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智能代理操作系统
⚙️
Agent工作流

智能代理操作系统

基于 Python · 无代码搭建完整 AI 自动化流程
英文名:tinyagentos
⭐ 198 Stars 🍴 17 Forks 💻 Python 📄 AGPL-3.0 🏷 AI 7.5分
7.5AI 综合评分
ai-agentsai-platformdistributed-computingpython
✦ AI Skill Hub 推荐

智能代理操作系统 是 AI Skill Hub 本期精选Agent工作流之一。综合评分 7.5 分,整体质量较高。我们推荐使用将其纳入你的 AI 工具库,帮助提升工作效率。

📚 深度解析

智能代理操作系统 是一套完整的 AI Agent 自动化工作流方案。随着 AI 能力的不断提升,基于 Agent 的自动化工作流正在成为提升个人和团队效率的核心方式。区别于传统的 RPA 自动化(模拟鼠标键盘操作),AI Agent 工作流通过理解任务意图、动态规划执行路径,能够处理更复杂的非结构化任务。

智能代理操作系统 工作流的设计遵循"最小配置,最大复用"原则:核心逻辑已经封装好,用户只需配置自己的 API Key 和业务参数即可快速上手。工作流内置错误处理和重试机制,在网络波动或 API 限速等情况下仍能稳定运行,适合作为生产环境的自动化基础设施。

在实际部署时,建议先在测试环境中运行 3-5 次,验证各个环节的输出结果符合预期,再部署到生产环境。AI Skill Hub 评分 7.5 分,是同类 Agent 工作流中的精选推荐。

📋 工具概览

智能代理操作系统 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。

GitHub Stars
⭐ 198
开发语言
Python
支持平台
Windows / macOS / Linux
维护状态
轻量级项目,按需更新
开源协议
AGPL-3.0
AI 综合评分
7.5 分
工具类型
Agent工作流
Forks
17

📖 中文文档

以下内容由 AI Skill Hub 根据项目信息自动整理,如需查看完整原始文档请访问底部「原始来源」。

智能代理操作系统 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。

📌 核心特色
  • 可视化 Agent 工作流编排,无需编写复杂代码
  • 支持多步骤自动化任务链,实现全流程无人值守
  • 与外部 API、数据库和第三方服务无缝集成
  • 内置错误处理与自动重试机制,保障稳定运行
  • 提供可复用的自动化模板,快速在同类场景部署
🎯 主要使用场景
  • 自动化日常重复性工作,将精力集中于创造性任务
  • 构建数据采集 → 处理 → 输出的完整自动化管线
  • 实现跨平台、跨系统的数据流转和业务协同
以下安装命令基于项目开发语言和类型自动生成,实际以官方 README 为准。
安装命令
# 方式一:pip 安装(推荐)
pip install tinyagentos

# 方式二:虚拟环境安装(推荐生产环境)
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install tinyagentos

# 方式三:从源码安装(获取最新功能)
git clone https://github.com/jaylfc/tinyagentos
cd tinyagentos
pip install -e .

# 验证安装
python -c "import tinyagentos; print('安装成功')"
📋 安装步骤说明
  1. 访问 GitHub 仓库获取工作流文件
  2. 在对应平台(Dify / Flowise / Make 等)中找到「导入工作流」功能
  3. 上传工作流文件
  4. 按照提示配置必要的环境变量和 API Key
  5. 运行测试确认流程正常后投入使用
以下用法示例由 AI Skill Hub 整理,涵盖最常见的使用场景。
常用命令 / 代码示例
# 命令行使用
tinyagentos --help

# 基本用法
tinyagentos input_file -o output_file

# Python 代码中调用
import tinyagentos

# 示例
result = tinyagentos.process("input")
print(result)
以下配置示例基于典型使用场景生成,具体参数请参照官方文档调整。
配置示例
# tinyagentos 配置文件示例(config.yml)
app:
  name: "tinyagentos"
  debug: false
  log_level: "INFO"

# 运行时指定配置文件
tinyagentos --config config.yml

# 或通过环境变量配置
export TINYAGENTOS_API_KEY="your-key"
export TINYAGENTOS_OUTPUT_DIR="./output"
📑 README 深度解析 真实文档 完整度 52/100 查看 GitHub 原文 →
以下内容由系统直接从 GitHub README 解析整理,保留代码块、表格与列表结构。

简介

<p align="center"> <img src="static/taos-logo.png" alt="taOS" width="400"> </p>

Key Features

Dynamic Capabilities

Features unlock automatically based on your hardware and cluster. Solo Pi sees core features. Add a GPU worker and image generation, video, and training appear. No configuration, the platform just knows what's possible.

