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, M 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.

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.

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 releases page — one static binary for Linux, macOS and Windows.

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

Enrolling a machine is a real grant, and the Daemon Guide says so plainly: it lets anyone who can authenticate as that account run commands on the machine as the user who started the daemon. Read it before you enrol anything. It also covers the local kill switch, which works without the server's cooperation, and what the audit trail records — starts, kills and attaches, never keystrokes.

⌨️ Agent slash commands

Agents connected through the ACP gateway advertise commands of their own — /init, /compact, /review, whatever they ship with. Type / in a task and they appear above the reply box, filtered as you type and chosen with the keyboard or the mouse. The list comes from the agent and follows it live, so it changes as the agent's context does. An agent that advertises none gets no menu and nothing changes. See the Slash Commands Guide.

🧠 Choosing the agent's model

Where an agent offers a choice of model, you can make it from the interface — on a workspace card, and on the form where a task is written, so the model is settled before the work starts rather than after. Choosing shows the new model straight away but marks it as asked-for until the agent itself confirms; if it refuses, or never answers, the interface goes back to what is actually running and says so.

The choice appears only where it would do something. An agent that reports no models — Claude Code connected directly, among others — shows none, and neither does an ACP gateway older than the release that learned to switch on request: it says which model it is on without claiming it can change it. Nothing to configure either way.

To run it from source:

make install       # dependencies for the whole repo
make desktop-dev   # run the desktop app against a local server
make desktop       # build installers into desktop/release/

🛠 Getting Started

Prerequisites

• Go 1.21+
• Node.js 18+ (with npm)
• Google Cloud Console: An OAuth2 Client ID and Secret.

Configuration

1. Create a _config/base.yaml (or development.yaml) in the backend directory.
2. Fill in your Google OAuth2 credentials:

auth:
  google:
    client_id: "your-google-client-id"
    client_secret: "your-google-client-secret"

Running Locally

Use the provided Makefile to start the full stack:

# 1. Install all dependencies
make install

# 2. Start both Frontend and Backend
make dev

The frontend will be available at http://localhost:5173. For the desktop app, run make desktop-dev in another terminal — see the Desktop Guide.

Self-Hosting (Docker)

For running the production or development stack using the pre-built Docker image, see the Self-Hosting Setup Guide.

[!NOTE] Agents / AI Assistants: If you need to set up, configure, run, or diagnose a local self-hosted instance of AgentRQ using Docker, refer to SETUP.md for step-by-step instructions, Docker run commands, and environment variable configurations.

🤖 Claude Code & AI Integration

AgentRQ is designed for seamless integration as a Claude Channel. This allows your AI agents to see tasks assigned to them and respond directly within your Claude session.

Each workspace has its own MCP URL and token (visible in the workspace setup modal). In production, these follow the pattern https://WORKSPACE_ID.mcp.agentrq.com/.

Step 1 — .mcp.json

Create a .mcp.json file in your local project directory (the leading dot is required). Each project gets its own file so Claude instances stay isolated per workspace. Replace YOUR_MCP_URL below with the full URL shown in the setup modal (e.g. https://WORKSPACE_ID.mcp.agentrq.com/?token=TOKEN).

{
  "mcpServers": {
    "agentrq-WORKSPACE_ID": {
      "type": "http",
      "url": "YOUR_MCP_URL"
    }
  }
}

Step 2 — .claude/settings.local.json

Add a .claude/settings.local.json file in the same project directory to pre-approve the AgentRQ tools and avoid permission prompts on every action. The wildcard covers every tool the workspace exposes, including any added later:

{
  "permissions": {
    "allow": ["mcp__agentrq-WORKSPACE_ID__*"]
  },
  "enableAllProjectMcpServers": true,
  "enabledMcpjsonServers": ["agentrq-WORKSPACE_ID"]
}

Step 3 — Start Claude

Once both files are in place, launch Claude Code from that project directory:

claude --dangerously-load-development-channels server:agentrq-WORKSPACE_ID

Tip: The workspace ID, full MCP URL (with token), and ready-to-paste config snippets are all available in the Setup modal inside each AgentRQ workspace.

