diff --git a/README.md b/README.md index 20dfd26..4c8d9f5 100644 --- a/README.md +++ b/README.md @@ -1,92 +1,112 @@ -# GenericAgent — 3,300 Lines to Full OS Autonomy +
+ +
-[English](#english) | [中文](#chinese) +

+ English | 中文 +

+--- +## 🌟 Overview -A minimalist autonomous agent framework that gives any LLM physical-level control over your PC — browser, terminal, file system, keyboard, mouse, screen vision, and mobile devices — in ~3,300 lines of Python. +**GenericAgent** is a minimal, self-evolving autonomous agent framework. Its core is just **~3,300 lines of code**. Through **7 atomic tools + a 92-line Agent Loop**, it grants any LLM system-level control over a local computer — covering browser, terminal, filesystem, keyboard/mouse input, screen vision, and mobile devices (ADB). -No Electron. No Docker. No Mac Mini. No 500K-line codebase. No paid installation service. +Its design philosophy: **don't preload skills — evolve them.** -## See It in Action +Every time GenericAgent solves a new task, it automatically crystallizes the execution path into an skill for direct reuse later. The longer you use it, the more skills accumulate — forming a skill tree that belongs entirely to you, grown from 3,300 lines of seed code. - - - - - -

"Order me a milk tea" — navigates a delivery app, picks items, and checks out.
"Find GEM stocks with EXPMA golden cross, turnover > 5%" — quantitative screening via mootdx.
+> **🤖 Self-Bootstrap Proof** — Everything in this repository, from installing Git and running `git init` to every commit message, was completed autonomously by GenericAgent. The author never opened a terminal once. - - - - - - -

Autonomous web exploration — browses and summarizes on its own schedule.
"Find expenses over ¥2K in the past 3 months" — drives Alipay on a phone via ADB.
WeChat batch messaging — yes, it can drive WeChat too.
+## 📋 Core Features +- **Self-Evolving**: Automatically crystallizes each task into an skill. Capabilities grow with every use, forming your personal skill tree. +- **Minimal Architecture**: ~3,300 lines of core code. Agent Loop is just 92 lines. No complex dependencies, zero deployment overhead. +- **Strong Execution**: Injects into a real browser (preserving login sessions). 7 atomic tools take direct control of the system. +- **High Compatibility**: Supports Claude / Gemini / Kimi and other major models. Cross-platform. -## What Happens When You Use It + +## 🧬 Self-Evolution Mechanism + +This is what fundamentally distinguishes GenericAgent from every other agent framework. ``` -You: "Read my WeChat messages" -Agent: installs dependencies → reverse-engineers DB → writes reader script → saves as SOP -Next time: instant recall, zero setup. - -You: "Monitor stock prices and alert me" -Agent: installs mootdx → builds screening workflow → sets up scheduled task → saves as SOP -Next time: one sentence to run. - -You: "Send this file via Gmail" -Agent: configures OAuth → writes send script → saves as SOP -Next time: just works. +[New Task] --> [Autonomous Exploration] (install deps, write