经 AI Skill Hub 精选评估,AgentRQ 获评「推荐使用」。这款MCP工具在功能完整性、社区活跃度和易用性方面表现出色,AI 评分 7.5 分,适合有一定技术背景的用户使用。
AgentRQ 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
AgentRQ 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
# 方式一:通过 Claude Code CLI 一键安装
claude skill install https://github.com/agentrq/agentrq
# 方式二:手动配置 claude_desktop_config.json
{
"mcpServers": {
"agentrq": {
"command": "npx",
"args": ["-y", "agentrq"]
}
}
}
# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
# 安装后在 Claude 对话中直接使用 # 示例: 用户: 请帮我用 AgentRQ 执行以下任务... Claude: [自动调用 AgentRQ MCP 工具处理请求] # 查看可用工具列表 # 在 Claude 中输入:"列出所有可用的 MCP 工具"
// claude_desktop_config.json 配置示例
{
"mcpServers": {
"agentrq": {
"command": "npx",
"args": ["-y", "agentrq"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
<p align="center"> <video src="demos/demo.mp4" width="480" controls="controls"></video> <br /> <a href="https://www.youtube.com/watch?v=GBAoSpuCzrU">Watch on YouTube in HD</a> </p>
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.
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.
make install
npm install -g @agentrq/acp-gateway
npm install -g @agentrq/codex-gateway@latest
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.jsonis used bycodex-gatewayto receive tasks..codex/config.tomlis used by the Codex agent itself to call agentrq tools (e.g.reply,updateTaskStatus) during execution.
.mcp.json in your project root.```bash
Run codex-gateway from your agentrq workspace root (the directory containing .mcp.json):
```bash
_config/base.yaml (or development.yaml) in the backend directory.auth:
google:
client_id: "your-google-client-id"
client_secret: "your-google-client-secret"
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"]
}
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/.
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.
AgentRQ supports multi-tenant Slack integration for real-time task creation, thread replies sync, and agent permission requests: - Slack Integration Setup & Usage Guide
AgentRQ是一个有潜力的MCP工具,支持人机协同
该工具未明确声明开源协议,商业使用前请联系原作者确认授权范围,避免侵权风险。
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
AI Skill Hub 点评:AgentRQ 的核心功能完整,质量良好。对于Claude Desktop / Claude Code 用户来说,这是一个值得纳入个人工具库的选择。建议先在非生产环境试用,再逐步推广。
| 原始名称 | agentrq |
| 原始描述 | 开源MCP工具:AgentRQ: Human-in-loop realtime conversational task manager for AI Agents.。⭐31 · Go |
| Topics | mcpacp-clientacp-gatewayagentic-aiagentic-workflowagentsgo |
| GitHub | https://github.com/agentrq/agentrq |
| 语言 | Go |
收录时间:2026-05-25 · 更新时间:2026-05-30 · License:未公布 · AI Skill Hub 不对第三方内容的准确性作法律背书。
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