能力标签
TangleClaw
⚙️
Agent工作流

TangleClaw

基于 JavaScript · 无代码搭建完整 AI 自动化流程
⭐ 6 Stars 🍴 2 Forks 💻 JavaScript 📄 MIT 🏷 AI 7.5分
7.5AI 综合评分
workflowaiai-agentai-codingaiderclaude-codejavascript
✦ AI Skill Hub 推荐

经 AI Skill Hub 精选评估,TangleClaw 获评「推荐使用」。这款Agent工作流在功能完整性、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。

📚 深度解析

TangleClaw 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。

TangleClaw 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

AI编程会话协调器,持久tmux会话,多引擎管理,提高开发效率

TangleClaw 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
⭐ 6
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
2

📖 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」。

AI编程会话协调器,持久tmux会话,多引擎管理,提高开发效率

TangleClaw 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 tangleclaw

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

# 方式三:项目依赖安装
npm install tangleclaw

# 方式四:从源码运行
git clone https://github.com/Jason-Vaughan/TangleClaw
cd TangleClaw
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
tangleclaw --help

# 基本用法
tangleclaw [options] <input>

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

const result = await tangleclaw.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
# tangleclaw 配置说明
# 查看配置选项
tangleclaw --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 TANGLECLAW_CONFIG="/path/to/config.yml"
📑 README 深度解析 真实文档 完整度 63/100 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。

TangleClaw v3

<p align="center"> <img src="https://github.com/Jason-Vaughan/project-assets/blob/main/tangleclaw-logo.png?raw=true" alt="TangleClaw logo" width="200"> </p>

<p align="center"> <strong>AI coding session orchestrator</strong> — persistent sessions, multi-engine management, methodology enforcement, mobile access </p>

<p align="center"> <code>claude code</code> &middot; <code>codex</code> &middot; <code>gemini cli</code> &middot; <code>aider</code> &middot; <code>openclaw</code> &middot; <code>tmux</code> &middot; <code>pwa</code> &middot; <code>zero dependencies</code> </p>

<p align="center"> <strong>macOS only</strong> (launchd required for service management) </p>

---

You VPN into your dev machine. You SSH in. You navigate to your project directory, fire up an AI coding agent, and start building. Thirty minutes later your VPN hiccups, or your SSH tunnel drops, or your laptop goes to sleep — and the session is gone. The agent's context, your conversation history, everything. There's no way to reconnect. You SSH back in, start over, and re-explain what you were doing.

TangleClaw was built to fix that. It wraps AI coding sessions in persistent tmux processes so they survive network drops, device switches, and reconnects. Close your laptop at your desk, open your phone on the couch, and pick up the exact same session. The agent never knows you left.

What started as session persistence grew into a full orchestration platform. Once you have persistent sessions, you start wanting a dashboard to manage them. Then you want your development methodology enforced as structural rules, not suggestions the agent can ignore. Then you want engine-native config generated automatically so Claude Code, Codex, Gemini CLI, and Aider all get the same instructions without you maintaining four different config files. Then you want port conflict management across projects, mobile access, idle detection, session wrap protocols.

TangleClaw is all of that — a local platform that sits between you and your AI coding agents, accessible from any browser or phone on your network. Project groups with shared markdown docs, per-session launch-mode selection (from fully interactive to full-autonomy sandboxed), file-based session memory that persists across restarts, a one-click mkcert HTTPS wizard, and a universal project-version detection chain are all wired in.