scripts, debug & verify) --> +[Crystallize Execution Path into skill] --> [Write to Memory Layer] --> [Direct Recall on Next Similar Task] ``` -**Dogfooding**: This repository — from installing Git to `git init`, writing this README, to every commit message — was built entirely by GenericAgent without the author opening a terminal once. +| What you say | What the agent does the first time | Every time after | +|---|---|---| +| *"Read my WeChat messages"* | Install deps → reverse DB → write read script → save skill | **one-line invoke** | +| *"Monitor stocks and alert me"* | Install mootdx → build selection flow → configure cron → save skill | **one-line start** | +| *"Send this file via Gmail"* | Configure OAuth → write send script → save skill | **ready to use** | -Every task the agent solves becomes a permanent skill. After a few weeks, your instance has a unique skill tree — grown entirely from 3,300 lines of seed code. +After a few weeks, your agent instance will have a skill tree no one else in the world has — all grown from 3,300 lines of seed code. -## The Seed Philosophy -Most agent frameworks ship as finished products. GenericAgent ships as a **seed**. +##### 🎯 Demo Showcase -The 5 core SOPs define how the agent thinks, remembers, and operates. From there, every new capability is discovered and recorded by the agent itself: +| 🧋 Food Delivery Order | 📈 Quantitative Stock Screening | +|:---:|:---:| +| Order Tea | Stock Selection | +| *"Order me a milk tea"* — Navigates the delivery app, selects items, and completes checkout automatically. | *"Find GEM stocks with EXPMA golden cross, turnover > 5%"* — Screens stocks with quantitative conditions. | +| 🌐 Autonomous Web Exploration | 💰 Expense Tracking | 💬 Batch Messaging | +| Web Exploration | Alipay Expense | WeChat Batch | +| Autonomously browses and periodically summarizes web content. | *"Find expenses over ¥2K in the last 3 months"* — Drives Alipay via ADB. | Sends bulk WeChat messages, fully driving the WeChat client. | -1. You ask it to do something new -2. It figures out how (install dependencies, write scripts, test) -3. It saves the procedure as a new SOP in its memory -4. Next time, it recalls and executes directly +## 📅 Latest News -The agent doesn't just execute — it **learns and remembers**. +- **2026-03-10:** [Released million-scale Skill Library](https://mp.weixin.qq.com/s/q2gQ7YvWoiAcwxzaiwpuiQ?scene=1&click_id=7) +- **2026-03-08:** [Released "Dintal Claw" — a GenericAgent-powered government affairs bot](https://mp.weixin.qq.com/s/eiEhwo-j6S-WpLxgBnNxBg) +- **2026-03-01:** [GenericAgent featured by Jiqizhixin (机器之心)](https://mp.weixin.qq.com/s/uVWpTTF5I1yzAENV_qm7yg) +- **2026-01-11:** GenericAgent V1.0 public release -## Quick Start +--- -> 💡 **Windows零基础用户**：不知道Python是什么？