Available MCP Tools

When connected, the AI agent has access to:

• createTask: Assign a task to the human user (supports optional cron_schedule for recurring tasks).
• updateTaskStatus: Move tasks through notstarted, ongoing, blocked, and completed.
• reply: Send messages back to the AgentRQ dashboard in real-time.
• getWorkspace: Fetch the workspace name, mission description, and task statistics.
• getTask: Fetch a task — with no taskId it dequeues the next "not started" task assigned to the agent; with a taskId it returns that task. Pass includeConversation: true to also include the chat history (cursor-based pagination).
• downloadAttachment: Retrieve an attachment by its ID.
• publishEvent: Fire a named event so subscriber workspaces spawn their trigger tasks.
• loadMemory: Read the workspace's notes — with no name it reads memory.md, the index of everything remembered here.
• saveMemory: Write a note that outlives the task, so the next agent starts with it.
• deleteMemory: Remove one of the workspace's notes.
• searchSkills: Find the skills the workspace can use — its own and those shared into it — with each one's description and skill:// URI. Optional q (at least 3 characters) matches name or description; optional limit/offset page through the results.
• loadSkill: Read one file of a skill by its skill://<name>/<path> URI; a SKILL.md comes with the URIs of the skill's other files.
• saveSkill: Write one file of one of the workspace's own skills. Writing SKILL.md creates or updates the skill.
• deleteSkill: Delete one of the workspace's own skills, or one of its files.
• elicit: Ask the human a question and block until they answer, either as a form or as a link to confirm.
• Real-time Notifications: Agents receive notifications via the notifications/claude/channel protocol whenever a human interacts with their tasks.

Skills

Skills are SKILL.md playbooks that agents load when a task matches one. Each workspace has its own, can import them from a public GitHub repository such as obra/superpowers, and can share them with the account's other workspaces. See docs/SKILLS.md for the format, limits, importing, sharing and the skill:// scheme.

🌉 ACP Gateway (Bridge for ACP Agents)

While Claude Code has native support for claude/notifications, other agents like Antigravity and Codex require a bridge to receive real-time task notifications from AgentRQ. The @agentrq/acp-gateway bridges the Agent Client Protocol (ACP) with MCP to enable this.

There is nothing to install — npx fetches the gateway, and the gateway fetches the agent you name.

Usage

1. Ensure you have a .mcp.json in your project root.
2. Log in to your agent once, then start the gateway from the same directory as .mcp.json:

# Using Antigravity
npx -y @agentrq/acp-gateway@latest --login --agent antigravity-acp --allow-unverified-agent
npx -y @agentrq/acp-gateway@latest --agent antigravity-acp --allow-unverified-agent

# Using Codex
npx -y @agentrq/acp-gateway@latest --login --agent codex-acp
npx -y @agentrq/acp-gateway@latest --agent codex-acp

Antigravity is published as a binary the registry carries no checksum for, so it needs --allow-unverified-agent on every command; Codex ships as an npm package and does not. Sign out again with --logout in place of --login.

The gateway will automatically:

• Connect to your AgentRQ workspace via the URL in .mcp.json.
• Spawn the agent subprocess and bridge standard I/O.
• Forward task assignments, messages, and permission requests in real-time.

🌌 Codex (via the ACP Gateway)

OpenAI Codex connects through the same ACP Gateway as every other agent. The gateway resolves codex-acp from the ACP registry and runs it for you, so there is nothing to install and nothing to configure beyond the .mcp.json the gateway reads.

Earlier releases used a separate @agentrq/codex-gateway package and a .codex/config.toml. Neither is needed now.