Features

  • Persistent sessions — AI engine sessions run in tmux, surviving network drops, device switches, and reconnects. Close your laptop, switch devices, pick up where you left off
  • Five built-in enginesClaude Code, Codex, Gemini CLI, Aider, and OpenClaw. Write rules once — TangleClaw generates engine-native config so every agent gets the same instructions
  • Launch mode selector — pick a permission mode when you start a session: Interactive (prompt on every action), Accept Edits (auto-accept file reads and edits), Plan Only (read-only, no code changes), Auto (full autonomy with safety classifier), or Bypass (accepts everything — containers/VMs only). Engines without launchModes launch as before; Claude Code ships with all five wired in
  • Project groups & shared docs — link related projects into a group, then share markdown documents across them (architecture notes, network diagrams, runbooks) with per-doc locking so two sessions can't step on each other. Shared directories auto-sync .md files on session launch
  • Session memory — file-based, per-project memory system at .tangleclaw/memories/ that persists context across AI sessions. The guide is injected into every engine config so Claude, Codex, Gemini, and Aider all read and update it the same way
  • Methodology enforcement — pluggable JSON templates define phases, rules, and session behavior. Rules are structural gates, not suggestions. First-class Prawduct integration for governed workflows with independent Critic review
  • PortHub built in — central port registry preventing conflicts across all projects. Originally a standalone CLI, now fully integrated into TangleClaw with permanent and TTL leases, heartbeat support, and system-wide conflict detection via lsof
  • HTTPS via mkcert, one click — first-run wizard detects mkcert, generates localhost certs into ~/.tangleclaw/certs/, and hot-swaps the server to HTTPS with a restart overlay. Required for OpenClaw Web UI device pairing; optional manual-cert and skip paths also supported
  • Dashboard & mobile PWA — manage projects, launch sessions, and interact with AI agents from any browser or phone on your network. Installable on iOS and Android. Archive projects you're not touching, get an update-available pill when a new TangleClaw ships, and see model status for Claude/OpenAI/Google in the session banner
  • OpenClaw integration — connect to remote OpenClaw instances via SSH or Web UI mode with automatic SSH tunnel management, and live background process visibility via ClawBridge
  • Universal project version detection — every project's current version is resolved through a layered chain: .tangleclaw/project-version.txt (AI-recorded) → CHANGELOG.mdversion.jsonpackage.json. The session wrap re-records it, so the dashboard badge stays honest across any project convention
  • Zero dependencies — Node.js 22+ stdlib only. No npm install, no build step, no bundler

<details> <summary>All features</summary>

Prerequisites

  • macOS — TangleClaw uses launchd for service management. Linux support is not yet available
  • Node.js 22+ — required for node:sqlite and node:test
  • ttyd — browser-based terminal access (brew install ttyd)
  • tmux — session multiplexer (brew install tmux)
  • At least one AI CLI engineClaude Code, Codex, Gemini CLI, or Aider
  • Prawduct (optional) — install separately for governed workflows with discovery, planning, building phases, and independent Critic review
  • OpenClaw (optional) — for remote AI agent sessions. Requires SSH access to the OpenClaw host
  • ClawBridge (optional) — for background process visibility on OpenClaw instances

Quick Start

git clone https://github.com/Jason-Vaughan/TangleClaw.git
cd TangleClaw
./deploy/install.sh

The install script: 1. Checks prerequisites (node 22+, ttyd, tmux) 2. Generates launchd plists with correct paths 3. Installs and loads the services 4. Runs a health check

Access the landing page at http://localhost:3102. On first launch, a setup wizard walks you through configuration — including choosing your projects directory. This is a single folder where all your managed projects live (e.g., ~/Projects). TangleClaw scans this directory, detects existing repos and engines, and lets you attach them as managed projects.

Screenshots

<p align="center"> <img src="https://github.com/Jason-Vaughan/project-assets/blob/main/tangleclaw-screenshots/project%20splash%20screen%20with%20sampele%20cards.png?raw=true" alt="TangleClaw dashboard" width="800"> <br><em>Dashboard — project cards with engine badges, methodology status, git info, and session indicators</em> </p>

<p align="center"> <img src="https://github.com/Jason-Vaughan/project-assets/blob/main/tangleclaw-screenshots/project%20info%20panel%20expanded.png?raw=true" alt="Project info panel" width="800"> <br><em>Project detail panel — engine, methodology, active session, git state, settings, and session management</em> </p>

