# GenericAgent **A Minimal, Self-Evolving Autonomous Agent Framework** *~3K lines of seed code · 9 atomic tools · ~100-line Agent Loop*

**[English](#-english) · [中文](#-中文)**

> 📌 **Official Channel** — This GitHub repository is the **only** official source of GenericAgent. > We have no affiliation with any third-party website using the GenericAgent name. --- ## 🌟 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). > Design philosophy — **don't preload skills, evolve them.** Every time GenericAgent solves a new task, it automatically crystallizes the execution path into a reusable **Skill**. The longer you use it, the more skills accumulate — forming a personal skill tree grown entirely 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. ### 📑 Table of Contents - [Key Features](#-key-features) - [Demo Showcase](#-demo-showcase) - [Quick Start](#-quick-start) - [Usage](#-usage) - [Architecture](#-architecture) - [Self-Evolution Mechanism](#-self-evolution-mechanism) - [Comparison](#-comparison) - [Evaluation](#-evaluation) - [Roadmap & News](#-roadmap--news) - [Community & Support](#-community--support) - [License](#-license) --- ## 📋 Key Features | Feature | Description | | :--- | :--- | | 🧬 **Self-Evolving** | Automatically crystallizes each task into a 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. Less noise, fewer hallucinations, higher success rate, lower cost. | --- ## 🎯 Demo Showcase

🧋 Food Delivery Order	📈 Quantitative Stock Screening

_{"Order me a milk tea" — navigates the delivery app, selects items, completes checkout.}	_{"Find GEM stocks with EXPMA golden cross, turnover > 5%" — quantitative screening.}
🌐 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.}
💬 Batch Messaging

_{Sends bulk WeChat messages, fully driving the WeChat client.}

--- ## 🚀 Quick Start > ⚠️ **Python version**: use **Python 3.11 or 3.12**. **Do not** use Python 3.14 — it is incompatible with `pywebview` and a few other GA dependencies. > > 📖 Detailed installation guide: **[installation.md](docs/installation.md)** · **[installation_zh.md（中文）](docs/installation_zh.md)** ### For LLM Agents Fetch the installation guide and follow it: ```bash curl -fsSL https://raw.githubusercontent.com/lsdefine/GenericAgent/refs/heads/main/docs/installation.md ``` ### For Humans #### Method 1 — One-line install *(recommended)* This installs GenericAgent with an isolated Python environment and Git, then downloads a ready-to-run package. **Windows PowerShell** ```powershell powershell -ExecutionPolicy Bypass -c "$env:GLOBAL=1; irm http://fudankw.cn:9000/files/ga_install.ps1 | iex" ``` **Linux / macOS** ```bash GLOBAL=1 bash -c "$(curl -fsSL http://fudankw.cn:9000/files/ga_install.sh)" ``` After installation, launch the desktop app from: ```text frontends/GenericAgent.exe ``` #### Method 2 — Python install *(for developers)* ```bash git clone https://github.com/lsdefine/GenericAgent.git cd GenericAgent uv venv uv pip install -e ".[ui]" # Core + UI dependencies cp mykey_template.py mykey.py # Fill in your LLM API key python launch.pyw ``` > 💡 GenericAgent is meant to grow its environment **through the Agent itself**, not by pre-installing every possible package. 📖 Full guide: [`docs/GETTING_STARTED.md`](docs/GETTING_STARTED.md) --- ## 💻 Usage ### Frontends #### Desktop App For one-line installs on Windows, double-click: ```text frontends/GenericAgent.exe ``` #### Terminal UI A lightweight, keyboard-driven interface built on [Textual](https://github.com/Textualize/textual). Supports multiple concurrent sessions and real-time streaming. ```bash python frontends/tuiapp_v2.py ```

⚠️ Windows TUI Troubleshooting

TUI rendering on Windows can be flaky depending on terminal + font. Common causes: 1. `textual` is not on the latest version — `pip install -U textual` first. 2. PowerShell / cmd ship with terminals that have rough Unicode + key-binding support. **Prefer Git Bash on Windows**, which is much better behaved. 3. If it still looks broken, ask GA itself to fix it: > *"My experience using `frontends/tuiapp_v2.py` in PowerShell / cmd / Git Bash on Windows is very poor — lots of incompatibility. Please refer to Claude Code's best practices for the Windows terminal and fix all font and rendering incompatibilities."*