[下载便携版](http://kw.fudan.edu.cn/resources/PC-Agent-Portable.zip)（19MB，解压即用） +## 🚀 Quick Start + +#### Method 1: Standard Installation ```bash -# 1. Clone +# 1. Clone the repo git clone https://github.com/lsdefine/GenericAgent.git cd GenericAgent -# 2. Install minimal deps +# 2. Install minimal dependencies pip install streamlit pywebview -# 3. Configure API key +# 3. Configure API Key cp mykey_template.py mykey.py -# Edit mykey.py with your LLM API key +# Edit mykey.py and fill in your LLM API Key # 4. Launch python launch.pyw ``` -## QQ Bot (Optional) +#### Method 2: Windows Portable Version (Recommended for beginners) -QQ support uses `qq-botpy` over WebSocket, so no public webhook is required. +[Download portable version](http://kw.fudan.edu.cn/resources/PC-Agent-Portable.zip) (19MB, unzip and run) + +Full guide: [WELCOME_NEW_USER.md](WELCOME_NEW_USER.md) + +#### Method 3: Android (Termux) + +```bash +cd /sdcard/ga +python agentmain.py +``` +--- + +## 🤖 Bot Interfaces (Optional) + +### QQ Bot + +Uses `qq-botpy` WebSocket long connection — **no public webhook required**: ```bash pip install qq-botpy ``` -Then add these fields to `mykey.py` or `mykey.json`: +Add to `mykey.py`: ```python qq_app_id = "YOUR_APP_ID" @@ -94,216 +114,209 @@ qq_app_secret = "YOUR_APP_SECRET" qq_allowed_users = ["YOUR_USER_OPENID"] # or ['*'] for public access ``` -Run QQ directly: - ```bash python qqapp.py -``` - -Or start it together with the desktop window: - -```bash +# or launch together with the desktop floating window python launch.pyw --qq ``` -Notes: -- Create the bot at [QQ Open Platform](https://q.qq.com) -- In sandbox mode, add your own QQ account to the message list first -- After the first inbound message, the user's openid will be written to `temp/qqapp.log` +> Create a bot at the [QQ Open Platform](https://q.qq.com) to get AppID / AppSecret. After the first message, user openid is logged in `temp/qqapp.log`. -## Feishu / WeCom / DingTalk (Optional) -Feishu: +### Lark (Feishu) ```bash pip install lark-oapi -python fsapp.py -# or -python launch.pyw --feishu +python fsapp.py # or python launch.pyw --feishu ``` -Config keys in `mykey.py` / `mykey.json`: - ```python fs_app_id = "cli_xxx" fs_app_secret = "xxx" fs_allowed_users = ["ou_xxx"] # or ['*'] ``` -Current Feishu support in this repo: -- inbound: text, post rich text, image, file, audio, media, interactive/share cards -- images are sent to multimodal-capable OpenAI-compatible backends as true image inputs on the first turn -- outbound: interactive progress cards, uploaded image replies, uploaded file/media replies +**Inbound support**: text, rich text post, images, files, audio, media, interactive cards / share cards +**Outbound support**: streaming progress cards, image replies, file / media replies +**Vision model**: Images are sent as true multimodal input to OpenAI Vision-compatible backends on the first turn -Detailed setup guide: `assets/SETUP_FEISHU.md` +Full setup: [assets/SETUP_FEISHU.md](assets/SETUP_FEISHU.md) -WeCom: + +### WeCom (Enterprise WeChat) ```bash pip install wecom_aibot_sdk -python wecomapp.py -# or -python launch.pyw --wecom +python wecomapp.py # or python launch.pyw --wecom ``` -Config keys: - ```python wecom_bot_id = "your_bot_id" wecom_secret = "your_bot_secret" -wecom_allowed_users = ["your_user_id"] # or ['*'] -wecom_welcome_message = "Hello" +wecom_allowed_users = ["your_user_id"] +wecom_welcome_message = "Hello, I'm online." ``` -DingTalk: +### DingTalk ```bash pip install dingtalk-stream -python dingtalkapp.py -# or -python launch.pyw --dingtalk +python dingtalkapp.py # or python launch.pyw --dingtalk ``` -Config keys: - ```python dingtalk_client_id = "your_app_key" dingtalk_client_secret = "your_app_secret" dingtalk_allowed_users = ["your_staff_id"] # or ['*'] ``` -**Also runs on Android** — tested successfully on Termux with `python agentmain.py` (CLI frontend): + +### Telegram Bot + +```python +# mykey.py +tg_bot_token = 'YOUR_BOT_TOKEN' +tg_allowed_users = [YOUR_USER_ID] +``` ```bash -# In Termux -cd /sdcard/ga -python agentmain.py +python tgapp.py ``` -Once running, tell the agent: *"Execute web setup SOP to unlock browser tools"* — it handles the rest. See [WELCOME_NEW_USER.md](WELCOME_NEW_USER.md) for the full bootstrap sequence. -## vs. Alternatives +## 📊 Comparison with Similar Tools -| | GenericAgent | OpenClaw | Claude Code | -|---|---|---|---| -| Codebase | ~3,300 lines | ~530,000 lines | Open-source (large) | -| Deploy | `pip install` + API key | Multi-service orchestration | CLI + subscription | -| Browser | Injects into real browser (keeps login state) | Sandboxed/headless | Via MCP plugins | -| OS Control | Keyboard, mouse, vision, ADB | Multi-agent delegation | File + terminal | -| Self-evolution | Grows SOPs & tools autonomously | Plugin ecosystem | Stateless per session | -| Core shipped | 10 .py + 5 SOPs | Hundreds of modules | Rich CLI toolkit | +| Feature | GenericAgent | OpenClaw | Claude Code | +|------|:---:|:---:|:---:| +| **Codebase** | ~3,300 lines | ~530,000 lines | Open-sourced (large) | +| **Deployment** | `pip install` + API Key | Multi-service orchestration | CLI + subscription | +| **Browser Control** | Real browser (session preserved) | Sandbox / headless browser | Via MCP plugin | +| **OS Control** | Mouse/kbd, vision, ADB | Multi-agent delegation | File + terminal | +| **Self-Evolution** | Autonomous skill growth | Plugin ecosystem | Stateless between sessions | +| **Out of the Box** | 10 .py files + 5 skills | Hundreds of modules | Rich CLI toolset | -## How It Works + +## 🧠 How It Works + +GenericAgent accomplishes complex tasks through **Layered Memory × Minimal Toolset × Autonomous Execution Loop**, continuously accumulating experience during execution. + +1️⃣ **Layered Memory System** +> _Memory crystallizes throughout task execution, letting the agent build stable, efficient working patterns over time._ + +- **L0 — Meta Rules**: Core behavioral rules and system constraints of the agent +- **L2 — Global Facts**: Stable knowledge accumulated over long-term operation +- **L3 — Task Skillss**: Workflows for completing specific task types + +2️⃣ **Autonomous Execution Loop** + +> _Perceive environment state → Task reasoning → Execute tools → Write experience to memory → Loop_ + +The entire core loop is just **92 lines of code** (`agent_loop.py`). + +3️⃣ **Minimal Toolset** +> _GenericAgent provides only **7 atomic