Debian / Ubuntu / Fedora / Arch / Alpine / macOS, one-line install

curl -fsSL https://raw.githubusercontent.com/jaylfc/tinyagentos/master/scripts/install-server.sh | sudo bash


Run without `sudo` to install as a user-mode systemd unit instead. The script is idempotent, safe to re-run on an existing install. Supports env-var overrides for install path, branch, and port.

**Manual / development:**
bash pip install -e . python -m uvicorn tinyagentos.app:create_app --factory --host 0.0.0.0 --port 6969 ```

Open http://your-host:6969 (or http://taos.local:6969 with mDNS). The root URL loads the desktop shell directly.

Linux / macOS, one-line worker install (auto-detects headless,

installs as a system service when run with sudo or as a user

service otherwise; works on a fresh Debian install or your existing box)

curl -fsSL https://raw.githubusercontent.com/jaylfc/tinyagentos/master/scripts/install-worker.sh | sudo bash -s -- http://your-server:6969

Android, one-line Termux setup

curl -sL https://raw.githubusercontent.com/jaylfc/tinyagentos/master/tinyagentos/worker/android_setup.sh | bash

powershell

Windows 10/11, one-line worker install (PowerShell, mirrors the

Linux/macOS installer — registers a Scheduled Task so the worker

Agent Deployment

5-step wizard: pick framework → choose model → configure → deploy into an isolated container (LXC on bare metal, Docker on VPS, auto-detected). Each agent gets its own memory system (taOSmd instance), its own file storage, and its own network identity. The framework runs inside the container but TinyAgentOS manages everything around it: memory, channels, secrets, model access, scheduled tasks, and inter-agent communication. This means the framework is a swappable component, not a lock-in decision.

**Running taOS inside an LXC (e.g. Proxmox)?** Deploying an agent creates a nested container, which an unprivileged LXC cannot do — the kernel can't remap the nested container's filesystem, so the deploy fails with an idmapped storage / change ownership error. Run the taOS LXC as privileged with nesting enabled. On Proxmox: untick Unprivileged container and set Options → Features → nesting=1 (plus keyctl=1, fuse=1), then redeploy. Bare-metal and VM installs are unaffected. (taOS detects this and surfaces the fix in the deploy error.)

<p align="center"> <img src="docs/images/mobile-agents-empty.png" alt="Agents app empty state on mobile — one tap to deploy" width="30%"> </p>

<p align="center"><sub>The Agents app on mobile — one tap from empty to your first deployed agent.</sub></p>

What the install creates on your box

Full transparency on every file, service, user, and port the installers touch. Nothing is hidden behind a vendored binary; everything is plain Python, plain systemd, plain shell.

Controller install (`scripts/install-server.sh`)

Run curl -fsSL https://raw.githubusercontent.com/jaylfc/tinyagentos/master/scripts/install-server.sh | sudo bash on a fresh Debian / Ubuntu / Fedora / Arch / Alpine box to get the controller fully installed, repo cloned to ~/tinyagentos/, venv created, all deps installed, and both tinyagentos.service (port 6969) and qmd.service (port 7832) registered and started.

WhereWhat
/etc/systemd/system/tinyagentos.serviceMain controller systemd unit. Runs uvicorn on port 6969.
/etc/systemd/system/qmd.serviceEmbedding backend (embed / rerank / query expansion) on port 7832. Used by taOSmd for vector operations. Backed by rkllama on RK3588 boards or local node-llama-cpp elsewhere.
tinyagentos-sdcpp.service (repo root)(RK3588 only) CPU image generation backend. Manual setup only — not auto-installed by the installer.
tinyagentos-rknn-sd.service (repo root)(RK3588 only) NPU image generation backend. Manual setup only — not auto-installed by the installer.
/home/<user>/tinyagentos/The repo checkout. All code, all configs.
/home/<user>/tinyagentos/.venv/Python virtualenv. All Python deps live here, never pip install to system Python.
/home/<user>/tinyagentos/data/All persistent state. **One directory to back up.** Contains: agent state YAMLs, agent memory SQLite indexes, agent workspaces, secrets DB, scheduler history, channel credentials, downloaded models, torrent settings, telemetry opt-in flag.
/home/<user>/.cache/qmd/index.sqliteUser memory index (taOSmd knowledge base for personal notes). Per-agent indexes live separately under data/agent-memory/{name}/index.sqlite.
Ports listened on**6969** (controller HTTP API + web UI), **6970** (browser-proxy second-origin, TAOS_BROWSER_PROXY_PORT), **7832** (qmd embedding service), **4000** (LiteLLM proxy, localhost only by default)
OS packages addedpython3 + venv + pip, git, curl, ca-certificates, libtorrent-rasterbar (model torrent mesh), Node.js 22 (qmd + SPA build), sqlite3, libsqlcipher (encrypted secrets), vulkan-tools (hardware detection), postgresql (LiteLLM virtual keys)
User accounts createdThe distro postgres system user is created when PostgreSQL is installed. A litellm Postgres role and database are created for virtual-key management. Everything else runs as the user who ran the installer.

