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Mind AI工作流
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Agent工作流

Mind AI工作流

基于 TypeScript · 无代码搭建完整 AI 自动化流程
英文名:mind
⭐ 108 Stars 🍴 7 Forks 💻 TypeScript 📄 MIT 🏷 AI 8.0分
8.0AI 综合评分
AI工作流TypeScript
✦ AI Skill Hub 推荐

经 AI Skill Hub 精选评估,Mind AI工作流 获评「强烈推荐」。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 8.0 分,适合有一定技术背景的用户使用。

📚 深度解析

Mind AI工作流 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。

Mind AI工作流 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 8.0 分,是同类 Agent 工作流中的精选推荐。

📋 工具概览

Mind AI工作流 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
⭐ 108
开发语言
TypeScript
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
8.0 分
工具类型
Agent工作流
Forks
7

📖 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」。

Mind AI工作流 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 mind

# 方式二:npx 直接运行(无需安装)
npx mind --help

# 方式三:项目依赖安装
npm install mind

# 方式四:从源码运行
git clone https://github.com/GabrielMartinMoran/mind
cd mind
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
mind --help

# 基本用法
mind [options] <input>

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

const result = await mind.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
# mind 配置说明
# 查看配置选项
mind --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 MIND_CONFIG="/path/to/config.yml"
📑 README 深度解析 真实文档 完整度 58/100 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。

🧠 Mind

Stop losing context across sessions, tools, and time. Give your AI workflow a memory that lasts.

Mind is a local memory layer for AI workflows: a persistent memory system for durable context such as decisions, bug fixes, patterns, checkpoints, and session summaries that would otherwise disappear across sessions and tools.

It supports recovery after compaction through checkpoint and session continuity behavior, while giving humans a way to visualize, inspect, and modify that memory through the CLI and web UI.

Why Mind works above the fold:

- Local persistent store. One SQLite-backed memory system you control. - Built for agent workflows. Use it through the CLI, MCP server, HTTP API, and web UI. - Resumption tools included. Checkpoints and session summaries help recover context and continue work. - Optional file sync. Experimental autosync can mirror project spaces into versioned .mind/ files. - Search when you need it. Full-text search is built in, and semantic search is available as an optional add-on.

Get started with Installation or jump straight to the Quick start.

Mind Preview

Requirements

  • Bun 1.2+ (auto installed by the one-line installer if not present)

Installation

You can install Mind quickly and start using it right away. If you want agent setup later, jump to Agent setup.

Install from source

git clone https://github.com/GabrielMartinMoran/mind.git
cd mind
bun install

Agent setup

Mind supports multiple agent integrations, but they are not all equally mature. Run mind setup without an agent name to see the current capability matrix, then configure the specific agent you want.

mind setup              # show capability matrix first
mind setup claude-code
mind setup opencode
mind setup cursor
mind setup codex
mind setup windsurf
mind setup gemini-cli
mind setup vscode
mind setup antigravity
mind setup refresh      # refresh detected mind-managed integrations

mind setup refresh is conservative by default. It refreshes only detected integrations, where detection means mind finds an existing mind-owned signal, such as an MCP config, managed protocol block, hook/plugin/script, or installed mind-management skill. Use --dry-run to preview changes, --all to refresh all supported agents, or --agent <name> to target one agent.

Post-install configuration

Most configuration is optional. You only need extra setup if you want to change paths, ports, or enable semantic search.

1. Create your .env file:

cp .env.example .env

Mind ships with a .env.example that contains all configurable options. The installer will also do this automatically if .env doesn't exist.

2. Configure environment variables (optional):

Edit .env to customize your setup:

VariableDefaultDescription
MIND_PORT30303Port for the web server
MIND_DATA_DIRdata/Directory for SQLite database and data
MIND_RAG_(empty)_Set to true to enable semantic search
OPENAI_API_KEY_(empty)_Your OpenAI API key (required for RAG)

RAG / semantic search setup:

To enable AI-powered semantic search:

```bash

Quick start

This is the fastest way to see what Mind feels like in practice.

```bash

Basic CLI example

mind create projects/mind "Mind project memory"
mind add projects/mind architecture "CLI uses command registry + atomic command modules"
mind search architecture

For the full command list:

mind help

Usage

In .env:

MIND_RAG=true OPENAI_API_KEY=sk-your-key-here ```

When enabled, memories are embedded using OpenAI's text-embedding-3-small model and combined with full-text search for hybrid retrieval.

mind autosync config

version: 1

Agent integration details

Use the commands in Installation to run setup. This section explains how to read the capability matrix and what each integration actually configures.

mind setup (without agent) now prints a capability matrix per integration using a 3-level model:

  • L1: MCP transport wiring
  • L2: instruction/protocol injection
  • L3: hooks/session/compaction automation

Each level is explicitly marked as supported, unsupported, or unverified with evidence/fallback notes. If a capability is not implemented, setup output is explicit (no silent skip).