tools**, forming the foundational capabilities for interacting with the outside world._ + +| Tool | Function | +|------|------| +| `code_run` | Execute arbitrary code | +| `file_read` | Read files | +| `file_write` | Write files | +| `file_patch` | Patch / modify files | +| `web_scan` | Perceive web content | +| `web_execute_js` | Control browser behavior | +| `ask_user` | Human-in-the-loop confirmation | + +4️⃣ **Capability Extension Mechanism** +> _Capable of dynamically creating new tools._ + +Via `code_run`, GenericAgent can dynamically install Python packages, write new scripts, call external APIs, or control hardware at runtime — crystallizing temporary abilities into permanent tools. + +
+ GenericAgent Workflow +
GenericAgent Workflow Diagram +
+ + +## ⭐ Support + +If this project helped you, please consider leaving a **Star!** 🙏 + +You're also welcome to join our **GenericAgent Community Group** for discussion, feedback, and co-building 👏 + +
+ +
+ + + +## 📄 License + +MIT License — see [LICENSE](LICENSE) + + +--- + +## 🌟 项目简介 + +**GenericAgent** 是一个极简、可自我进化的自主 Agent 框架。核心仅 **~3,300 行代码**，通过 **7 个原子工具 + 92 行 Agent Loop**，赋予任意 LLM 对本地计算机的系统级控制能力，覆盖浏览器、终端、文件系统、键鼠输入、屏幕视觉及移动设备。 + +它的设计哲学是：**不预设技能，靠进化获得能力。** + +每解决一个新任务，GenericAgent 就将执行路径自动固化为 Skill，供后续直接调用。使用时间越长，沉淀的技能越多，形成一棵完全属于你、从 3,300 行种子代码生长出来的专属技能树。 + +> **🤖 自举实证** — 本仓库的一切，从安装 Git、`git init` 到每一条 commit message，均由 GenericAgent 自主完成。作者全程未打开过一次终端。 + +## 📋 核心特性 +- **自我进化**: 每次任务自动沉淀 Skill，能力随使用持续增长，形成专属技能树 +- **极简架构**: ~3,300 行核心代码，Agent Loop 仅 92 行，无复杂依赖，部署零负担 +- **强执行力**: 注入真实浏览器（保留登录态），7 个原子工具直接接管系统 +- **高兼容性**: 支持 Claude / Gemini / Kimi 等主流模型，跨平台运行 + +## 🧬 自我进化机制 + +这是 GenericAgent 区别于其他 Agent 框架的根本所在。 ``` -User instruction - ↓ -┌─────────────────────┐ -│ agent_loop.py (92L) │ ← Sense-Think-Act cycle -│ "What do I know? │ -│ What should I do?" │ -└────────┬────────────┘ - ↓ -┌─────────────────────┐ -│ 7 Atomic Tools │ ← All capabilities derive from these -│ code_run │ Execute any Python/PowerShell -│ file_read/write │ Direct disk access -│ file_patch │ Surgical code edits -│ web_scan │ Read live web pages -│ web_execute_js │ Control browser DOM -│ ask_user │ Human-in-the-loop -└────────┬────────────┘ - ↓ -┌─────────────────────┐ -│ Memory System │ ← Persistent across sessions -│ L0: Meta-SOP │ How to manage memory itself -│ L2: Global Facts │ Environment, credentials, paths -│ L3: Task SOPs │ Learned procedures (self-growing) -└─────────────────────┘ +[遇到新任务]-->[自主摸索](安装依赖、编写脚本、调试验证)--> +[将执行路径固化为 Skill]-->[写入记忆层]-->[下次同类任务直接调用] ``` -The agent starts with 7 primitive tools. Through `code_run`, it can install packages, write scripts, and interface with any hardware or API — effectively manufacturing new tools at runtime. +| 你说的一句话 | Agent 第一次做了什么 | 之后每次 | +|---|---|---| +| *"监控股票并提醒我"* | 安装 mootdx → 构建选股流程 → 配置定时任务 → 保存 Skill | **一句话启动** | +| *"用 Gmail 发这个文件"* | 配置 OAuth → 编写发送脚本 → 保存 Skill | **直接可用** | -
