FreeBaby

English | 中文 | 📄 Technical Report:  | 📘 Documentation

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🌟 Overview

FreeBaby 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 FreeBaby 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 FreeBaby. 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 FreeBaby 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 — *FreeBaby: 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 - 2026-03-08: Released "Dintal Claw" — a FreeBaby-powered government affairs bot - 2026-03-01: FreeBaby featured by Jiqizhixin (机器之心) - 2026-01-16: FreeBaby V1.0 public release

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🚀 Quick Start

Method 1: Standard Installation

# 1. Clone the repo
git clone https://github.com/weiwei0450/FreeBaby.git
cd FreeBaby
# 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, FreeBaby also provides a minimal pyproject.toml:

git clone https://github.com/weiwei0450/FreeBaby.git
cd FreeBaby
uv pip install -e ".[ui]"        # Core + GUI dependencies
cp mykey_template.py mykey.py
python launch.pyw

> FreeBaby is meant to grow its environment through the Agent itself, not by pre-installing every possible package.

Full guide: GETTING_STARTED.md

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🤖 Bot Interface (Optional)

Telegram Bot

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

python frontends/tgapp.py

Alternative App Frontends

Besides the default Streamlit web UI, you can also try other frontend styles:

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 Nth recoverable conversation

📊 Comparison with Similar Tools

| Feature | FreeBaby | 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

FreeBaby 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 > _FreeBaby 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, FreeBaby can dynamically install Python packages, write new scripts, call external APIs, or control hardware at runtime — crystallizing temporary abilities into permanent tools.

⭐ Support

If this project helped you, please consider leaving a Star! 🙏

You're also welcome to join our FreeBaby Community Group for discussion, feedback, and co-building 👏

🚩 Friendly Links

Thanks for the support from the LinuxDo community!


📄 License

MIT License — see 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 FreeBaby name.*

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🌟 项目简介

FreeBaby 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，FreeBaby 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 FreeBaby 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 FreeBaby 区别于其他 Agent 框架的根本所在。

[遇到新任务]-->自主摸索-->
[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 — *FreeBaby: 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 - 2026-03-08: 发布以 FreeBaby 为核心的"政务龙虾" Dintal Claw - 2026-03-01: FreeBaby 被机器之心报道 - 2026-01-16: FreeBaby V1.0 公开版本发布

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🚀 快速开始

方法一：标准安装

# 1. 克隆仓库
git clone https://github.com/weiwei0450/FreeBaby.git
cd FreeBaby
# 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，FreeBaby 也提供了一个最小化的 pyproject.toml：

git clone https://github.com/weiwei0450/FreeBaby.git
cd FreeBaby
uv pip install -e ".[ui]"        # 核心 + GUI 依赖
cp mykey_template.py mykey.py
python launch.pyw

> FreeBaby 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。

📖 新手使用指南（图文版）：飞书文档

📘 完整入门教程：FreeBaby 文档

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🤖 Bot 接口（可选）

微信 Bot（个人微信）

无需额外配置，扫码登录即可：

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：

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 frontends/qqapp.py

> 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 frontends/fsapp.py

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 frontends/wecomapp.py

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 frontends/dingtalkapp.py

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：

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 | FreeBaby | 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

FreeBaby 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 >FreeBaby 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，FreeBaby 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。

⭐ 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FreeBaby体验交流群，一起交流、反馈和共建 👏

🚩 友情链接

感谢 LinuxDo 社区的支持！


📄 许可

MIT License — 详见 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 FreeBaby 名义从事商业活动。*

📈 Star History

brew install python

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

python3 --version

# 变量名含 'oai' → 走 OpenAI 兼容格式 (/chat/completions)
oai_config = {
    'apikey': 'sk-你的密钥',
    'apibase': 'http://你的API地址:端口',
    'model': '模型名称',
}

# 变量名含 'claude'（不含 'native'）→ 走 Claude 兼容格式 (/messages)
claude_config = {
    'apikey': 'sk-你的密钥',
    'apibase': 'http://你的API地址:端口',
    'model': 'claude-sonnet-4-20250514',
}

# MiniMax 使用 OpenAI 兼容格式，变量名含 'oai' 即可
# 温度自动修正为 (0, 1]，支持 M2.7 / M2.5 全系列，204K 上下文
oai_minimax_config = {
    'apikey': 'eyJh...',
    'apibase': 'https://api.minimax.io/v1',
    'model': 'MiniMax-M2.7',
}

# 变量名同时含 'native' 和 'claude' → Claude 标准工具调用格式
native_claude_config = {
    'apikey': 'sk-ant-你的密钥',
    'apibase': 'https://api.anthropic.com',
    'model': 'claude-sonnet-4-20250514',
}

cd 你的解压路径
python3 agentmain.py

帮我在桌面创建一个 hello.txt，内容是 Hello World

请查看你的代码，安装所有用得上的 python 依赖

> pip install requests
> 

python3 launch.pyw

请帮我建立 git 连接，方便以后更新代码

请帮我在桌面创建一个 launch.pyw 的快捷方式

打开淘宝，搜索 iPhone 16，按价格排序
去 B 站，查看我最近看过的历史视频