https://github.com/lsdefine/GenericAgent
Self-evolving agent: grows skill tree from 3.3K-line seed, achieving full system control with 6x less token consumption
https://github.com/lsdefine/GenericAgent
ai-agent automation autonomous-agent browser-automation claude computer-control desktop-automation gemini lightweight llm-agent memory-system python self-evolving skill-tree task-automation
Last synced: 1 day ago
JSON representation
Self-evolving agent: grows skill tree from 3.3K-line seed, achieving full system control with 6x less token consumption
- Host: GitHub
- URL: https://github.com/lsdefine/GenericAgent
- Owner: lsdefine
- License: mit
- Created: 2026-01-16T15:45:24.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2026-04-28T04:01:07.000Z (3 days ago)
- Last Synced: 2026-04-28T06:09:24.187Z (2 days ago)
- Topics: ai-agent, automation, autonomous-agent, browser-automation, claude, computer-control, desktop-automation, gemini, lightweight, llm-agent, memory-system, python, self-evolving, skill-tree, task-automation
- Language: Python
- Homepage: https://github.com/lsdefine/GenericAgent
- Size: 24.7 MB
- Stars: 7,823
- Watchers: 22
- Forks: 901
- Open Issues: 38
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- awesome-ai-agents - lsdefine/GenericAgent - GenericAgent is a minimal, self-evolving autonomous AI agent framework (~3K lines of code) enabling LLMs to control a local computer and grow a personalized skill tree. (Personal Assistants & Conversational Agents / Chatbots)
- awesome-github-projects - GenericAgent - Self-evolving agent: grows skill tree from 3.3K-line seed, achieving full system control with 6x less token consumption ⭐8,235 `Python` 🔥 (🤖 AI & Machine Learning)
- awesome-ai-agents - GenericAgent - evolving agent that grows its own skill tree from ~3K lines of seed code. 9 atomic tools for full system control (browser, terminal, filesystem, screen vision) with automatic skill crystallization. (Frameworks & Libraries / Single Agent)
README
English | 中文 | 📄 Technical Report: 
| 📘 教程
**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 |
|:---:|:---:|
| 
| 
|
| *"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 |
| 
| 
| 
|
| 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
- **2026-04-21:** 📄 [Technical Report released on arXiv](https://arxiv.org/abs/2604.17091) — *GenericAgent: A Token-Efficient Self-Evolving LLM Agent via Contextual Information Density Maximization*
- **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 requests 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: uv (for experienced Python users)
If you prefer a modern Python workflow, GenericAgent also provides a minimal `pyproject.toml`:
```bash
git clone https://github.com/lsdefine/GenericAgent.git
cd GenericAgent
uv pip install -e ".[ui]" # Core + GUI dependencies
cp mykey_template.py mykey.py
python launch.pyw
```
> GenericAgent is meant to grow its environment through the Agent itself, not by pre-installing every possible package.
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
```
### Common Chat Commands
The default Streamlit desktop UI started by `python launch.pyw`, plus the QQ / Telegram / Feishu / WeCom / DingTalk frontends, support these chat commands:
- `/new` - start a fresh conversation and clear the current context
- `/continue` - list recoverable conversation snapshots
- `/continue N` - restore the `N`th recoverable conversation
## 📊 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.
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 👏
WeChat Group 8
WeChat Group 9
WeChat Group 11
## 🚩 Friendly Links
Thanks for the support from the LinuxDo community!
[](https://linux.do/)
## 📄 License
MIT License — see [LICENSE](LICENSE)
*Disclaimer: This project does not build or operate any commercial website. Apart from DintalClaw, no institution, organization, or individual is currently officially authorized to conduct commercial activities under the GenericAgent name.*
**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。
#### 🎯 实例展示
| 🧋 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 | 💬 批量消息 |
| | | |
| 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
- **2026-04-21:** 📄 [技术报告已发布至 arXiv](https://arxiv.org/abs/2604.17091) — *GenericAgent: A Token-Efficient Self-Evolving LLM Agent via Contextual Information Density Maximization*
- **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 requests 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：uv 快速安装（熟悉 Python 的用户）
如果你习惯现代 Python 工作流，GenericAgent 也提供了一个最小化的 `pyproject.toml`：
```bash
git clone https://github.com/lsdefine/GenericAgent.git
cd GenericAgent
uv pip install -e ".[ui]" # 核心 + GUI 依赖
cp mykey_template.py mykey.py
python launch.pyw
```
> GenericAgent 更推荐由 Agent 在使用中自举环境，而不是预先手动装完整依赖。
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)
📘 完整入门教程（Datawhale 出品）：[Hello GenericAgent](https://datawhalechina.github.io/hello-generic-agent/) · [GitHub](https://github.com/datawhalechina/hello-generic-agent)
---
## 🤖 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
```
### 通用聊天命令
默认通过 `python launch.pyw` 启动的 Streamlit 桌面 UI，以及 QQ / Telegram / 飞书 / 企业微信 / 钉钉前端，都支持以下命令：
- `/new` - 开启新对话并清空当前上下文
- `/continue` - 列出可恢复会话快照
- `/continue N` - 恢复第 `N` 个可恢复会话
## 📊 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。
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 👏
微信群 9
微信群 11
微信群 12
## 🚩 友情链接
感谢 **LinuxDo** 社区的支持！
[](https://linux.do/)
## 📄 许可
MIT License — 详见 [LICENSE](LICENSE)
*声明：本项目未构建任何商业站点；除 DintalClaw 外，目前未官方授权任何机构、组织或个人以 GenericAgent 名义从事商业活动。*
## 📈 Star History