-What Ships in the Box +用几周后，你的 Agent 实例将拥有一套任何人都没有的专属技能树，全部从 3,300 行种子代码中生长而来。 -**Core engine** (runs the agent): -- `agent_loop.py` — Sense-Think-Act loop (92 lines) -- `ga.py` — Tool definitions and execution -- `llmcore.py` — LLM communication (multi-backend) -- `agentmain.py` — Session orchestration + -**Interface** (talk to the agent): -- `stapp.py` — Streamlit web UI -- `tgapp.py` — Telegram bot interface -- `fsapp.py` — Feishu bot interface -- `qqapp.py` — QQ bot interface -- `wecomapp.py` — WeCom bot interface -- `dingtalkapp.py` — DingTalk bot interface -- `launch.pyw` — One-click launcher with floating window +#### 🎯 实例展示 -**Infrastructure**: -- `TMWebDriver.py` — Browser injection bridge (not Selenium — injects JS into your real browser via Tampermonkey) -- `simphtml.py` — HTML→text cleaner for web perception +| 🧋 外卖下单 | 📈 量化选股 | +|:---:|:---:| +| Order Tea | Stock Selection | +| *"Order me a milk tea"* — 自动导航外卖 App，选品并完成结账 | *"Find GEM stocks with EXPMA golden cross, turnover > 5%"* — 量化条件筛股 | +| 🌐 自主网页探索 | 💰 支出追踪 | 💬 批量消息 | +| Web Exploration | Alipay Expense | WeChat Batch | +| 自主浏览并定时汇总网页信息 | *"查找近 3 个月超 ¥2K 的支出"* — 通过 ADB 驱动支付宝 | 批量发送微信消息，完整驱动微信客户端 | -**5 Core SOPs** (shipped, version-controlled): -1. `memory_management_sop` — L0 constitution: how the agent manages its own memory -2. `autonomous_operation_sop` — Self-directed task execution -3. `scheduled_task_sop` — Cron-like recurring tasks -4. `web_setup_sop` — Browser environment bootstrap -5. `ljqCtrl_sop` — Desktop physical control (keyboard, mouse, DPI-aware) -Everything else — Gmail integration, WeChat automation, vision APIs, game downloaders, stock analysis workflows — the agent builds and memorizes on its own through use. -
+## 📅 最新动态 + +- **2026-03-:** [发布百万级 Skill 库](https://mp.weixin.qq.com/s/q2gQ7YvWoiAcwxzaiwpuiQ?scene=1&click_id=7) +- **2026-03-08:** [发布以 GenericAgent 为核心的"政务龙虾" Dintal Claw](https://mp.weixin.qq.com/s/eiEhwo-j6S-WpLxgBnNxBg) +- **2026-03-01:** [GenericAgent 被机器之心报道](https://mp.weixin.qq.com/s/uVWpTTF5I1yzAENV_qm7yg) +- **2026-01-11:** GenericAgent V1.0 公开版本发布 --- - +## 🚀 快速开始 -# GenericAgent — 3,300 行代码，完整 OS 级自主控制 - -一个极简自主 Agent 框架。用约 3,300 行 Python，让任意 LLM 获得对你 PC 的物理级控制能力——浏览器、终端、文件系统、键鼠、屏幕视觉、移动设备。 - -不需要 Electron，不需要 Docker，不需要 Mac Mini，不需要 53 万行代码，不需要付费安装服务。 - -## 用起来是什么样的 - -``` -你："帮我读取微信消息" -Agent：安装依赖 → 逆向数据库 → 写读取脚本 → 保存为 SOP -下次：一句话直接调用，零配置。 - -你："帮我监控股票并提醒" -Agent：安装 mootdx → 构建选股工作流 → 设置定时任务 → 保存为 SOP -下次：一句话启动。 - -你："用 Gmail 发这个文件" -Agent：配置 OAuth → 写发送脚本 → 保存为 SOP -下次：直接能用。 -``` - -**自举实证**：本仓库从安装 Git、`git init`、编写 README 到每一条 commit message，全程由 GenericAgent 完成——作者没有打开过一次终端。 - -每个解决过的任务都会变成永久技能。用几周后，你的 Agent 实例会拥有一套独特的技能树——全部从 3,300 行种子代码中生长出来。 - -## 自举哲学 - -多数 Agent 框架以成品形态发布。GenericAgent 以**种子**形态发布。 - -5 个核心 SOP 定义了 Agent 如何思考、记忆和行动。之后的一切能力，由 Agent 在使用中自主发现并记录： - -1. 你让它做一件新事 -2. 它自己摸索方法（安装依赖、写脚本、测试） -3. 把流程保存为新 SOP -4. 下次直接调用 - -Agent 不只是执行——它**学习并记忆**。 - -## 快速开始 +#### 方法一：标准安装 ```bash -# 1. 克隆 +# 1. 克隆仓库 git clone https://github.com/lsdefine/GenericAgent.git cd GenericAgent @@ -312,164 +325,176 @@ pip install streamlit pywebview # 3. 配置 API Key cp mykey_template.py mykey.py -# 编辑 mykey.py 填入你的 LLM API Key +# 编辑 mykey.py，填入你的 LLM API Key # 4. 