<p align="center"> <img src="https://github.com/Jason-Vaughan/project-assets/blob/main/tangleclaw-screenshots/porthub-registry%20list%20example.png?raw=true" alt="PortHub registry" width="800"> <br><em>PortHub registry — all port leases grouped by project with conflict detection</em> </p>

<p align="center"> <img src="https://github.com/Jason-Vaughan/project-assets/blob/main/tangleclaw-screenshots/launch%20mode%20selector%20modal.png?raw=true" alt="Launch mode selector" width="480"> <br><em>Launch mode selector — pick per-session permission mode (Interactive / Accept Edits / Plan Only / Auto / Bypass)</em> </p>

<p align="center"> <img src="https://github.com/Jason-Vaughan/project-assets/blob/main/tangleclaw-screenshots/shared%20directories%20and%20files%20between%20groups%20modal.png?raw=true" alt="Shared directories between groups" width="800"> <br><em>Project groups & shared docs — link related projects and share markdown across them with doc locking</em> </p>

Configuration

Global config lives at ~/.tangleclaw/config.json (auto-created on first run).

Key settings: - serverPort — Landing page server port (code default: 3101, launchd override: 3102) - ttydPort — ttyd terminal port (code default: 3100, launchd override: 3101) - projectsDir — Root directory for managed projects - defaultEngine — Default AI engine for new projects - defaultMethodology — Default methodology template - deletePassword — Optional password for destructive operations - httpsEnabled / httpsCertPath / httpsKeyPath — TLS configuration

Engine profiles: ~/.tangleclaw/engines/*.json Methodology templates: ~/.tangleclaw/templates/

See the Configuration Reference for all fields, types, and defaults.

Integrations

  • OpenClaw — SSH or Web UI mode, connection registry, health checks, auto SSH tunnels, reverse proxy, auto device pairing
  • ClawBridge — live background process visibility — status pills, detail panels with timestamps, exit codes, attention flags
  • Eval Audit Mode — multi-tiered AI agent evaluation. Ingests exchange data from OpenClaw, scores with intelligent gating, tracks baselines, detects drift, generates incidents
🎯 aiskill88 AI 点评 A 级 2026-05-23

TangleClaw是一个开源的AI工作流,提供了持久的tmux会话和多引擎管理功能,提高了开发效率,但其使用场景和安装说明需要进一步完善

📚 实用指南(长尾问题)
适合谁
  • 构建多智能体协作系统的 Agent 开发者
最佳实践
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
部署方案
  • CLI:直接 npm install -g / pip install,命令行调用
  • 云端托管:可放在 Vercel / Railway / Fly.io 等 PaaS 平台
相关搜索
TangleClaw 中文教程TangleClaw 安装报错怎么办TangleClaw Agent 工作流TangleClaw 与同类工具对比TangleClaw 最佳实践TangleClaw 适合谁用

⚡ 核心功能

👥 适合谁
  • 构建多智能体协作系统的 Agent 开发者
⭐ 最佳实践
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
⚠️ 常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)

👥 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

TangleClaw 是一款JavaScript开发的AI辅助工具。开源AI工作流:AI coding session orchestrator — persistent tmux sessions, multi-engine manageme。⭐6 · JavaScript 主要应用场景包括:开发者可以使用TangleClaw来管理多个AI编程会话,提高开发效率。
💡 AI Skill Hub 点评

AI Skill Hub 点评:TangleClaw 的核心功能完整,质量良好。对于自动化工程师和运维人员来说,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。

⬇️ 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 TangleClaw
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 TangleClaw
原始描述 开源AI工作流:AI coding session orchestrator — persistent tmux sessions, multi-engine manageme。⭐6 · JavaScript
Topics workflowaiai-agentai-codingaiderclaude-codejavascript
GitHub https://github.com/Jason-Vaughan/TangleClaw
License MIT
语言 JavaScript
🔗 原始来源
🐙 GitHub 仓库  https://github.com/Jason-Vaughan/TangleClaw

收录时间:2026-05-23 · 更新时间:2026-05-30 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。