能力标签
⚙️
Agent工作流

DashClaw

基于 JavaScript · 无代码搭建完整 AI 自动化流程
⭐ 269 Stars 🍴 49 Forks 💻 JavaScript 📄 MIT 🏷 AI 7.5分
7.5AI 综合评分
ai-agentsagent-frameworkjavascript
✦ AI Skill Hub 推荐

DashClaw 是 AI Skill Hub 本期精选Agent工作流之一。综合评分 7.5 分,整体质量较高。我们推荐使用将其纳入你的 AI 工具库,帮助提升工作效率。

📚 深度解析

DashClaw 是一套完整的 AI Agent 自动化工作流方案。随着 AI 能力的不断提升,基于 Agent 的自动化工作流正在成为提升个人和团队效率的核心方式。区别于传统的 RPA 自动化(模拟鼠标键盘操作),AI Agent 工作流通过理解任务意图、动态规划执行路径,能够处理更复杂的非结构化任务。

DashClaw 工作流的设计遵循"最小配置,最大复用"原则:核心逻辑已经封装好,用户只需配置自己的 API Key 和业务参数即可快速上手。工作流内置错误处理和重试机制,在网络波动或 API 限速等情况下仍能稳定运行,适合作为生产环境的自动化基础设施。

在实际部署时,建议先在测试环境中运行 3-5 次,验证各个环节的输出结果符合预期,再部署到生产环境。AI Skill Hub 评分 7.5 分,是同类 Agent 工作流中的精选推荐。

📋 工具概览

DashClaw 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。

GitHub Stars
⭐ 269
开发语言
JavaScript
支持平台
Windows / macOS / Linux
维护状态
轻量级项目,按需更新
开源协议
MIT
AI 综合评分
7.5 分
工具类型
Agent工作流
Forks
49

📖 中文文档

以下内容由 AI Skill Hub 根据项目信息自动整理,如需查看完整原始文档请访问底部「原始来源」。

DashClaw 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。

📌 核心特色
  • 可视化 Agent 工作流编排,无需编写复杂代码
  • 支持多步骤自动化任务链,实现全流程无人值守
  • 与外部 API、数据库和第三方服务无缝集成
  • 内置错误处理与自动重试机制,保障稳定运行
  • 提供可复用的自动化模板,快速在同类场景部署
🎯 主要使用场景
  • 自动化日常重复性工作,将精力集中于创造性任务
  • 构建数据采集 → 处理 → 输出的完整自动化管线
  • 实现跨平台、跨系统的数据流转和业务协同
以下安装命令基于项目开发语言和类型自动生成,实际以官方 README 为准。
安装命令
# 方式一:npm 全局安装
npm install -g dashclaw

# 方式二:npx 直接运行(无需安装)
npx dashclaw --help

# 方式三:项目依赖安装
npm install dashclaw

# 方式四:从源码运行
git clone https://github.com/ucsandman/DashClaw
cd DashClaw
npm install
npm start
📋 安装步骤说明
  1. 访问 GitHub 仓库获取工作流文件
  2. 在对应平台(Dify / Flowise / Make 等)中找到「导入工作流」功能
  3. 上传工作流文件
  4. 按照提示配置必要的环境变量和 API Key
  5. 运行测试确认流程正常后投入使用
以下用法示例由 AI Skill Hub 整理,涵盖最常见的使用场景。
常用命令 / 代码示例
# 命令行使用
dashclaw --help

# 基本用法
dashclaw [options] <input>

# Node.js 代码中使用
const dashclaw = require('dashclaw');

const result = await dashclaw.run(options);
console.log(result);
以下配置示例基于典型使用场景生成,具体参数请参照官方文档调整。
配置示例
# dashclaw 配置说明
# 查看配置选项
dashclaw --config-example > config.yml

# 常见配置项
# output_dir: ./output
# log_level: info
# workers: 4

# 环境变量(覆盖配置文件)
export DASHCLAW_CONFIG="/path/to/config.yml"
📑 README 深度解析 真实文档 完整度 69/100 查看 GitHub 原文 →
以下内容由系统直接从 GitHub README 解析整理,保留代码块、表格与列表结构。

简介

DashClaw

DashClaw

<p><strong>Govern AI agents before they act.</strong></p>

<p> DashClaw is the governance layer for AI agents that touch real systems. It sits between agents and the world, evaluates policy on every risky action, routes human approval where it is required, records verifiable evidence, and tracks terminal outcomes so a retried agent never silently double-executes. </p>

<p><sub>Plugs into the agents you already run: Claude Code, Codex, Hermes Agent, OpenClaw, Claude Desktop, and Claude Managed Agents. Framework integrations for LangChain, CrewAI, AutoGen, LangGraph, and OpenAI Agents SDK. Any other runtime over MCP, the Node/Python SDK, or direct REST.</sub></p>

