278 lines
10 KiB
Markdown
278 lines
10 KiB
Markdown
# GenericAgent — 3,300 Lines to Full OS Autonomy
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[English](#english) | [中文](#chinese)
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<a name="english"></a>
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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.
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No Electron. No Docker. No Mac Mini. No 500K-line codebase. No paid installation service.
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## See It in Action
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<p align="center">
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<img src="assets/demo/order_tea.gif" alt="Agent ordering milk tea" width="600">
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<br><em>"Order me a milk tea" — the agent navigates a delivery app, picks items, and checks out, fully autonomously.</em>
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</p>
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<table>
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<tr>
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<td width="60%"><img src="assets/demo/autonomous_explore.png" width="100%"><br><em>Autonomous quantitative analysis — the agent explores data sources and generates reports on its own schedule.</em></td>
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<td width="40%"><img src="assets/demo/wechat_batch.png" width="100%"><br><em>WeChat batch messaging — yes, it can drive WeChat too.</em></td>
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</tr>
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</table>
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## What Happens When You Use It
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```
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You: "Read my WeChat messages"
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Agent: installs dependencies → reverse-engineers DB → writes reader script → saves as SOP
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Next time: instant recall, zero setup.
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You: "Monitor stock prices and alert me"
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Agent: installs mootdx → builds screening workflow → sets up scheduled task → saves as SOP
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Next time: one sentence to run.
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You: "Send this file via Gmail"
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Agent: configures OAuth → writes send script → saves as SOP
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Next time: just works.
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```
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**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.
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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.
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## The Seed Philosophy
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Most agent frameworks ship as finished products. GenericAgent ships as a **seed**.
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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:
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1. You ask it to do something new
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2. It figures out how (install dependencies, write scripts, test)
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3. It saves the procedure as a new SOP in its memory
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4. Next time, it recalls and executes directly
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The agent doesn't just execute — it **learns and remembers**.
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## Quick Start
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```bash
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# 1. Clone
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git clone https://github.com/lsdefine/pc-agent-loop.git
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cd pc-agent-loop
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# 2. Install minimal deps
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pip install streamlit pywebview
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# 3. Configure API key
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cp mykey_template.py mykey.py
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# Edit mykey.py with your LLM API key
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# 4. Launch
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python launch.pyw
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```
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**Also runs on Android** — tested successfully on Termux with `python agentmain.py` (CLI frontend):
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```bash
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# In Termux
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cd /sdcard/ga
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python agentmain.py
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```
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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.
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## vs. Alternatives
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| | GenericAgent | OpenClaw | Claude Code |
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| Codebase | ~3,300 lines | ~530,000 lines | Open-source (large) |
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| Deploy | `pip install` + API key | Multi-service orchestration | CLI + subscription |
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| Browser | Injects into real browser (keeps login state) | Sandboxed/headless | Via MCP plugins |
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| OS Control | Keyboard, mouse, vision, ADB | Multi-agent delegation | File + terminal |
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| Self-evolution | Grows SOPs & tools autonomously | Plugin ecosystem | Stateless per session |
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| Core shipped | 10 .py + 5 SOPs | Hundreds of modules | Rich CLI toolkit |
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## How It Works
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```
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User instruction
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↓
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┌─────────────────────┐
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│ agent_loop.py (92L) │ ← Sense-Think-Act cycle
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│ "What do I know? │
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│ What should I do?" │
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└────────┬────────────┘
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↓
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┌─────────────────────┐
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│ 7 Atomic Tools │ ← All capabilities derive from these
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│ code_run │ Execute any Python/PowerShell
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│ file_read/write │ Direct disk access
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│ file_patch │ Surgical code edits
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│ web_scan │ Read live web pages
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│ web_execute_js │ Control browser DOM
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│ ask_user │ Human-in-the-loop
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└────────┬────────────┘
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↓
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┌─────────────────────┐
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│ Memory System │ ← Persistent across sessions
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│ L0: Meta-SOP │ How to manage memory itself
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│ L2: Global Facts │ Environment, credentials, paths
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│ L3: Task SOPs │ Learned procedures (self-growing)
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└─────────────────────┘
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```
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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.
