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开源AI工作流:Personal Reasoning Engine

基于 JavaScript · 无代码搭建完整 AI 自动化流程
英文名:pre
⭐ 10 Stars 🍴 2 Forks 💻 JavaScript 📄 MIT 🏷 AI 7.5分
7.5AI 综合评分
workflowapple-silicongemma4inferencellmlocal-aijavascript
✦ AI Skill Hub 推荐

AI Skill Hub 推荐使用:开源AI工作流:Personal Reasoning Engine 是一款优质的Agent工作流。AI 综合评分 7.5 分,在同类工具中表现稳健。如果你正在寻找可靠的Agent工作流解决方案,这是一个值得深入了解的选择。

📚 深度解析
开源AI工作流:Personal Reasoning Engine 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。

开源AI工作流:Personal Reasoning Engine 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

Personal Reasoning Engine — Gemma 4 powered Agentic assistant running locally,支持JavaScript开发。

开源AI工作流:Personal Reasoning Engine 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
⭐ 10
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」。

Personal Reasoning Engine — Gemma 4 powered Agentic assistant running locally,支持JavaScript开发。

开源AI工作流:Personal Reasoning Engine 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 pre

# 方式二:npx 直接运行(无需安装)
npx pre --help

# 方式三:项目依赖安装
npm install pre

# 方式四:从源码运行
git clone https://github.com/sunkencity999/pre
cd pre
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
pre --help

# 基本用法
pre [options] <input>

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

const result = await pre.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
# pre 配置说明
# 查看配置选项
pre --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 PRE_CONFIG="/path/to/config.yml"
📑 README 深度解析 真实文档 完整度 75/100 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。

PRE 2.0 — Personal Reasoning Engine

CI

A local AI operating system for macOS, Windows, and Linux. 79 tools, autonomous background agents, multi-model routing, self-improving calibration, knowledge graph, desktop automation, document intelligence, voice interface, event-driven triggers, 16 enterprise integrations, persistent memory, self-architecting virtual tools, and a full management GUI — running entirely on your hardware by default. No cloud required. Optionally connect any OpenAI-compatible API, Azure AI Foundry, or Anthropic endpoint for cloud-powered inference while keeping all tools, memory, and data local.

PRE is not a chatbot with tools bolted on. It is a purpose-built agentic platform — engineered from the ground up around one specific model on one specific hardware target, then generalized across platforms. Every architectural decision, from socket-level I/O to dynamic memory allocation to prompt compression, exists to make Google Gemma 4 26B-A4B run at its absolute ceiling on Apple Silicon. The result is a local agent that doesn't feel local: ~73 tokens/second, sub-second time to first token, 128K context window, 79 integrated tools, persistent memory, local RAG, local image generation, autonomous scheduling, event-driven triggers, voice interface, a built-in web GUI, and real agentic workflows — all running on your hardware.

PRE 2.0 adds four pillars that bring capabilities previously exclusive to frontier cloud agents:

PillarWhat It DoesWhy It Matters
**Multi-Model Router**Classifies messages by complexity and routes to fast, standard, or frontier modelsGet instant answers for simple queries, full reasoning for complex ones — like Claude's model routing, but you control the tiers
**Autonomous Background Agents**Fire-and-forget agents that run in parallel, survive sleep/hibernate/reboot, checkpoint every tool turnLong-running research, code generation, and analysis tasks run autonomously while you work — something no other local agent offers
**Learning from Experience**Calibrated self-knowledge, skill effectiveness tracking, knowledge graph, and training data export for personal fine-tuningPRE knows what it's good at, understands how its knowledge connects, improves over time, and can export its experience as fine-tuning data — a closed feedback loop no cloud agent provides
**Self-Awareness**Runtime operational state summary injected into every prompt — confidence, agents, triggers, graph statsPRE knows exactly what it has running, where it's strong, and how much it's learned — no other local agent has introspective context

PRE has two interfaces: a CLI (macOS Apple Silicon only, Objective-C) and a Web GUI (Node.js) that runs on macOS (Apple Silicon + Intel with eGPU), Windows, and Linux. The Web GUI provides full access to all 79 tools with platform-native implementations on each OS.

The reference system is a MacBook Pro with an M4 Max (128 GB unified memory). Windows and Linux systems require an NVIDIA GPU (Linux also supports AMD via ROCm). Intel Macs are supported via eGPU (NVIDIA Ampere+ or AMD RDNA3+) using the TinyGPU driver. All three installers auto-detect GPU VRAM and select the optimal quantization (28+ GB VRAM for q8_0, otherwise q4_K_M).