Agent status matrix

Status labels used here:

  • Complete: L1/L2/L3 are all supported
  • Partial: at least one level is unsupported or unverified
  • Experimental: declaration exists but integration is explicitly unstable/unverified
  • Roadmap: planned declaration only, no adapter wiring
**Agent****Status****Capability reality**
OpenCodeCompleteL1 supported, L2 supported, L3 supported
Claude CodeCompleteL1 supported, L2 supported, L3 supported (opt-in hooks)
CodexPartialL1 supported, L2 supported, L3 unsupported
CursorPartialL1 supported, L2 unverified, L3 supported
WindsurfPartialL1 supported, L2 unsupported, L3 unsupported
Gemini CLIPartialL1 supported, L2 unsupported, L3 unsupported
VSCodePartialL1 supported, L2 unsupported, L3 unsupported
AntigravityPartialL1 supported, L2 unsupported, L3 unsupported

Rollout policy:

  • Wave 1 priority agents: OpenCode, Claude Code, Gemini CLI, Cursor
  • Claude Code now includes managed L2 protocol injection by writing ~/.claude/instructions/mind-memory-protocol.md and maintaining a managed block in ~/.claude/CLAUDE.md
  • Claude Code L3 hooks automation is opt-in and non-blocking (default off). Enable with MIND_SETUP_CLAUDE_ENABLE_HOOKS=true before running setup.
  • Cursor L2 remains intentionally unverified (no verified global user-rules injection path)
  • Cursor L3 is implemented with global hooks wiring (~/.cursor/hooks.json + managed hook script)
  • Existing integrations outside Wave 1 remain wired in the same capability model with explicit status
  • Antigravity is now a supported agent with L1 MCP wiring and skill installation at ~/.gemini/antigravity/

mind setup opencode is idempotent and non-destructive:

  • prefers ~/.config/opencode/opencode.jsonc over opencode.json when both exist, and never creates opencode.json if opencode.jsonc is already present. New files are created as opencode.jsonc (the JSONC superset of JSON that OpenCode accepts natively).
  • preserves unknown keys already present in the chosen config file
  • configures mcp.mind as local command transport (type: "local", command: ["<path-to-mind>", "mcp"])
  • writes/refreshes ~/.config/opencode/instructions/mind-memory-protocol.md
  • ensures that instruction file is present in OpenCode's instructions list
  • configures prudent L3 session/compaction automation by default and non-blocking, writing ~/.config/opencode/plugins/mind-automation.js during setup
Setup safety contract: all setup flows that touch a user-owned JSON/JSONC config file (opencode, claude fallback, cursor, windsurf, gemini-cli, vscode, antigravity) now go through src/setup/safe-config.ts. Existing files are parsed strictly — a parse failure aborts the run with a clear error rather than overwriting the file with an empty default. Content-changing writes are preceded by a timestamped sibling backup (.bak.YYYYMMDDTHHMMSSmmmZ) and the new content is staged in a sibling .tmp file and renamed into place so a crash mid-write cannot leave a partial file. The same contract applies to mind setup refresh.

mind setup codex keeps setup idempotent and writes local MCP command args in ~/.codex/config.toml:

  • [mcp_servers.mind]
  • command = "<path-to-mind>"
  • args = ["mcp"]

It also non-destructively upserts a managed Memory Protocol block in ~/.codex/AGENTS.md.

mind setup cursor keeps existing MCP setup behavior and now configures global L3 continuity hooks:

  • writes/updates ~/.cursor/hooks.json with managed idempotent entries for sessionStart, preCompact, and stop
  • writes/refreshes executable hook script ~/.cursor/hooks/mind-session-continuity.sh
  • preserves existing hook config keys/entries

Check server process status:

mind server-status

⚡ 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

自动化AI工作流
💡 AI Skill Hub 点评

AI Skill Hub 点评:Mind AI工作流 的核心功能完整,质量优秀。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 Mind AI工作流
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 mind
原始描述 开源AI工作流:Mind is an automated memory layer for AI workflows. Say goodbye to your agents a。⭐108 · TypeScript
Topics AI工作流TypeScript
GitHub https://github.com/GabrielMartinMoran/mind
License MIT
语言 TypeScript
🔗 原始来源
🐙 GitHub 仓库  https://github.com/GabrielMartinMoran/mind

收录时间:2026-06-08 · 更新时间:2026-06-08 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。