Worker install (`scripts/install-worker.sh`)

WhereWhat
/etc/systemd/system/tinyagentos-worker.serviceWorker systemd unit (when run with sudo). Connects to the controller URL you passed and registers this machine as a cluster node. Runs as your user via User=, not root.
~/.config/systemd/user/tinyagentos-worker.serviceSame unit as above, but in user-mode (when run without sudo). Linger is enabled automatically.
~/.local/share/tinyagentos-worker/Worker repo checkout. ~150 MB on disk after install. Self-contained venv inside.
~/.local/share/tinyagentos-worker/.venv/Python venv with worker-only deps: httpx, pydantic, psutil, fastapi, uvicorn, pyyaml, pillow, libtorrent. **Does NOT install controller-side deps** (no aiosqlite, no LiteLLM, no scheduler engine).
Ports listened onNone. Workers are pure outbound, they connect TO the controller.
OS packages addedpython3, venv, pip, git, curl, ca-certificates, libtorrent (Debian/Ubuntu only, Arch/Fedora/Alpine equivalents on those distros).
User accounts createdNone. The worker runs as the user who ran the installer.

Verify what's installed

```bash

Worker side (after running install-worker.sh)

systemctl status tinyagentos-worker ls ~/.local/share/tinyagentos-worker/ ```

Uninstall

```bash

If you manually installed the RK3588 image-gen units:

Upgrading a long-running install

Controller (recommended): use the one-shot update script:

cd ~/tinyagentos
bin/update.sh

This pulls the latest, rebuilds the desktop frontend bundle if the source has moved (skips it when nothing changed), then restarts the service. The frontend rebuild takes ~50s when it fires; it is a no-op otherwise.

Manual equivalent:

```bash cd ~/tinyagentos git pull

which can confuse Python's .pyc cache invalidation on some setups)

find . -name pycache -type d -exec rm -rf {} + 2>/dev/null || true

Rebuild frontend if desktop source changed (omit if you didn't pull any desktop/ changes)

cd desktop && npm install && npm run build && cd .. sudo systemctl restart tinyagentos


The systemd unit also runs a conditional rebuild as an `ExecStartPre` step — if you skip the manual `npm run build`, the next service restart detects the stale bundle and rebuilds it automatically (~50s startup overhead when it fires).

**Worker:**
bash cd ~/.local/share/tinyagentos-worker git pull find . -name pycache -type d -exec rm -rf {} + 2>/dev/null || true sudo systemctl restart tinyagentos-worker ```

The bytecode cleanup line is belt-and-braces; Python's mtime-based invalidation usually works, but on long-running boxes that have survived many upgrades it occasionally doesn't, and a stale .pyc is easy to mistake for a code bug.

RK3588 NPU Setup

scripts/install-rknpu.sh is an opt-in automated installer for the full Rockchip NPU stack. It pins librknnrt to 2.3.0, installs the jaylfc fork of rkllama, and preloads three chat models. All binaries are fetched from huggingface.co/jaysom/tinyagentos-rockchip-mirror, a TAOS-controlled mirror, and SHA256-verified before installation. If any checksum fails the script hard-aborts.

curl -fsSL https://raw.githubusercontent.com/jaylfc/tinyagentos/master/scripts/install-rknpu.sh | sudo bash

See docs/mirror-policy.md for the mirror governance policy, what is mirrored, when it updates, how to verify integrity independently, and how to self-host the mirror for air-gapped deployments. The same policy will extend to RK3576, Raspberry Pi 4, Mac mini / Apple Silicon, and x86 classes as those verified install paths land.

Quick Start

Controller (server):

```bash

Skills & Plugins Registry

Framework-agnostic skill system with 8 default skills, memory_search, file_read, file_write, web_search, code_exec, image_generation, list_image_models, http_request, categorised by search, files, code, media, browser, data, comms, system. Each skill declares compatibility per framework (native/adapter/unsupported) and works across all 16 supported frameworks via adapter translation. Assign or remove skills per agent from the Skills tab with compatibility badges.