#### Streamlit UI ```bash python launch.pyw ``` ### Bot Interface (IM) GenericAgent also supports IM frontends such as Telegram, WeChat, QQ, Feishu / Lark, WeCom, and DingTalk. | Platform | Command | | :--- | :--- | | Telegram | `python frontends/tgapp.py` | | WeChat | `python frontends/wechatapp.py` | | QQ | `python frontends/qqapp.py` | | Feishu / Lark | `python frontends/fsapp.py` | | WeCom | `python frontends/wecomapp.py` | | DingTalk | `python frontends/dingtalkapp.py` | > For detailed setup, ask GenericAgent itself. ### Common Chat Commands | Command | Description | | :--- | :--- | | `/new` | Start a fresh conversation and clear the current context | | `/continue` | List recoverable conversation snapshots | | `/continue N` | Restore the `N`-th recoverable conversation | --- ## 🧠 Architecture 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.* | Layer | Name | Description | | :---: | :--- | :--- | | **L0** | Meta Rules | Core behavioral rules and system constraints | | **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`](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 (Python / PowerShell) | | `file_read` | Read files | | `file_write` | Write / create / overwrite files | | `file_patch` | Patch / modify files | | `web_scan` | Perceive web content | | `web_execute_js` | Control browser behavior | | `ask_user` | Human-in-the-loop confirmation | | `update_working_checkpoint` | *(memory)* Short-term working notepad | | `start_long_term_update` | *(memory)* Distill long-term memory | ### 4️⃣ Capability Extension > *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

--- ## 🧬 Self-Evolution Mechanism This is what fundamentally distinguishes GenericAgent from every other agent framework. ```text [New Task] │ ▼ [Autonomous Exploration] ─► install deps · write scripts · debug · verify │ ▼ [Crystallize into Skill] ─► write to memory layer │ ▼ [Direct Recall on Next Similar Task] ``` | What you say | 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. --- ## 📊 Comparison | 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** | Few core files + starter skills | Hundreds of modules | Rich CLI toolset | --- ## 📈 Evaluation > 📂 Full evaluation datasets and results: [**JinyiHan99/GA-Technical-Report**](https://github.com/JinyiHan99/GA-Technical-Report/tree/main) We evaluate GenericAgent across **five dimensions**: | # | Dimension | Question | Benchmarks | | :---: | :--- | :--- | :--- | | 1 | **Task Completion & Token Efficiency** | Can GA complete hard tasks more cheaply than leading agents? | SOP-Bench, Lifelong AgentBench, RealFin-Benchmark | | 2 | **Tool-Use Efficiency** | Can a minimal atomic toolset solve what specialized toolsets solve, with less overhead? | Tool Efficiency Benchmark (11 simple + 5 long-horizon) | | 3 | **Memory System Effectiveness** | Does condensed hierarchical memory beat full/redundant memory and embedding-based retrievers? | SOP-Bench (dangerous goods), LoCoMo, 20-skill stress test | | 4 | **Self-Evolution Capability** | Can the agent distill experience into reusable SOPs and code, without intervention? | 9-round LangChain longitudinal study, 8-task cross-task web benchmark | | 5 | **Web Browsing Capability** | Does density-driven design survive the open web? | WebCanvas, BrowseComp-ZH, Custom Tasks (22) | Baselines across these dimensions include **Claude Code**, **OpenAI CodeX**, and **OpenClaw**, evaluated under *Claude Sonnet 4.6*, *Claude Opus 4.6*, *GPT-5.4*, and *MiniMax M2.7* backbones.

_{Tool-use efficiency radar. GA dominates token, request, and tool-call axes while preserving quality across four task dimensions.}

_{Cross-task self-evolution. Second- and third-run GA executions converge to a stable low-cost regime across eight web tasks, while OpenClaw shows no such convergence.}

--- ## 📅 Roadmap & News - **2026-05-15** — 🖥️ **Desktop GUI released**. One-line installs ship a ready-to-run desktop app (`frontends/GenericAgent.exe`). Developers launch via `python launch.pyw`. - **2026-05-14** — 🆕 **Conductor sub-agent orchestration**. Spawn, supervise, and auto-clean parallel sub-agents; first-class delegation primitives complementing `/btw` side-questions. - **2026-05-12** — 🆕 **TUI v2 released** (`frontends/tuiapp_v2.py`). Refined Textual frontend with image-paste folding, file paste, block-delete, Ctrl+C copy, history navigation, and `/llm` / `/export` / `/continue` pickers. - **2026-04-21** — 📄 [**Technical Report 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** — Personal WeChat supported 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** — [Featured by Jiqizhixin (机器之心)](https://mp.weixin.qq.com/s/uVWpTTF5I1yzAENV_qm7yg). - **2026-01-16** — GenericAgent **V1.0** public release. --- ## ⭐ Community & Support If this project helped you, please consider leaving a **Star!** 🙏 You're also welcome to join the **GenericAgent Community Group** for discussion, feedback, and co-building 👏