---

<p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="docs/pre_poster.svg"> <source media="(prefers-color-scheme: light)" srcset="docs/pre_poster.svg"> <img alt="PRE Architecture Overview" src="docs/pre_poster_4x.png" width="800"> </picture> </p>

---

What About Skills?

Anthropic's Claude ecosystem introduced a "Skills" concept — static instruction files (SKILL.md) that teach Claude how to perform specific workflows. It's a good idea, but it's manual: someone writes the skill, tests it, distributes it.

PRE takes this further with progressive skill intelligence:

  1. Auto-discovery — PRE analyzes completed tool chains and automatically identifies reusable patterns worth saving as skills. No manual authoring required.
  2. Behavioral guidance — Each auto-generated skill includes not just the mechanical steps (call X, then Y) but behavioral instructions: when to use it vs. alternatives, how to handle failures, quality criteria, common pitfalls. This teaches the model how to think about a domain, not just what tools to run.
  3. Progressive disclosure — Skill context is injected into the system prompt proportionally to relevance. A strong match (>80%) gets full behavioral guidance. A partial match gets a one-liner. No match adds nothing. This prevents token waste while ensuring rich context when it matters.
  4. Effectiveness tracking — Every skill execution is tracked by outcome (success, failure, user correction). Low-performing skills are automatically filtered from recommendations. High-performing skills surface more often. The system improves without intervention.

Static skills are a starting point. PRE's skills are alive — they emerge from usage, evolve with feedback, and die when they stop working.

---

What's New in 2.0

PRE 2.0 is a major release that adds three capability pillars previously available only in frontier cloud agents — or not available anywhere at all.

macOS Prerequisites (Apple Silicon)

ComponentRequired
**macOS**14.0+ (Sonoma or later)
**Chip**Apple Silicon (M1 or later)
**RAM**32 GB minimum, 64+ GB recommended
**Disk**~28 GB for model, +8 GB for image generation (optional)
**Ollama**[ollama.ai](https://ollama.ai) or brew install ollama
**Xcode CLI**xcode-select --install
**Node.js 18+**For web GUI (brew install node)
**Python 3.10-3.13**Optional — for ComfyUI image generation

macOS Prerequisites (Intel + eGPU)

ComponentRequired
**macOS**12.1+ (Monterey or later)
**Chip**Intel x86_64
**eGPU**NVIDIA Ampere+ (RTX 3000+) or AMD RDNA3+ via Thunderbolt/USB4
**TinyGPU**[tinygrad TinyGPU driver](https://docs.tinygrad.org/tinygpu/) (Apple-signed)
**Docker Desktop**Required for NVIDIA CUDA path ([docker.com](https://www.docker.com/products/docker-desktop/))
**RAM**16 GB minimum (model runs on eGPU VRAM)
**eGPU VRAM**16+ GB (q4_K_M); 28+ GB for q8_0
**Disk**~15 GB (q4_K_M) or ~28 GB (q8_0) for model
**Ollama 0.4+**[ollama.ai](https://ollama.ai) (auto-detects TinyGPU)
**Node.js 18+**For web GUI (brew install node)
Note: Intel Macs run the Web GUI only (CLI requires Apple Silicon). PRE detects eGPU connections at runtime (30-second polling) — you can plug in an eGPU after PRE is already running and it will be detected automatically.

Windows Prerequisites

ComponentRequired
**Windows**10 or 11
**GPU**NVIDIA (for Ollama GPU inference)
**RAM**16 GB minimum, 64+ GB recommended for large context windows
**GPU VRAM**16+ GB (q4_K_M); 28+ GB for q8_0 — model must fit in VRAM for full speed
**Disk**~15 GB (q4_K_M) or ~28 GB (q8_0) for model
**Ollama**[ollama.ai](https://ollama.ai) or installed via winget by the installer
**Node.js 18+**Installed via winget by the installer

Linux Prerequisites

ComponentRequired
**Distro**Ubuntu 22.04+, Debian 12+, Fedora 38+ (Arch best-effort)
**GPU**NVIDIA with CUDA (for Ollama GPU inference); AMD ROCm future
**RAM**16 GB minimum, 64+ GB recommended for large context windows
**GPU VRAM**16+ GB (q4_K_M); 28+ GB for q8_0 — model must fit in VRAM for full speed
**Disk**~15 GB (q4_K_M) or ~28 GB (q8_0) for model
**Ollama**Installed via official script by the installer
**Node.js 18+**Via nvm, nodesource, or distro package

Optional: evolution-data-server (calendar/contacts/reminders via GNOME EDS), xdotool + scrot (desktop automation, X11), espeak-ng (TTS), xclip (clipboard).