Setup

1. Ensure you have a .mcp.json in your project root.
2. Log in — Codex will not open a session until you have. The first run fetches the agent, then hands you its login:

npx -y @agentrq/acp-gateway@latest --login --agent codex-acp

3. Start the bridge. Run it from the same directory as .mcp.json:

npx -y @agentrq/acp-gateway@latest --agent codex-acp

Sign out again with --logout in place of --login. The registry publishes Codex as an npm package, so npx fetches it on first use and keeps it current — unlike the binary agents, it needs no --allow-unverified-agent.

👑 Supervisor (CoreMCP)

While individual workspaces provide a scoped view for specific projects, the Supervisor (CoreMCP) is a global MCP server that grants an agent bird's-eye view and management capabilities across your entire AgentRQ account.

The Supervisor is accessible at https://mcp.agentrq.com/mcp. It uses OAuth2 for secure authentication, allowing modern AI tools (like Claude Code) to connect securely.

Why use the Supervisor?

• Multi-Workspace Management: List, create, and update workspaces.
• Global Task View: Fetch tasks from all workspaces in a single call (listAllTasks).
• Administrative Control: Manage task assignments, status, and priorities globally.
• Unified Statistics: Access detailed statistics and health metrics for any workspace.

Available Supervisor Tools

The Supervisor provides a comprehensive suite of tools for global management, requiring workspaceId parameters where applicable:

Workspace Management

• listWorkspaces: Overview of all active and archived workspaces.
• createWorkspace: Bootstrap new project environments.
• getWorkspace: Retrieve details of a specific workspace by ID.
• updateWorkspace: Modify workspace settings and metadata.
• getWorkspaceStats: Retrieve high-level analytics and performance data for a workspace.

Task Management

• listAllTasks: Search and filter tasks across the entire platform.
• listTasks: List tasks within a specific workspace.
• createTask: Create a new task in a specific workspace.
• getTask: Retrieve details of a specific task.
• updateTaskStatus: Change a task's status.
• updateTaskOrder: Reorder a task in the list.
• updateTaskAssignee: Change the assignee of a task.
• updateTaskAllowAll: Toggle allow_all_commands permission for a task.
• updateScheduledTask: Modify a scheduled/cron task.
• deleteTask: Delete a task with its messages and attachments — the way to retire a schedule rather than leave it running.

Communication & Files

• replyToTask: Post a message to a task's chat thread.
• respondToTask: Submit an allow/deny verdict for a permission request.
• getAttachment: Retrieve data as base64 and metadata for a specific attachment.

Workspace Memory

• listMemories: List a workspace's memories — name, size and when each changed.
• getMemory: Read one memory in full. MEMORY.md is the index the others hang off.

Workspace Skills

• searchSkills: Find the skills a workspace can use, its own and those shared into it, by name or description (q, at least 3 characters), with limit/offset paging and a total. Content is not included.
• getSkill: Read one file of a skill by its skill://<name>/<path> URI; skill://<name> alone reads its SKILL.md.

Machine Setup

• createEnrolmentCode: Mint a one-time code for enrolling a new machine with agentrqd. Shown once and expires shortly — there is no remote enrolment, so it hands back a ready-to-run command rather than acting on the machine itself.

Events & Triggers An event is a named signal a workspace publishes; a trigger creates a task somewhere when it fires. Publishing stays agent-side (publishEvent on the per-workspace server) — the supervisor builds the wiring, the workers fire it.

• listEvents, createEvent, getEvent, updateEvent, deleteEvent: define the signals.
• createEventTrigger, listEventTriggers, getEventTrigger, updateEventTrigger, deleteEventTrigger: decide what each one causes.
• listEventTasks: see the tasks an event has spawned.

Workflows The graph those pieces add up to: a start event, and the steps that react to it and to each other.