启动 python launch.pyw ``` -**同样可在 Android 上运行** — 已在 Termux 上测试通过，通过 `python agentmain.py`（CLI 前端）启动： +#### 方法二：Windows 便携版（推荐新手） + +[下载便携版](http://kw.fudan.edu.cn/resources/PC-Agent-Portable.zip)（19MB，解压即用） + +完整引导流程见 [WELCOME_NEW_USER.md](WELCOME_NEW_USER.md)。 + +#### 方法三：Android（Termux） ```bash -# 在 Termux 中 cd /sdcard/ga python agentmain.py ``` -启动后告诉 Agent："执行 web setup SOP 解锁浏览器工具"——剩下的它自己搞定。完整引导流程见 [WELCOME_NEW_USER.md](WELCOME_NEW_USER.md)。 +--- -## QQ Bot（可选） +## 🤖 Bot 接口（可选） -QQ 适配使用 `qq-botpy` 的 WebSocket 长连接，不需要公网 webhook。 +### QQ Bot + +使用 `qq-botpy` WebSocket 长连接，**无需公网 webhook**： ```bash pip install qq-botpy ``` -然后在 `mykey.py` 或 `mykey.json` 中补充： +在 `mykey.py` 中补充： ```python qq_app_id = "YOUR_APP_ID" qq_app_secret = "YOUR_APP_SECRET" -qq_allowed_users = ["YOUR_USER_OPENID"] # 或 ['*'] 表示公开访问 +qq_allowed_users = ["YOUR_USER_OPENID"] # 或 ['*'] 公开访问 ``` -启动方式： - ```bash python qqapp.py -``` - -或和桌面悬浮窗一起启动： - -```bash +# 或与桌面悬浮窗一起启动 python launch.pyw --qq ``` -补充说明： -- 在 [QQ 开放平台](https://q.qq.com) 创建机器人并拿到 `AppID` / `AppSecret` -- 沙箱调试时，先把自己的 QQ 号加入消息列表 -- 首次给机器人发消息后，用户 openid 会记录在 `temp/qqapp.log` 中，便于填入 `qq_allowed_users` +> 在 [QQ 开放平台](https://q.qq.com) 创建机器人获取 AppID / AppSecret。首次消息后，用户 openid 记录于 `temp/qqapp.log`。 -## Feishu / WeCom / DingTalk（可选） - -Feishu： +### 飞书（Lark） ```bash pip install lark-oapi -python fsapp.py -# 或 -python launch.pyw --feishu +python fsapp.py # 或 python launch.pyw --feishu ``` -配置项： - ```python fs_app_id = "cli_xxx" fs_app_secret = "xxx" fs_allowed_users = ["ou_xxx"] # 或 ['*'] ``` -当前仓库里的飞书能力： -- 入站：文本、富文本 post、图片、文件、音频、media、交互卡片/分享卡片 -- 图片首轮会以真正的多模态图片输入发送给支持 OpenAI 兼容视觉的模型后端 -- 出站：流式进度卡片、图片回传、文件或 media 回传 +**入站支持**：文本、富文本 post、图片、文件、音频、media、交互卡片 / 分享卡片 +**出站支持**：流式进度卡片、图片回传、文件 / media 回传 +**视觉模型**：图片首轮以真正的多模态输入发送给兼容 OpenAI Vision 的后端 -详细配置流程见 `assets/SETUP_FEISHU.md` +详细配置见 [assets/SETUP_FEISHU.md](assets/SETUP_FEISHU.md) -WeCom(企业微信)： + +### 企业微信（WeCom） ```bash pip install wecom_aibot_sdk -python wecomapp.py -# 或 -python launch.pyw --wecom +python wecomapp.py # 或 python launch.pyw --wecom ``` -配置项： - ```python wecom_bot_id = "your_bot_id" wecom_secret = "your_bot_secret" -wecom_allowed_users = ["your_user_id"] # 或 ['*'] +wecom_allowed_users = ["your_user_id"] wecom_welcome_message = "你好，我在线上。" ``` -DingTalk(钉钉)： +### 钉钉（DingTalk） ```bash pip install dingtalk-stream -python dingtalkapp.py -# 或 -python launch.pyw --dingtalk +python dingtalkapp.py # 或 python launch.pyw --dingtalk ``` -配置项： - ```python dingtalk_client_id = "your_app_key" dingtalk_client_secret = "your_app_secret" dingtalk_allowed_users = ["your_staff_id"] # 或 ['*'] ``` -## 对比 -| | GenericAgent | OpenClaw | Claude Code | -|---|---|---|---| -| 代码量 | ~3,300 行 | ~530,000 行 | 已开源（体量大） | -| 部署 | `pip install` + API key | 多服务编排 | CLI + 订阅 | -| 浏览器 | 注入真实浏览器（保留登录态） | 沙箱/无头浏览器 | 通过 MCP 插件 | -| OS 控制 | 键鼠、视觉、ADB | 多 Agent 委派 | 文件 + 终端 | -| 自我进化 | 自主生长 SOP 和工具 | 插件生态 | 会话间无状态 | -| 出厂配置 | 10 个 .py + 5 个 SOP | 数百模块 | 丰富 CLI 工具集 | +### Telegram Bot -## 工作原理 +```python +# mykey.py +tg_bot_token = 'YOUR_BOT_TOKEN' +tg_allowed_users = [YOUR_USER_ID] +``` -Agent 拥有 7 个原子工具：`code_run`（执行任意代码）、`file_read/write/patch`（文件操作）、`web_scan`（网页感知）、`web_execute_js`（浏览器控制）、`ask_user`（人机协作）。 +```bash +python tgapp.py +``` -通过 `code_run`，它可以安装任何包、编写任何脚本、对接任何硬件——相当于在运行时制造新工具。学到的流程保存为 SOP，下次直接调用。 -核心循环只有 92 行（`agent_loop.py`）：感知 → 思考 → 行动 → 记忆。 +## 📊 与同类产品对比 -