What the installer does

All three installers follow the same pattern: check prerequisites, install missing dependencies, pull the Gemma 4 model from Ollama, auto-select quantization based on available memory, create ~/.pre/ data directories, size the context window, set up the Web GUI, and optionally configure auto-start. Everything stays local — no accounts, no cloud setup, no telemetry. The install is idempotent: running it again updates without breaking your existing data.

PRE detects your project, loads memories, and drops you into an interactive prompt:

╔══════════════════════════════════════════════════╗
║  Personal Reasoning Engine (PRE) 2.0            ║
║  Gemma 4 26B-A4B                                ║
╚══════════════════════════════════════════════════╝
  Server:  http://localhost:11434
  Web GUI: http://localhost:7749
  Project: my-project  /Users/you/my-project
  Channel: #general
  Memory:  3 entries loaded
  Type /help for commands

my-project #general>

---

Installation

macOS Install

git clone https://github.com/sunkencity999/pre.git
cd pre
./install.sh

The installer handles everything: system validation, Ollama, model pull, binary compilation, web GUI dependencies, terminal-notifier, ComfyUI (optional), data directories, RAM-based context window sizing, MCP auto-setup for Claude/Codex/Antigravity, model pre-warming, fast tier model creation (pre-gemma4-fast), inference backend selection (Ollama vs llama.cpp), and optional auto-start at login.

Inference backend: The installer auto-selects the best backend for your hardware. NVIDIA GPU users (via eGPU) get llama.cpp (Flash Attention + GBNF grammar constraints). Apple Silicon users get Ollama (simpler model management, similar performance). You can switch backends anytime from Settings.