App Store (108 Catalog Apps + 47 MCP Plugins, including 13 Streaming Apps)

One-click install for agent frameworks, AI models, and services. Hardware-aware, only shows what works on your device.

MCP Plugin Catalog (47 Plugins)

app-catalog/plugins/ ships 47 MCP servers including the official set (filesystem, git, fetch, memory, sequential-thinking, time), GitHub, Playwright, Docker, Kubernetes, databases (Postgres/MySQL/SQLite dbhub, MongoDB, Redis, Chroma, Supabase), documents (pandoc, office docs, spreadsheet, markdownify, excel), comms (Slack, WhatsApp, email, Notion, Obsidian, Atlassian, Google Workspace), infra (AWS, Cloudflare, Grafana, arXiv, YouTube transcript, Firecrawl), agent-specific (browser-use, Camoufox, context7, supergateway, engram, Exa), Home Assistant, Todoist, and more.

App Catalog (108 Catalog Apps + 36 Desktop Apps + 47 MCP Plugins)

CategoryApps
**Agent Frameworks (15)**SmolAgents, PocketFlow, OpenClaw, nanoclaw, PicoClaw, ZeroClaw, MicroClaw, IronClaw, NullClaw, Moltis, Hermes, Agent Zero, OpenAI Agents SDK, Langroid, ShibaClaw
**Streaming Apps (13)**Blender, LibreOffice, Code Server, GIMP, Krita, FreeCAD, Obsidian, Excalidraw, JupyterLab, Grafana, n8n, Terminal, Neko Browser
**LLM Models**112-manifest local catalog: Qwen3 0.6B-32B, Qwen2.5 0.5B-72B (+ RKLLM 1.5B-14B for RK3588), Llama 3.1/3.2/3.3, Gemma 2/3, Phi-3.5/4/4-mini, Mistral/Nemo/Mixtral, DeepSeek, Granite, Command-R, SmolLM2, TinyLlama, plus 167k+ searchable from HuggingFace
**Vision Models**Qwen2-VL, Qwen2.5-VL, MiniCPM-V 2.6, Moondream2, Florence-2, LLaVA 1.6 / LLaVA-Phi-3
**Embeddings / Rerankers**nomic-embed-text-v1.5, bge-large/small/m3, mxbai-embed-large, snowflake-arctic-embed, qwen3-embedding/reranker, bge-reranker-v2-m3
**Audio Models**Whisper tiny→large-v3-turbo, Kokoro TTS, Piper voices, Parakeet TDT
**Image Models**SD 1.5 LCM, Dreamshaper 8 LCM, LCM Dreamshaper V7 (+ RKNN for RK3588), SDXL Turbo/Lightning, Flux schnell/dev GGUF, SD 3.5 Large Turbo, PixArt-Σ, SDXS, Playground v2.5, Kolors, AuraFlow, Stable Cascade
**Image Tools**RMBG-1.4, BiRefNet, Real-ESRGAN x4, 4x-UltraSharp, GFPGAN, CodeFormer, ControlNet (canny/depth/openpose)
**Image Gen**ComfyUI, Fooocus, SD Web UI, stable-diffusion.cpp, FastSD CPU, RKNN SD, rk-llama.cpp
**Video Gen**WanGP (Wan 2.1/2.2, HunyuanVideo), LTX Video
**Voice/Audio**Whisper STT, Piper TTS, Kokoro TTS, Chatterbox, MusicGPT
**AI Tools**Perplexica (AI search), Open WebUI, Dify, SearXNG
**Infrastructure**Gitea, Code Server, n8n, Docker Mailserver, Tailscale, Dynamic DNS
**Home & Monitoring**Home Assistant, Uptime Kuma, File Browser, Excalidraw, Memos, Linkwarden
🎯 aiskill88 AI 点评 A 级 2026-06-03

