AgentRQ ── Agent-Human Collaboration Platform

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.

🏛 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.

🛠 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.

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:

{
  "permissions": {
    "allow": [
      "mcp__agentrq-WORKSPACE_ID__updateTaskStatus",
      "mcp__agentrq-WORKSPACE_ID__getWorkspace",
      "mcp__agentrq-WORKSPACE_ID__reply",
      "mcp__agentrq-WORKSPACE_ID__createTask",
      "mcp__agentrq-WORKSPACE_ID__downloadAttachment",
      "mcp__agentrq-WORKSPACE_ID__getTaskMessages",
      "mcp__agentrq-WORKSPACE_ID__getNextTask"
    ]
  },
  "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.
• getTaskMessages: Read the chat history of a task with cursor-based pagination.
• getNextTask: Efficiently retrieve the next "not started" task assigned to the agent.
• downloadAttachment: Retrieve an attachment by its ID.
• Real-time Notifications: Agents receive notifications via the notifications/claude/channel protocol whenever a human interacts with their tasks.

🌉 ACP Gateway (Bridge for ACP Agents)

While Claude Code has native support for claude/notifications, other agents like Gemini CLI 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.

Installation

npm install -g @agentrq/acp-gateway

Usage

1. Ensure you have a .mcp.json in your project root.
2. Run the gateway followed by your agent's ACP command:

# Using Gemini CLI
acp-gateway -- gemini --acp

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 Gateway (Bridge for OpenAI Codex)

Similar to the ACP Gateway, the @agentrq/codex-gateway connects OpenAI Codex to AgentRQ workspaces by bridging the Model Context Protocol (MCP) with the Codex app-server protocol.

Installation

npm install -g @agentrq/codex-gateway@latest

Setup

1. Configure agentrq MCP server for Codex (project-level)

Codex reads project-level MCP server config from .codex/config.toml. Create this file so the Codex agent can use agentrq tools directly during task execution (replace <WORKSPACEID> and <TOKEN> with your values from the agentrq dashboard):

mkdir -p .codex
cat >> .codex/config.toml << 'EOF'

[mcp_servers.agentrq-workspace]
url = "https://<WORKSPACEID>.mcp.agentrq.com/?token=<TOKEN>"

[mcp_servers.agentrq-<ID>.tools.updateTaskStatus]
approval_mode = "approve"

[mcp_servers.agentrq-<ID>.tools.getWorkspace]
approval_mode = "approve"

[mcp_servers.agentrq-<ID>.tools.reply]
approval_mode = "approve"

[mcp_servers.agentrq-<ID>.tools.createTask]
approval_mode = "approve"

[mcp_servers.agentrq-<ID>.tools.downloadAttachment]
approval_mode = "approve"

[mcp_servers.agentrq-<ID>.tools.getTaskMessages]
approval_mode = "approve"

[mcp_servers.agentrq-<ID>.tools.getNextTask]
approval_mode = "approve"
EOF

2. Configure the gateway's agentrq connection

Create a .mcp.json in your project root so codex-gateway can connect to the same agentrq workspace:

{
  "mcpServers": {
    "agentrq": {
      "type": "http",
      "url": "https://<WORKSPACEID>.mcp.agentrq.com/mcp?token=<TOKEN>"
    }
  }
}

Note: .mcp.json is used by codex-gateway to receive tasks. .codex/config.toml is used by the Codex agent itself to call agentrq tools (e.g. reply, updateTaskStatus) during execution.

Usage

Run codex-gateway from your agentrq workspace root (the directory containing .mcp.json):

# Default: runs `codex app-server`
codex-gateway

# Custom codex command
codex-gateway -- codex app-server

👑 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 wo