# pc-agent-loop

**Repository Path**: yngeek/pc-agent-loop

## Basic Information

- **Project Name**: pc-agent-loop
- **Description**: （Advantage AI Agent 实验室，由深圳夸夸菁领科技有限公司与复旦大学知识工场实验室联合成立的科研团队）研发的新型智能体：GenericAgent。
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-03-02
- **Last Updated**: 2026-04-18

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

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

<a href="https://trendshift.io/repositories/25944" target="_blank"><img src="https://trendshift.io/api/badge/repositories/25944" alt="lsdefine%2FGenericAgent | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>

</div>

<p align="center">
  <a href="#english">English</a> | <a href="#chinese">中文</a> | 📄 <a href="assets/GenericAgent_Technical_Report.pdf">Technical Report</a>
</p>

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

**GenericAgent** is a minimal, self-evolving autonomous agent framework. Its core is just **~3K lines of code**. Through **9 atomic tools + a ~100-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 3K 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**: ~3K lines of core code. Agent Loop is ~100 lines. No complex dependencies, zero deployment overhead.
- **Strong Execution**: Injects into a real browser (preserving login sessions). 9 atomic tools take direct control of the system.
- **High Compatibility**: Supports Claude / Gemini / Kimi / MiniMax and other major models. Cross-platform.
- **Token Efficient**: <30K context window — a fraction of the 200K–1M other agents consume. Layered memory ensures the right knowledge is always in scope. Less noise, fewer hallucinations, higher success rate — at a fraction of the cost.


## 🧬 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 3K 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

- **2026-04-11:** Introduced **L4 session archive memory** and scheduler cron integration
- **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)
- **2026-01-16:** 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** | ~3K 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** | A few core files + starter 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
- **L1 — Insight Index**: Minimal memory index for fast routing and recall
- **L2 — Global Facts**: Stable knowledge accumulated over long-term operation
- **L3 — Task Skills / SOPs**: Reusable workflows for completing specific task types
- **L4 — Session Archive**: Archived task records distilled from finished sessions for long-horizon recall

2️⃣ **Autonomous Execution Loop**

> _Perceive environment state → Task reasoning → Execute tools → Write experience to memory → Loop_

The entire core loop is just **~100 lines of code** (`agent_loop.py`).

3️⃣ **Minimal Toolset**
> _GenericAgent provides only **9 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">
  <table>
    <tr>
      <td align="center"><strong>WeChat Group 5</strong><br><img src="assets/images/wechat_group5.jpg" alt="WeChat Group 5 QR Code" width="250"/></td>
      <td align="center"><strong>WeChat Group 6</strong><br><img src="assets/images/wechat_group6.jpg" alt="WeChat Group 6 QR Code" width="250"/></td>
      <td align="center"><strong>WeChat Group 7</strong><br><img src="assets/images/wechat_group7.jpg" alt="WeChat Group 7 QR Code" width="250"/></td>
    </tr>
  </table>
</div>

## 🚩 Friendly Links

Thanks for the support from the LinuxDo community!

[![LinuxDo](https://img.shields.io/badge/社区-LinuxDo-blue?style=for-the-badge)](https://linux.do/)

## 📄 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 **~3K 行代码**，通过 **9 个原子工具 + ~100 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 3K 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**: ~3K 行核心代码，Agent Loop 约百行，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），9 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
- **极致省 Token**: 上下文窗口不到 30K，是其他 Agent（200K–1M）的零头。分层记忆让关键信息始终在场——噪声更少，幻觉更低，成功率反而更高，而成本低一个数量级。

## 🧬 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 3K 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

- **2026-04-11:** 引入 **L4 会话归档记忆**，并接入 scheduler cron 调度
- **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)
- **2026-01-16:** 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)。

📖 新手使用指南（图文版）：[飞书文档](https://my.feishu.cn/wiki/CGrDw0T76iNFuskmwxdcWrpinPb)

---

## 🤖 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** | ~3K 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** | 几个核心文件 + 少量初始 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
- **L1 — 记忆索引（Insight Index）**：极简索引层，用于快速路由与召回
- **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 / SOPs**：完成特定任务类型的可复用流程
- **L4 — 会话归档（Session Archive）**：从已完成任务中提炼出的归档记录，用于长程召回

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 **约百行代码**（`agent_loop.py`）。

3️⃣ **最小工具集**
>GenericAgent 仅提供 **9 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">
  <table>
    <tr>
      <td align="center"><strong>微信群 5</strong><br><img src="assets/images/wechat_group5.jpg" alt="微信群 5 二维码" width="250"/></td>
      <td align="center"><strong>微信群 6</strong><br><img src="assets/images/wechat_group6.jpg" alt="微信群 6 二维码" width="250"/></td>
      <td align="center"><strong>微信群 7</strong><br><img src="assets/images/wechat_group7.jpg" alt="微信群 7 二维码" width="250"/></td>
    </tr>
  </table>
</div>

## 🚩 友情链接

感谢 **LinuxDo** 社区的支持！

[![LinuxDo](https://img.shields.io/badge/社区-LinuxDo-blue?style=for-the-badge)](https://linux.do/)


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

## 📈 Star History

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