-出厂清单 +| 特性 | GenericAgent | OpenClaw | Claude Code | +|------|:---:|:---:|:---:| +| **代码量** | ~3,300 行 | ~530,000 行 | 已开源（体量大） | +| **部署方式** | `pip install` + API Key | 多服务编排 | CLI + 订阅 | +| **浏览器控制** | 注入真实浏览器（保留登录态） | 沙箱 / 无头浏览器 | 通过 MCP 插件 | +| **OS 控制** | 键鼠、视觉、ADB | 多 Agent 委派 | 文件 + 终端 | +| **自我进化** | 自主生长 Skill 和工具 | 插件生态 | 会话间无状态 | +| **出厂配置** | 10 个 .py + 5 个 Skills | 数百模块 | 丰富 CLI 工具集 | -**核心引擎**： -- `agent_loop.py` — 感知-思考-行动循环（92 行） -- `ga.py` — 工具定义与执行 -- `llmcore.py` — LLM 通信（多后端） -- `agentmain.py` — 会话编排 -**交互界面**： -- `stapp.py` — Streamlit Web UI -- `tgapp.py` — Telegram 机器人 -- `fsapp.py` — 飞书机器人 -- `qqapp.py` — QQ 机器人 -- `wecomapp.py` — 企业微信机器人 -- `dingtalkapp.py` — 钉钉机器人 -- `launch.pyw` — 一键启动 + 悬浮窗 +## 🧠 工作机制 -**基础设施**： -- `TMWebDriver.py` — 浏览器注入桥接（非 Selenium，通过 Tampermonkey 注入真实浏览器） -- `simphtml.py` — HTML→文本清洗 +GenericAgent 通过**分层记忆 × 最小工具集 × 自主执行循环**完成复杂任务，并在执行过程中持续积累经验。 -**5 个核心 SOP**（出厂自带，版本控制）： -1. `memory_management_sop` — L0 宪法：Agent 如何管理自身记忆 -2. `autonomous_operation_sop` — 自主任务执行 -3. `scheduled_task_sop` — 定时任务 -4. `web_setup_sop` — 浏览器环境引导 -5. `ljqCtrl_sop` — 桌面物理控制（键鼠、DPI 感知） +1️⃣ **分层记忆系统** +> 记忆在任务执行过程中持续沉淀，使 Agent 逐步形成稳定且高效的工作方式 -其余一切——Gmail、微信自动化、视觉 API、游戏下载、股票分析——都是 Agent 在使用中自主构建并记忆的。 -
+- **L0 — 元规则（Meta Rules）**：Agent 的基础行为规则和系统约束 +- **L2 — 全局事实（Global Facts）**：在长期运行过程中积累的稳定知识 +- **L3 — 任务 Skills（Standard Operating Procedure）**：完成特定任务的操作流程 + +2️⃣ **自主执行循环** + +> 感知环境状态 → 任务推理 → 调用工具执行 → 经验写入记忆 → 循环 + +整个核心循环仅 **92 行代码**（`agent_loop.py`）。 + +3️⃣ **最小工具集** +>GenericAgent 仅提供 **7 个原子工具**，构成与外部世界交互的基础能力 + +| 工具 | 功能 | +|------|------| +| `code_run` | 执行任意代码 | +| `file_read` | 读取文件 | +| `file_write` | 写入文件 | +| `file_patch` | 修改文件 | +| `web_scan` | 感知网页内容 | +| `web_execute_js` | 控制浏览器行为 | +| `ask_user` | 人机协作确认 | + +4️⃣ **能力扩展机制** +> 具备动态创建新的工具能力 +> +通过 `code_run`，GenericAgent 可在运行时动态安装 Python 包、编写新脚本、调用外部 API 或控制硬件，将临时能力固化为永久工具。 + +
+ GenericAgent 工作流程 +
GenericAgent 工作流程图 +
+ + +## ⭐ 支持 +如果这个项目对您有帮助，欢迎点一个 **Star!** 🙏 + +同时也欢迎加入我们的**GenericAgent体验交流群**，一起交流、反馈和共建 👏 +
+ +
+ + +## 📄 许可 +MIT License — 详见 [LICENSE](LICENSE) diff --git a/assets/images/bar.jpg b/assets/images/bar.jpg new file mode 100644 index 0000000..46ecf4a Binary files /dev/null and b/assets/images/bar.jpg differ diff --git a/assets/images/logo.jpg b/assets/images/logo.jpg new file mode 100644 index 0000000..86555cb Binary files /dev/null and b/assets/images/logo.jpg differ diff --git a/assets/images/wechat_group.jpg b/assets/images/wechat_group.jpg new file mode 100644 index 0000000..6fc8084 Binary files /dev/null and b/assets/images/wechat_group.jpg differ diff --git a/assets/images/workflow.jpg b/assets/images/workflow.jpg new file mode 100644 index 0000000..3fb3ce9 Binary files /dev/null and b/assets/images/workflow.jpg differ