./install.sh --yes   # Non-interactive — accepts all defaults

Or install manually:

```bash

Build PRE CLI + Telegram bot

make pre telegram

Windows Install

git clone https://github.com/sunkencity999/pre.git
cd pre

Easiest: Double-click install.cmd in the pre folder. This handles execution policy automatically.

From a terminal:

powershell -ExecutionPolicy Bypass -File install.ps1
powershell -ExecutionPolicy Bypass -File install.ps1 -Yes   # Non-interactive

The installer checks system requirements, installs Ollama and Node.js via winget, pulls the model, creates the fast tier model (pre-gemma4-fast), creates ~/.pre/ directories, auto-sizes the context window based on RAM, offers llama.cpp as the default backend for NVIDIA GPUs (with Ollama as fallback for embeddings), configures Ollama environment variables, and optionally enables auto-start at login.

Note: The Windows installer sets up the Web GUI only. The CLI engine (pre.m) is an Objective-C application that requires macOS. The Telegram bot is included in the Web GUI and works on all platforms.

Linux Install

git clone https://github.com/sunkencity999/pre.git
cd pre
./install-linux.sh

The installer detects your distro and package manager (apt/dnf/pacman), checks NVIDIA VRAM via nvidia-smi, installs Ollama, pulls the model with VRAM-aware quant selection, creates the fast tier model (pre-gemma4-fast), offers llama.cpp as the default backend for NVIDIA GPUs, installs Node.js dependencies, auto-sizes the context window, and optionally installs voice tools, GNOME PIM integration (evolution-data-server), desktop automation tools (xdotool/scrot), and systemd autostart.

Note: Linux runs the Web GUI only. The CLI engine requires macOS. Native app integration (calendar, contacts, reminders) requires GNOME with Evolution Data Server.

Quick Start

Optional: clickable cron notifications

brew install terminal-notifier

Enterprise Integrations

16 services in one interface — Jira, Confluence, SharePoint, Smartsheet, Slack, Linear, Zoom, Figma, Asana, Dynamics 365, Gmail, Google Drive, Google Docs, GitHub, Telegram, Brave Search, and Wolfram Alpha. Search Jira, cross-reference Confluence, pull a file from SharePoint, and post a summary to Slack — in one conversation.

Native app integrations (zero config) — Mail, Calendar, Contacts, Reminders, Notes, and Spotlight work immediately with whatever accounts you've configured. On macOS, uses Mail.app, Calendar.app, Contacts.app, Reminders.app, and Notes.app via AppleScript/EventKit. On Windows, uses Outlook COM for mail, calendar, contacts, and tasks, plus local markdown notes. Spotlight uses Windows Search on Windows. No API keys, no OAuth on either platform.

Head-to-Head Comparison

CapabilityPREClaude CodeCodex CLICursorWindsurfAiderGoose
**Local inference (zero cost)**DefaultNo (cloud API)No (cloud API)No (cloud)No (cloud)OptionalOptional
**Tool count**79~15~10~25~12~10~10
**Background agents w/ crash recovery**YesNoNoPartialPartialNoNo
**Cron scheduling**YesNoNoNoNoNoNo
**Event-driven triggers**YesNoNoNoNoNoNo
**Self-improving calibration**YesNoNoNoNoNoNo
**Skill auto-discovery + behavioral guidance**YesNoNoNoNoNoNo
**Training data export**YesNoNoNoNoNoNo
**Native OS app integration**Yes (Mail, Calendar, Contacts, Reminders, Notes)NoNoNoNoNoNo
**Desktop automation + workflow replay**YesNoLimitedNoNoNoNo
**16 enterprise integrations**YesNo (MCP only)NoNoNoNoMCP only
**Local RAG (semantic search)**YesNoNoCodebase onlyCodebase onlyCodebase mapNo
**Local image generation**Yes (ComfyUI)NoNoNoNoNoNo
**Voice interface**Yes (Whisper + TTS)NoNoNoNoVoice input onlyNo
**Persistent memory (auto-extracted, embedding-ranked)**YesProject-scopedNoLimitedNoNoNo
**Multi-model routing**YesNoNoManual model pickerNoManualManual
**Full management GUI**YesCLI primaryCLI onlyIDE-boundIDE-boundCLI onlyDesktop app
**Monthly cost**$0$20-200+API billing$20+$15+API billingFree + API
🎯 aiskill88 AI 点评 A 级 2026-05-23

该项目提供了一个开源AI工作流解决方案,支持JavaScript开发,适用于需要本地AI工作流的开发者,但需要注意依赖安装和配置。

📚 实用指南(长尾问题)
适合谁
  • 需要让 Claude / Cursor 操作本地工具的 AI 工程师
  • 构建多智能体协作系统的 Agent 开发者
  • 构建企业知识库 / RAG 检索应用的团队
  • 做语音类 AI 产品的开发者
最佳实践
  • 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC,避免暴露公网
  • 生产部署优先使用 Docker Compose 隔离依赖,并挂载 volume 持久化数据
  • 本地部署优先选 GGUF 量化模型,节省显存并保持响应速度
  • 分块大小建议 256-512 tokens,向量库优选 pgvector 或 Qdrant
  • 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)
  • MCP 配置路径拼错或权限不足,重启 Claude Desktop 才生效
  • 容器内无法访问宿主机 localhost — 使用 host.docker.internal
  • embedding 模型与查询模型不一致导致检索失效
  • 显存不足直接 OOM — 优先降低 context 或换更小的量化模型
部署方案
  • Docker:pre 提供官方镜像,docker compose up 一键启动
  • CLI:直接 npm install -g / pip install,命令行调用
  • 本地部署:CPU 8GB 起,GPU 推荐 16GB+ 显存
  • 云端托管:可放在 Vercel / Railway / Fly.io 等 PaaS 平台
相关搜索
pre 中文教程pre 安装报错怎么办pre MCP 配置pre Docker 部署pre Agent 工作流pre 与同类工具对比pre 最佳实践pre 适合谁用
⚡ 核心功能
👥 适合谁
  • 需要让 Claude / Cursor 操作本地工具的 AI 工程师
  • 构建多智能体协作系统的 Agent 开发者
  • 构建企业知识库 / RAG 检索应用的团队
  • 做语音类 AI 产品的开发者
⭐ 最佳实践
  • 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC,避免暴露公网
  • 生产部署优先使用 Docker Compose 隔离依赖,并挂载 volume 持久化数据
  • 本地部署优先选 GGUF 量化模型,节省显存并保持响应速度
  • 分块大小建议 256-512 tokens,向量库优选 pgvector 或 Qdrant
⚠️ 常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • MCP 配置路径拼错或权限不足,重启 Claude Desktop 才生效
  • 容器内无法访问宿主机 localhost — 使用 host.docker.internal
  • embedding 模型与查询模型不一致导致检索失效
👥 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。

🔗 相关工具推荐
📰 相关 AI 新闻
🍿 AI 圈相关吃瓜
🗺️ 相关解决方案
🧩 你可能还需要
基于当前 Skill 的能力图谱,自动补全的工具组合
❓ 常见问题 FAQ
运行npm install
💡 AI Skill Hub 点评

总体来看,开源AI工作流:Personal Reasoning Engine 是一款质量良好的Agent工作流,在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态,建议收藏备用,结合自身场景选择合适时机引入使用。

⬇️ 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 开源AI工作流:Personal Reasoning Engine
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 pre
原始描述 开源AI工作流:Personal Reasoning Engine — Gemma 4 powered Agentic assistant running locally an。⭐10 · JavaScript
Topics workflowapple-silicongemma4inferencellmlocal-aijavascript
GitHub https://github.com/sunkencity999/pre
License MIT
语言 JavaScript
🔗 原始来源
🐙 GitHub 仓库  https://github.com/sunkencity999/pre

收录时间:2026-05-18 · 更新时间:2026-05-19 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。