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 initto every commit message, was completed autonomously by GenericAgent. The author never opened a terminal once.
- 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.
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.
| 🧋 Food Delivery Order | 📈 Quantitative Stock Screening |
|---|---|
 |
 |
| "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 |
 |
 |
| Autonomously browses and periodically summarizes web content. | "Find expenses over ¥2K in the last 3 months" — Drives Alipay via ADB. |
- 2026-03-10: Released million-scale Skill Library
- 2026-03-08: Released "Dintal Claw" — a GenericAgent-powered government affairs bot
- 2026-03-01: GenericAgent featured by Jiqizhixin (机器之心)
- 2026-01-11: GenericAgent V1.0 public release
# 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.pywDownload portable version (19MB, unzip and run)
Full guide: WELCOME_NEW_USER.md
cd /sdcard/ga
python agentmain.pyUses qq-botpy WebSocket long connection — no public webhook required:
pip install qq-botpyAdd to mykey.py:
qq_app_id = "YOUR_APP_ID"
qq_app_secret = "YOUR_APP_SECRET"
qq_allowed_users = ["YOUR_USER_OPENID"] # or ['*'] for public accesspython qqapp.py
# or launch together with the desktop floating window
python launch.pyw --qqCreate a bot at the QQ Open Platform to get AppID / AppSecret. After the first message, user openid is logged in
temp/qqapp.log.
pip install lark-oapi
python fsapp.py # or python launch.pyw --feishufs_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
pip install wecom_aibot_sdk
python wecomapp.py # or python launch.pyw --wecomwecom_bot_id = "your_bot_id"
wecom_secret = "your_bot_secret"
wecom_allowed_users = ["your_user_id"]
wecom_welcome_message = "Hello, I'm online."pip install dingtalk-stream
python dingtalkapp.py # or python launch.pyw --dingtalkdingtalk_client_id = "your_app_key"
dingtalk_client_secret = "your_app_secret"
dingtalk_allowed_users = ["your_staff_id"] # or ['*']# mykey.py
tg_bot_token = 'YOUR_BOT_TOKEN'
tg_allowed_users = [YOUR_USER_ID]python tgapp.py| 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 |
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 Diagram
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You're also welcome to join our GenericAgent Community Group for discussion, feedback, and co-building 👏
MIT License — see LICENSE
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。
- 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
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。
| 🧋 外卖下单 | 📈 量化选股 |
|---|---|
|
|
| "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 |
|
|
| 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 |
- 2026-03-10: 发布百万级 Skill 库
- 2026-03-08: 发布以 GenericAgent 为核心的"政务龙虾" Dintal Claw
- 2026-03-01: GenericAgent 被机器之心报道
- 2026-01-11: GenericAgent V1.0 公开版本发布
# 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下载便携版（19MB，解压即用）
完整引导流程见 WELCOME_NEW_USER.md。
cd /sdcard/ga
python agentmain.py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。
pip install lark-oapi
python fsapp.py # 或 python launch.pyw --feishufs_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
pip install wecom_aibot_sdk
python wecomapp.py # 或 python launch.pyw --wecomwecom_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。"pip install dingtalk-stream
python dingtalkapp.py # 或 python launch.pyw --dingtalkdingtalk_client_id = "your_app_key"
dingtalk_client_secret = "your_app_secret"
dingtalk_allowed_users = ["your_staff_id"] # 或 ['*']# mykey.py
tg_bot_token = 'YOUR_BOT_TOKEN'
tg_allowed_users = [YOUR_USER_ID]python tgapp.py| 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 |
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 工作流程图
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 👏
MIT License — 详见 LICENSE