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<details>
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<summary>What Ships in the Box</summary>
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**Core engine** (runs the agent):
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- `agent_loop.py` — Sense-Think-Act loop (92 lines)
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- `ga.py` — Tool definitions and execution
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- `sidercall.py` — LLM communication (multi-backend)
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- `agentmain.py` — Session orchestration
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**Interface** (talk to the agent):
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- `stapp.py` — Streamlit web UI
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- `tgapp.py` — Telegram bot interface
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- `launch.pyw` — One-click launcher with floating window
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**Infrastructure**:
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- `TMWebDriver.py` — Browser injection bridge (not Selenium — injects JS into your real browser via Tampermonkey)
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- `simphtml.py` — HTML→text cleaner for web perception
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**5 Core SOPs** (shipped, version-controlled):
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1. `memory_management_sop` — L0 constitution: how the agent manages its own memory
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2. `autonomous_operation_sop` — Self-directed task execution
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3. `scheduled_task_sop` — Cron-like recurring tasks
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4. `web_setup_sop` — Browser environment bootstrap
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5. `ljqCtrl_sop` — Desktop physical control (keyboard, mouse, DPI-aware)
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Everything else — Gmail integration, WeChat automation, vision APIs, game downloaders, stock analysis workflows — the agent builds and memorizes on its own through use.
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</details>
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---
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<a name="chinese"></a>
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# GenericAgent — 3,300 行代码，完整 OS 级自主控制
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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。
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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。
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## 用起来是什么样的
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```
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你："帮我读取微信消息"
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Agent：安装依赖 → 逆向数据库 → 写读取脚本 → 保存为 SOP
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下次：一句话直接调用，零配置。
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你："帮我监控股票并提醒"
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Agent：安装 mootdx → 构建选股工作流 → 设置定时任务 → 保存为 SOP
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下次：一句话启动。
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你："用 Gmail 发这个文件"
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Agent：配置 OAuth → 写发送脚本 → 保存为 SOP
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下次：直接能用。
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```
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**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。
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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。
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## 自举哲学
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多数 Agent 框架以成品形态发布。GenericAgent 以**种子**形态发布。
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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：
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1. 你让它做一件新事
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2. 它自己摸索方法（安装依赖、写脚本、测试）
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3. 把流程保存为新 SOP
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4. 下次直接调用
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Agent 不只是执行——它**学习并记忆**。
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## 快速开始
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```bash
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# 1. 克隆
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git clone https://github.com/lsdefine/pc-agent-loop.git
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cd pc-agent-loop
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# 2. 安装最小依赖
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pip install streamlit pywebview
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# 3. 配置 API Key
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cp mykey_template.py mykey.py
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# 编辑 mykey.py 填入你的 LLM API Key
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# 4. 启动
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python launch.pyw
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```
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**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：
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```bash
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# 在 Termux 中
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cd /sdcard/ga
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python agentmain.py
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```
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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)。
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## 对比
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| | GenericAgent | OpenClaw | Claude Code |
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| 代码量 | ~3,300 行 | ~530,000 行 | 已开源（体量大） |
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| 部署 | `pip install` + API key | 多服务编排 | CLI + 订阅 |
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| 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 |
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| OS 控制 | 键鼠、视觉、ADB | 多 Agent 委派 | 文件 + 终端 |
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| 自我进化 | 自主生长 SOP 和工具 | 插件生态 | 会话间无状态 |
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| 出厂配置 | 10 个 .py + 5 个 SOP | 数百模块 | 丰富 CLI 工具集 |
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## 工作原理
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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）。
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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。
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核心循环只有 92 行（`agent_loop.py`）：感知 → 思考 → 行动 → 记忆。
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<details>
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<summary>出厂清单</summary>
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**核心引擎**：
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- `agent_loop.py` — 感知-思考-行动循环（92 行）
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- `ga.py` — 工具定义与执行
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- `sidercall.py` — LLM 通信（多后端）
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- `agentmain.py` — 会话编排
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**交互界面**：
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- `stapp.py` — Streamlit Web UI
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- `tgapp.py` — Telegram 机器人
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- `launch.pyw` — 一键启动 + 悬浮窗
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**基础设施**：
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- `TMWebDriver.py` — 浏览器注入桥接（非 Selenium，通过 Tampermonkey 注入真实浏览器）
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- `simphtml.py` — HTML→文本清洗
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**5 个核心 SOP**（出厂自带，版本控制）：
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1. `memory_management_sop` — L0 宪法：Agent 如何管理自身记忆
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2. `autonomous_operation_sop` — 自主任务执行
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3. `scheduled_task_sop` — 定时任务
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4. `web_setup_sop` — 浏览器环境引导
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5. `ljqCtrl_sop` — 桌面物理控制（键鼠、DPI 感知）
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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。
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</details>
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## 许可
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MIT |