<p> <a href="#deploy"><img alt="Deploy" src="https://img.shields.io/badge/Deploy-Vercel%20%2B%20Neon-orange?style=flat-square" /></a> <a href="#10-second-demo"><img alt="Try the demo" src="https://img.shields.io/badge/Demo-npx%20dashclaw--demo-blue?style=flat-square" /></a> <a href="#choose-your-integration-path"><img alt="Connect an agent" src="https://img.shields.io/badge/Connect-MCP%20%7C%20SDK%20%7C%20Hooks-zinc?style=flat-square" /></a> </p>

<p> <a href="https://dashclaw.io"><img src="https://img.shields.io/badge/website-dashclaw.io-orange?style=flat-square" alt="Website" /></a> <a href="https://dashclaw.io/docs"><img src="https://img.shields.io/badge/docs-SDK%20%26%20API-blue?style=flat-square" alt="Docs" /></a> <a href="https://github.com/ucsandman/DashClaw/stargazers"><img src="https://img.shields.io/github/stars/ucsandman/DashClaw?style=flat-square&color=yellow" alt="GitHub stars" /></a> <a href="https://github.com/ucsandman/DashClaw/blob/main/LICENSE"><img src="https://img.shields.io/badge/license-MIT-green?style=flat-square" alt="License" /></a> <a href="https://www.npmjs.com/package/dashclaw"><img src="https://img.shields.io/npm/v/dashclaw?style=flat-square&color=orange" alt="npm" /></a> <a href="https://pypi.org/project/dashclaw/"><img src="https://img.shields.io/pypi/v/dashclaw?style=flat-square&color=orange" alt="PyPI" /></a> </p> </div>

<br />

Claude Code — installer for hooks + plugin

npm run hooks:install ln -s "$(pwd)/plugins/dashclaw" ~/.claude/plugins/dashclaw

Codex — installer wires manifest, hooks, and AGENTS.md governance protocol

node cli/bin/dashclaw.js install codex --project /path/to/your/project

Deploy

Deploy with Vercel

$0 to deploy. Vercel free tier plus Neon free tier. Click the button, add the Neon integration when prompted, fill in the env vars listed in .env.example. The schema migration runs as part of the build, so there is no manual migration step.

After deploy

  1. Open the app at https://your-app.vercel.app and sign in.
  2. Copy the integration snippet from Mission Control. It pre-fills your base URL and gives you a one-click API key.
  3. Run it. node --env-file=.env demo.js from any client environment and watch the governed action land in your decisions ledger.

Quick start

10-second demo

npx dashclaw-demo

Spins up a local demo runtime, fires a simulated high-risk deployment, lets DashClaw block it, and opens Decision Replay in your browser. No setup, no accounts.

Optional

  • Live decision stream. Add Upstash Redis credentials (UPSTASH_REDIS_REST_URL, UPSTASH_REDIS_REST_TOKEN) to get cross-instance event replay. Without it, Mission Control uses in-memory events, which is fine for getting started but does not persist across serverless invocations.
  • Hosted trial mode. If you want DashClaw itself to mint trial workspaces (operator deployments only), follow docs/hosted-deployment-runbook.md. That path needs Turnstile, cleanup secrets, and an operator-managed cron.
  • Self-host without Vercel. A Dockerfile and standalone next start path are available; see Dockerfile. The schema migration in scripts/auto-migrate.mjs is idempotent and safe to re-run.

---

3. Node and Python SDKs — including framework integrations

For custom agents, frameworks, and anywhere you want explicit control over what gets governed.

