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🌟 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 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
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
Web Exploration Alipay Expense
Autonomously browses and periodically summarizes web content. "Find expenses over ¥2K in the last 3 months" — Drives Alipay via ADB.

📅 Latest News

🚀 Quick Start

Method 1: Standard Installation

# 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

Method 2: Windows Portable Version (Recommended for beginners)

Download portable version (19MB, unzip and run)

Full guide: WELCOME_NEW_USER.md

Method 3: Android (Termux)

cd /sdcard/ga
python agentmain.py

🤖 Bot Interfaces (Optional)

QQ Bot

Uses qq-botpy WebSocket long connection — no public webhook required:

pip install qq-botpy

Add to mykey.py:

qq_app_id = "YOUR_APP_ID"
qq_app_secret = "YOUR_APP_SECRET"
qq_allowed_users = ["YOUR_USER_OPENID"]  # or ['*'] for public access
python qqapp.py
# or launch together with the desktop floating window
python launch.pyw --qq

Create a bot at the QQ Open Platform to get AppID / AppSecret. After the first message, user openid is logged in temp/qqapp.log.

Lark (Feishu)

pip install lark-oapi
python fsapp.py          # or python launch.pyw --feishu
fs_app_id = "cli_xxx"
fs_app_secret = "xxx"
fs_allowed_users = ["ou_xxx"]  # or ['*']

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

Full setup: assets/SETUP_FEISHU.md

WeCom (Enterprise WeChat)

pip install wecom_aibot_sdk
python wecomapp.py       # or python launch.pyw --wecom
wecom_bot_id = "your_bot_id"
wecom_secret = "your_bot_secret"
wecom_allowed_users = ["your_user_id"]
wecom_welcome_message = "Hello, I'm online."

DingTalk

pip install dingtalk-stream
python dingtalkapp.py    # or python launch.pyw --dingtalk
dingtalk_client_id = "your_app_key"
dingtalk_client_secret = "your_app_secret"
dingtalk_allowed_users = ["your_staff_id"]  # or ['*']

Telegram Bot

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

📊 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.

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

🌟 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。

[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。

🎯 实例展示

🧋 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
Web Exploration Alipay Expense
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

📅 最新动态

🚀 快速开始

方法一：标准安装

# 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

方法二：Windows 便携版（推荐新手）

下载便携版（19MB，解压即用）

完整引导流程见 WELCOME_NEW_USER.md。

方法三：Android（Termux）

cd /sdcard/ga
python agentmain.py

🤖 Bot 接口（可选）

QQ Bot

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

pip install qq-botpy

在 mykey.py 中补充：

qq_app_id = "YOUR_APP_ID"
qq_app_secret = "YOUR_APP_SECRET"
qq_allowed_users = ["YOUR_USER_OPENID"]  # 或 ['*'] 公开访问
python qqapp.py
# 或与桌面悬浮窗一起启动
python launch.pyw --qq

在 QQ 开放平台 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）

pip install lark-oapi
python fsapp.py          # 或 python launch.pyw --feishu
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

企业微信（WeCom）

pip install wecom_aibot_sdk
python wecomapp.py       # 或 python launch.pyw --wecom
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）

pip install dingtalk-stream
python dingtalkapp.py    # 或 python launch.pyw --dingtalk
dingtalk_client_id = "your_app_key"
dingtalk_client_secret = "your_app_secret"
dingtalk_allowed_users = ["your_staff_id"]  # 或 ['*']

Telegram Bot

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

📊 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。

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

About

AI-powered PC agent loop for desktop automation and intelligent task execution

github.com/lsdefine/GenericAgent

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DinTalClaw (GenericAgent) v1.0.0 — One-Click Installer Latest
Mar 7, 2026

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