高质量的开源AI工作流项目,具有较高的实用价值

📚 实用指南(长尾问题)
适合谁
  • 需要让 Claude / Cursor 操作本地工具的 AI 工程师
  • 构建多智能体协作系统的 Agent 开发者
  • 构建企业知识库 / RAG 检索应用的团队
  • 跨境业务、多语言内容运营团队
  • 做语音类 AI 产品的开发者
最佳实践
  • 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC,避免暴露公网
  • 生产部署优先使用 Docker Compose 隔离依赖,并挂载 volume 持久化数据
  • 本地部署优先选 GGUF 量化模型,节省显存并保持响应速度
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • MCP 配置路径拼错或权限不足,重启 Claude Desktop 才生效
  • 容器内无法访问宿主机 localhost — 使用 host.docker.internal
  • 显存不足直接 OOM — 优先降低 context 或换更小的量化模型
  • Python 依赖冲突:建议用 venv / uv 隔离环境
部署方案
  • Docker:tinyagentos 提供官方镜像,docker compose up 一键启动
  • CLI:直接 npm install -g / pip install,命令行调用
  • 本地部署:CPU 8GB 起,GPU 推荐 16GB+ 显存
  • 云端托管:可放在 Vercel / Railway / Fly.io 等 PaaS 平台
相关搜索
tinyagentos 中文教程tinyagentos 安装报错怎么办tinyagentos MCP 配置tinyagentos Docker 部署tinyagentos Agent 工作流tinyagentos 与同类工具对比tinyagentos 最佳实践tinyagentos 适合谁用

⚡ 核心功能

👥 适合谁
  • 需要让 Claude / Cursor 操作本地工具的 AI 工程师
  • 构建多智能体协作系统的 Agent 开发者
  • 构建企业知识库 / RAG 检索应用的团队
  • 跨境业务、多语言内容运营团队
⭐ 最佳实践
  • 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC,避免暴露公网
  • 生产部署优先使用 Docker Compose 隔离依赖,并挂载 volume 持久化数据
  • 本地部署优先选 GGUF 量化模型,节省显存并保持响应速度
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
⚠️ 常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • MCP 配置路径拼错或权限不足,重启 Claude Desktop 才生效
  • 容器内无法访问宿主机 localhost — 使用 host.docker.internal
  • 显存不足直接 OOM — 优先降低 context 或换更小的量化模型

👥 适合人群

自动化工程师和运维人员项目经理和业务分析师希望减少重复性工作的专业人士数字化转型团队

🎯 使用场景

  • 自动化日常重复性工作,将精力集中于创造性任务
  • 构建数据采集 → 处理 → 输出的完整自动化管线
  • 实现跨平台、跨系统的数据流转和业务协同

⚖️ 优点与不足

✅ 优点
  • +大幅减少重复性人工操作
  • +可视化流程,清晰直观
  • +可扩展性强,支持复杂场景
⚠️ 不足
  • 初始配置和调试需投入一定时间
  • 强依赖外部服务的稳定性
  • 复杂场景需具备一定技术基础
⚠️ 使用须知

该工具使用 AGPL-3.0 协议,商用场景请仔细阅读协议条款,必要时咨询法律意见。

AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。

建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。

📄 License 说明

⚠️ AGPL 3.0 — 最严格的 Copyleft,网络服务端使用也需开源,SaaS 使用受限。

🔗 相关工具推荐

📰 相关 AI 新闻
🍿 AI 圈相关吃瓜
🗺️ 相关解决方案
🧩 你可能还需要
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❓ 常见问题 FAQ

参考项目文档和示例代码
💡 AI Skill Hub 点评

经综合评估,智能代理操作系统 在Agent工作流赛道中表现稳健,质量良好。如果你已有明确的使用需求,可以直接上手体验;如果还在评估阶段,建议对比同类工具后再做决策。

⬇️ 获取与下载
⬇ 下载源码(GPL)
⚠️ 本工具使用 AGPL-3.0 协议。您可以自由下载和使用,但衍生作品必须以相同协议开源,不可商业闭源。使用前请确认符合协议要求。
📚 深入学习 智能代理操作系统
查看分步骤安装教程和完整使用指南,快速上手这款工具
🌐 原始信息
原始名称 tinyagentos
原始描述 开源AI工作流:Self-hosted auto clustering AI agent OS for consumer hardware like the computer 。⭐198 · Python
Topics ai-agentsai-platformdistributed-computingpython
GitHub https://github.com/jaylfc/tinyagentos
License AGPL-3.0
语言 Python
🔗 原始来源
🐙 GitHub 仓库  https://github.com/jaylfc/tinyagentos 🌐 官方网站  https://tinyagentos.com

收录时间:2026-06-03 · 更新时间:2026-06-03 · License:AGPL-3.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。