npm install dashclaw     # Node 18+
pip install dashclaw     # Python 3.7+

87-method canonical Node surface: core governance, durable execution finality, scoring profiles, learning analytics, messaging, handoffs, security scanning, threads, sessions, and the execution-studio domains (workflow templates, model strategies, knowledge collections, capability runtime). The Python SDK exposes 227 methods including ready-made framework integrations:

```python

5. Direct REST API and webhooks

Every governance primitive is reachable as HTTP. The stable contract is pinned in docs/openapi/critical-stable.openapi.json; the full inventory (259 routes: 46 stable, 24 beta, 189 experimental) is at docs/api-inventory.md. Webhook events include signal.detected, decision.created, action.created, lost_confirmation, and the rest of the catalog — configurable per org.

6. Skills — governance protocol + live platform reference

Two drop-in skills, both available as zip bundles or source directories in public/downloads/ and auto-bundled into the coding-agent plugins:

  • dashclaw-governance — governance protocol skill. Teaches agents the decision tree (allow / warn / block / require_approval), action recording, approval-wait protocol, session lifecycle, plus six new sections for handoffs, secret hygiene, skill safety, action-scoped open loops, learning, and in-session retrospection.
  • dashclaw-platform-intelligence — live API reference, env var contract, and troubleshooting playbook with progressive disclosure. Regenerated from the codebase on every release so it never drifts from the running runtime.
cp -r public/downloads/dashclaw-governance .claude/skills/
cp -r public/downloads/dashclaw-platform-intelligence .claude/skills/

Or grab the zips from dashclaw.io/downloads. The platform-intelligence skill is also published on ClawHub.

---

Real agent in 8 minutes (SDK path)

npm install dashclaw   # or: pip install dashclaw
import { DashClaw, GuardBlockedError, ApprovalDeniedError } from 'dashclaw';

const claw = new DashClaw({
  baseUrl: process.env.DASHCLAW_BASE_URL,
  apiKey: process.env.DASHCLAW_API_KEY,
  agentId: 'my-agent',
});

// 1. Guard
const decision = await claw.guard({ action_type: 'deploy', risk_score: 80 });

// 2. Record
const action = await claw.createAction({
  action_type: 'deploy',
  declared_goal: 'Ship release 2.13.4 to production',
});

// 3. Verify reasoning basis
await claw.recordAssumption({
  action_id: action.action_id,
  assumption: 'Tests passed on the candidate commit',
});

// 4. Outcome (durable, retry-safe)
try {
  // ...do the real work...
  await claw.reportActionSuccess(action.action_id, 'Deployed 2.13.4');
} catch (err) {
  await claw.reportActionFailure(action.action_id, err.message);
}

Python uses the same shape with snake_case. Full reference: sdk/README.md. Step-by-step walkthrough: QUICK-START.md.

---

Choose your integration path

DashClaw meets agents where they already are. Every path lands on the same governance primitives, audit ledger, and approval queue — pick the one closest to how your agent already runs.

1. Coding-agent plugins (Claude Code, Codex, Hermes Agent)

One plugin source, three ecosystems. Distributed via plugins/dashclaw/. Each manifest ships the MCP server config, the dashclaw-governance protocol skill, the dashclaw-platform-intelligence reference skill, and a distinct agent_id so Mission Control separates sessions per host.

```bash

4. OpenClaw plugin

For agents built on OpenClaw, @dashclaw/openclaw-plugin wires governance into the lifecycle directly.

npm install @dashclaw/openclaw-plugin

It intercepts every tool-use call (before_tool_call, llm_output, after_tool_call, agent_end), calls guard / record / waitForApproval automatically, and ships a HOOK.md the openclaw CLI installs. Tool-classification vocabulary aligns with DashClaw guard action types so policies behave consistently across plugin, hook, and SDK paths.

🎯 aiskill88 AI 点评 A 级 2026-05-31

DashClaw是一个有趣的AI工作流项目,提供了决策基础设施

⚡ 核心功能

👥 适合人群

自动化工程师和运维人员项目经理和业务分析师希望减少重复性工作的专业人士数字化转型团队

🎯 使用场景

  • 自动化日常重复性工作,将精力集中于创造性任务
  • 构建数据采集 → 处理 → 输出的完整自动化管线
  • 实现跨平台、跨系统的数据流转和业务协同

⚖️ 优点与不足

✅ 优点
  • +MIT 协议,可免费商用
  • +大幅减少重复性人工操作
  • +可视化流程,清晰直观
  • +可扩展性强,支持复杂场景
⚠️ 不足
  • 初始配置和调试需投入一定时间
  • 强依赖外部服务的稳定性
  • 复杂场景需具备一定技术基础
⚠️ 使用须知

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。

📄 License 说明

✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。

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❓ 常见问题 FAQ

DashClaw是一个开源的AI工作流项目,提供决策基础设施
💡 AI Skill Hub 点评

经综合评估,DashClaw 在Agent工作流赛道中表现稳健,质量良好。如果你已有明确的使用需求,可以直接上手体验;如果还在评估阶段,建议对比同类工具后再做决策。

⬇️ 获取与下载
⬇ 下载源码 ZIP

✅ MIT 协议 · 可免费商用 · 直接从 aiskill88 服务器下载,无需跳转 GitHub

📚 深入学习 DashClaw
查看分步骤安装教程和完整使用指南,快速上手这款工具
🌐 原始信息
原始名称 DashClaw
Topics ai-agentsagent-frameworkjavascript
GitHub https://github.com/ucsandman/DashClaw
License MIT
语言 JavaScript
🔗 原始来源
🐙 GitHub 仓库  https://github.com/ucsandman/DashClaw 🌐 官方网站  https://www.dashclaw.io/

收录时间:2026-05-31 · 更新时间:2026-05-31 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。