WeChat Group 19
WeChat Group 19 QR

### 🚩 Friendly Links Thanks to the **LinuxDo** community for the support! [![LinuxDo](https://img.shields.io/badge/Community-LinuxDo-blue?style=for-the-badge)](https://linux.do/) **Community GUIs** *(independent open-source projects)*: - [chilishark27/ga-manager](https://github.com/chilishark27/ga-manager) - [wangjc683/galley](https://github.com/wangjc683/galley) --- ## 📄 License Distributed under the **MIT License**. See [`LICENSE`](LICENSE) for full text. > *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.* --- ## 🌟 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（ADB）。 > 设计哲学 —— **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。 ### 📑 目录 - [核心特性](#-核心特性) - [实例展示](#-实例展示) - [快速开始](#-快速开始) - [使用方式](#-使用方式) - [架构设计](#-架构设计) - [自我进化机制](#-自我进化机制) - [与同类产品对比](#-与同类产品对比) - [评测](#-评测) - [路线图与最新动态](#-路线图与最新动态) - [社区与支持](#-社区与支持) - [许可](#-许可) --- ## 📋 核心特性 | 特性 | 说明 | | :--- | :--- | | 🧬 **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；噪声更少、幻觉更低、成功率更高，成本低一个数量级 | --- ## 🎯 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

_{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}
💬 批量消息

_{批量发送微信消息，完整驱动微信客户端}

--- ## 🚀 快速开始 > ⚠️ **Python 版本：** 推荐使用 **Python 3.11 或 3.12**。**请不要使用 Python 3.14**，与 `pywebview` 及部分依赖不兼容。 > > 📖 详细安装指南：**[installation_zh.md（中文）](docs/installation_zh.md)** · **[installation.md (English)](docs/installation.md)** ### 给 LLM Agent 看的获取安装指南并照做： ```bash curl -fsSL https://raw.githubusercontent.com/lsdefine/GenericAgent/refs/heads/main/docs/installation_zh.md ``` ### 给人类用户看的 #### 方法一 — 一键安装 *（推荐）* 一键安装会自动准备独立 Python 环境、Git、项目文件和桌面端，不污染系统环境。 **Windows PowerShell** ```powershell powershell -ExecutionPolicy Bypass -c "irm http://fudankw.cn:9000/files/ga_install.ps1 | iex" ``` **Linux / macOS** ```bash curl -fsSL http://fudankw.cn:9000/files/ga_install.sh | bash ``` 安装完成后，双击启动： ```text frontends/GenericAgent.exe ``` #### 方法二 — Python 安装 *（开发者）* ```bash git clone https://github.com/lsdefine/GenericAgent.git cd GenericAgent uv venv uv pip install -e ".[ui]" # 核心 + UI 依赖 cp mykey_template.py mykey.py # 填入你的 LLM API Key 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 [`docs/GETTING_STARTED.md`](docs/GETTING_STARTED.md) 📖 新手图文版：[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) --- ## 💻 使用方式 ### 前端启动 #### 桌面端一键安装自带桌面端，双击： ```text frontends/GenericAgent.exe ``` #### 终端 UI 基于 [Textual](https://github.com/Textualize/textual) 的轻量键盘驱动界面。支持多会话并发、实时流式输出，有终端就能跑。 ```bash python frontends/tuiapp_v2.py ```

⚠️ Windows 上 TUI 显示异常的排查思路

1. `textual` 版本太旧，先 `pip install -U textual`； 2. PowerShell / cmd 自带终端对 Unicode 和键位的支持比较糟糕，**Windows 上推荐用 Git Bash**，体验明显更稳； 3. 仍然显示异常时，可以让 GA 自己修一遍，参考 Prompt： > *"我在 Windows 的 PowerShell / cmd / Git Bash 中使用 `frontends/tuiapp_v2.py` 体验非常差，出现了一堆不兼容问题。请参考 Claude Code 在 Windows 终端的最佳配置，把所有字体和显示不兼容的问题修一遍。"*