• listWorkflows, createWorkflow, getWorkflow, updateWorkflow, deleteWorkflow: the graph itself.
• createWorkflowStep, listWorkflowSteps, deleteWorkflowStep: its nodes, one at a time.
• getWorkflowText, replaceWorkflowFromText: the whole graph as the indented document the UI's text mode edits — the declarative way to write one.
• listWorkflowTasks: see the tasks a workflow has spawned.

Supervisor Resources & Prompts

Beyond tools, the Supervisor also exposes MCP resources — read-only reference material an agent can pull into its own context — and MCP prompts — ready-made templates for the workflows a "single brain overseeing many workspaces" is for.

Resources:

• agentrq://guides/new-workspace: how to set up a new workspace end-to-end.
• agentrq://guides/agentrqd-setup: how to install agentrqd and enrol a new machine, with the enrol command templated to this server.

Prompts:

• new-workspace: scaffold a new workspace for a stated purpose.
• setup-agentrqd: mint an enrolment code and hand back the exact commands to run on a new machine.
• workspace-status: a status report across every workspace at once.

Connecting to Supervisor (Claude Code)

Since the Supervisor uses OAuth2, you can connect it using the following configuration in your ~/.mcp.json:

{
  "mcpServers": {
    "agentrq": {
      "type": "http",
      "url": "https://mcp.agentrq.com/mcp"
    }
  }
}

When you first run Claude with this server, it will provide a link to authenticate via your browser.

🧩 Official Extensions

AgentRQ provides official extensions for major AI agent CLI tools to simplify setup and integration with its supervisor MCP. The sub agents MCPs should use their own workspace specific MCP server URLs.

🍊 Claude Code

Two plugins for Claude Code are published from this repository's own marketplace, each with a skill and pre-configured MCP access:

• agentrq — the supervisor, talking to the account-level MCP server so one agent can orchestrate work across every workspace you own.
• agentrq-workspace — the workspace agent, connected to a single workspace's MCP server to work its queue.

Installation:

/plugin marketplace add https://github.com/agentrq/agentrq
/plugin install agentrq@agentrq
/plugin install agentrq-workspace@agentrq

Previously these lived in a separate agentrq-claude-extension repository. The marketplace URL is now this repository; if you added the old one, re-add the marketplace at the URL above.

♊ Gemini CLI

The Gemini CLI extension allows you to manage AgentRQ workspaces and tasks directly from your terminal using Google's Gemini models.

Tip: To enable real-time task notifications with Gemini, use the ACP Gateway.

Installation:

gemini extensions install https://github.com/agentrq/agentrq-gemini-extension

🐋 DeepSeek Harness

The @agentrq/dsh-plugin-agentrq bundle brings AgentRQ into DeepSeek Harness. It bridges the workspace's tools to the model as mcp__agentrq__* and holds a supervised workspace session, so tasks assigned to the agent and the human's replies arrive over the MCP channel and land in the live session — no polling, and no leaving the harness to work the queue.

Installation:

npx @deepseek-ai/dsh plugin --profile agentrq-<workspace> add @agentrq/dsh-plugin-agentrq
# pin this workspace's MCP URL in ~/.dsh/profiles/agentrq-<workspace>/cordis.patch.yml
npx @deepseek-ai/dsh --profile agentrq-<workspace>

Copy the filled-in commands and config block from Workspace Settings → Setup → DeepSeek Harness. A dsh profile serves one workspace and carries its own endpoint, so run one profile per workspace and switching workspaces is switching profiles. Delivery, startup catch-up, and reconnect behavior are configurable; see the plugin README.

🔌 Integrations

Slack Integration

AgentRQ supports multi-tenant Slack integration for real-time task creation, thread replies sync, and agent permission requests:

• Slack Integration Setup & Usage Guide

🙌 Contributing

Bug reports go in a GitHub issue and feature or architecture ideas go in a short written proposal — see CONTRIBUTING.md.

🤝 Credits

• AgentRQ — The official Agent-Human collaboration platform.
• HasMCP — Bridge the Gap Between APIs and Agents.

📝 License

Apache-2.0