writes the file and prints where it went. See cli/agentrq-ws.

🖥️ Your own machines

Install agentrqd on a computer, enrol it once, and you can start an agent for a workspace on it from the control panel — then watch its terminal and type into it, Esc included. The machines page shows what each box has left: memory, CPU and free space per filesystem, so you can tell whether it can take another agent.

On Linux and macOS:

curl -fsSL https://agentrq.com/install-agentrqd.sh | sh

On Windows (PowerShell):

irm https://agentrq.com/install-agentrqd.ps1 | iex

Either one picks the right build, verifies it against the checksums published with the release, and puts it on your PATH; running it again updates in place. It installs only — enrolling stays a separate, deliberate step, and it never runs as root or Administrator. Manual installs are on the — one static binary for Linux, macOS and Windows.

Then Machines → Add machine gives you a code to enrol it with.