#### Streamlit UI ```bash python launch.pyw ``` ### Bot 接口（IM） GenericAgent 支持 Telegram、微信、QQ、飞书 / Lark、企业微信、钉钉等 IM 前端。 | 平台 | 启动命令 | | :--- | :--- | | Telegram | `python frontends/tgapp.py` | | 微信 | `python frontends/wechatapp.py` | | QQ | `python frontends/qqapp.py` | | 飞书 / Lark | `python frontends/fsapp.py` | | 企业微信 | `python frontends/wecomapp.py` | | 钉钉 | `python frontends/dingtalkapp.py` | > 详细配置直接问 GenericAgent。 ### 通用聊天命令 | 命令 | 说明 | | :--- | :--- | | `/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 | --- ## 🧠 架构设计 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。* | 层级 | 名称 | 说明 | | :---: | :--- | :--- | | **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`](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（Python / PowerShell） | | `file_read` | 读取文件 | | `file_write` | 写入 / 创建 / 覆盖文件 | | `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 | | `update_working_checkpoint` | *（记忆）* 短期工作记事板 | | `start_long_term_update` | *（记忆）* 提炼长期记忆 | ### 4️⃣ 能力扩展机制 > *具备动态创建新工具的能力。* 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

--- ## 🧬 自我进化机制这是 GenericAgent 区别于其他 Agent 框架的根本所在。 ```text [遇到新任务] │ ▼ [自主摸索] ─► 安装依赖 · 编写脚本 · 调试验证 │ ▼ [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 | 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。 --- ## 📊 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 | --- ## 📈 评测 > 📂 完整的评测数据集以及评测结果见：[**JinyiHan99/GA-Technical-Report**](https://github.com/JinyiHan99/GA-Technical-Report/tree/main) 我们从 **五大维度** 评测 GenericAgent： | # | 维度 | 核心问题 | 使用的基准 | | :---: | :--- | :--- | :--- | | 1 | **任务完成度与 Token 效率** | GA 能否以更低成本完成高难度任务？ | SOP-Bench、Lifelong AgentBench、RealFin-Benchmark | | 2 | **工具使用效率** | 最小原子工具集能否以更低开销替代专用工具集？ | Tool Efficiency Benchmark | | 3 | **记忆系统有效性** | 精简分层记忆能否超越冗余记忆和基于 Embedding 的检索器？ | SOP-Bench、LoCoMo、20-skill 压力测试 | | 4 | **自我进化能力** | Agent 能否在无人干预下将经验提炼为可复用的 SOP 与代码？ | 9 轮 LangChain 纵向研究、8 任务跨任务 Web 基准 | | 5 | **网页浏览能力** | 信息密度驱动设计能否适应开放网页？ | WebCanvas、BrowseComp-ZH、自定义任务 | 以上维度的基线包括 **Claude Code**、**OpenAI CodeX** 和 **OpenClaw**，分别在 *Claude Sonnet 4.6*、*Claude Opus 4.6*、*GPT-5.4* 和 *MiniMax M2.7* 底座上进行评测。

_{工具使用效率雷达图。GA 在 Token、请求数和工具调用轴上全面领先，同时在四个任务维度上保持质量。}

_{跨任务自我进化。GA 的第二轮和第三轮执行在 8 个 Web 任务上收敛至稳定的低成本区间。}

--- ## 📅 路线图与最新动态 - **2026-05-15** — 🖥️ **桌面 GUI 发布**。一键安装会自带可直接运行的桌面端（`frontends/GenericAgent.exe`），开发者也可用 `python launch.pyw` 启动。 - **2026-05-14** — 🆕 **Conductor 子 Agent 编排**。派发、监督、自动清理并行子 Agent；与 `/btw` 旁路子 Agent 互补，提供一等公民级的任务委派原语。 - **2026-05-12** — 🆕 **TUI v2 正式发布**（`frontends/tuiapp_v2.py`）。重做视觉风格的 Textual 前端，支持图片粘贴折叠、文件粘贴、块删除、Ctrl+C 复制、历史导航，以及 `/llm` / `/export` / `/continue` 选择器。 - **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** — [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。 --- ## ⭐ 社区与支持如果这个项目对你有帮助，欢迎点一个 **Star!** 🙏 也欢迎加入 **GenericAgent 体验交流群**，一起交流、反馈、共建 👏

微信群 19
微信群 19 二维码

### 🚩 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/) **社区 GUI 客户端** *（独立开源项目）*： - [chilishark27/ga-manager](https://github.com/chilishark27/ga-manager) - [wangjc683/galley](https://github.com/wangjc683/galley) - https://github.com/FroStorM/A3Agent/tree/workbench --- ## 📄 许可基于 **MIT License** 发布，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