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<img src="assets/images/bar.jpg" width="880"/>
</div>

<p align="center">
  <a href="#english">English</a> | <a href="#chinese">中文</a>
</p>

---
<a name="english"></a>
## 🌟 Overview

**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).

Its design philosophy: **don't preload skills — evolve them.**

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.

> **🤖 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.

## 📋 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 / MiniMax and other major models. Cross-platform.


## 🧬 Self-Evolution Mechanism

This is what fundamentally distinguishes GenericAgent from every other agent framework.

```
[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]
```

| 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** |

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.


##### 🎯 Demo Showcase

| 🧋 Food Delivery Order | 📈 Quantitative Stock Screening |
|:---:|:---:|
| <img src="assets/demo/order_tea.gif" width="100%" alt="Order Tea"> | <img src="assets/demo/selectstock.gif" width="100%" alt="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 |
| <img src="assets/demo/autonomous_explore.png" width="100%" alt="Web Exploration"> | <img src="assets/demo/alipay_expense.png" width="100%" alt="Alipay Expense"> | <img src="assets/demo/wechat_batch.png" width="100%" alt="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. |

## 📅 Latest News

- **2026-03-23:** Support personal WeChat as a bot frontend
- **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

#### Method 1: Standard Installation

```bash
# 1. Clone the repo
git clone https://github.com/lsdefine/GenericAgent.git
cd GenericAgent

# 2. Install minimal dependencies
pip install streamlit pywebview

# 3. Configure API Key
cp mykey_template.py mykey.py
# Edit mykey.py and fill in your LLM API Key

# 4. Launch
python launch.pyw
```

Full guide: [GETTING_STARTED.md](GETTING_STARTED.md)

---

## 🤖 Bot Interface (Optional)

### Telegram Bot

```python
# mykey.py
tg_bot_token = 'YOUR_BOT_TOKEN'
tg_allowed_users = [YOUR_USER_ID]
```

```bash
python frontends/tgapp.py
```

### Alternative App Frontends

Besides the default Streamlit web UI, you can also try other frontend styles:

```bash
python frontends/qtapp.py                # Qt-based desktop app
streamlit run frontends/stapp2.py        # Alternative Streamlit UI
```


## 📊 Comparison with Similar Tools

| 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

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 |

> Additionally, 2 **memory management tools** (`update_working_checkpoint`, `start_long_term_update`) allow the agent to persist context and accumulate experience across sessions.

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.

<div align="center">
  <img src="assets/images/workflow.jpg" alt="GenericAgent Workflow" width="400"/>
  <br><em>GenericAgent Workflow Diagram</em>
</div>


## ⭐ 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 👏

<div align="center">
<img src="assets/images/wechat_group.jpg" width="280"/>
</div>



## 📄 License

MIT License — see [LICENSE](LICENSE)


---
<a name="chinese"></a>
## 🌟 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 / MiniMax 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。

```
[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]
```

| 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** |

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。

<!-- | *"帮我读取微信消息"* | 安装依赖 → 逆向数据库 → 写读取脚本 → 保存 Skill | **一句话调用** | -->

#### 🎯 实例展示

| 🧋 外卖下单 | 📈 量化选股 |
|:---:|:---:|
| <img src="assets/demo/order_tea.gif" width="100%" alt="Order Tea"> | <img src="assets/demo/selectstock.gif" width="100%" alt="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 |
| <img src="assets/demo/autonomous_explore.png" width="100%" alt="Web Exploration"> | <img src="assets/demo/alipay_expense.png" width="100%" alt="Alipay Expense"> | <img src="assets/demo/wechat_batch.png" width="100%" alt="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 |



## 📅 最新动态

- **2026-03-23:** 支持个人微信接入作为 Bot 前端
- **2026-03-10:** [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

#### 方法一：标准安装

```bash
# 1. 克隆仓库
git clone https://github.com/lsdefine/GenericAgent.git
cd GenericAgent

# 2. 安装最小依赖
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

# 4. 启动
python launch.pyw
```

完整引导流程见 [GETTING_STARTED.md](GETTING_STARTED.md)。

---

## 🤖 Bot 接口（可选）

### 微信 Bot（个人微信）

无需额外配置，扫码登录即可：

```bash
pip install pycryptodome qrcode requests
python frontends/wechatapp.py
```

> 首次启动会弹出二维码，用微信扫码完成绑定。之后通过微信消息与 Agent 交互。

### QQ Bot

使用 `qq-botpy` WebSocket 长连接，**无需公网 webhook**：

```bash
pip install qq-botpy
```

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 ['*'] 公开访问
```

```bash
python frontends/qqapp.py
```

> 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`。

### 飞书（Lark）

```bash
pip install lark-oapi
python frontends/fsapp.py
```

```python
fs_app_id = "cli_xxx"
fs_app_secret = "xxx"
fs_allowed_users = ["ou_xxx"]  # 或 ['*']
```

**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

详细配置见 [assets/SETUP_FEISHU.md](assets/SETUP_FEISHU.md)


### 企业微信（WeCom）

```bash
pip install wecom_aibot_sdk
python frontends/wecomapp.py
```

```python
wecom_bot_id = "your_bot_id"
wecom_secret = "your_bot_secret"
wecom_allowed_users = ["your_user_id"]
wecom_welcome_message = "你好，我在线上。"
```

### 钉钉（DingTalk）

```bash
pip install dingtalk-stream
python frontends/dingtalkapp.py
```

```python
dingtalk_client_id = "your_app_key"
dingtalk_client_secret = "your_app_secret"
dingtalk_allowed_users = ["your_staff_id"]  # 或 ['*']
```

### 其他 App 前端

除默认的 Streamlit Web UI 外，还可以尝试不同风格的前端：

```bash
python frontends/qtapp.py                # 基于 Qt 的桌面应用
streamlit run frontends/stapp2.py        # 另一种 Streamlit 风格 UI
```


## 📊 与同类产品对比

| 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 |


## 🧠 工作机制

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。

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


- **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 |

> 此外，还有 2 个**记忆管理工具**（`update_working_checkpoint`、`start_long_term_update`），使 Agent 能够跨会话积累经验、维持持久上下文。

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。

<div align="center">
  <img src="assets/images/workflow.jpg" alt="GenericAgent 工作流程" width="400"/>
  <br><em>GenericAgent 工作流程图</em>
</div>


## ⭐ 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 👏
<div align="center">
<img src="assets/images/wechat_group.jpg" width="280"/>
</div>


## 📄 许可
MIT License — 详见 [LICENSE](LICENSE)
