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autonomous-loop-studio/Autonomous

Self-hosted AI agent console in plain PHP — any model, real tools, live agent map

装之前:没有可直接复制的命令 · 许可证 Apache-2.0 · 链接可访问

第一步:这条暂时没有可执行的下一步,可先看原仓库 https://github.com/autonomous-loop-studio/Autonomous

在 GitHub 查看 ↗链接已于 2026-11-12 核查 · HTTP 200 · 打开的是厂商自己的官网
安装与配置
客户端配置(取自该仓库的安装命令)
{
  "mcpServers": {
    "autonomous-loop-studio-autonomous": {
      "command": "npx",
      "args": ["-y", "mcp-remote"]
    }
  }
}

解析自我们于 2026-11-11 抓取的该仓库 README;我们未执行过。

command/args 来自仓库写明的安装命令;不确定时请以该项目 README 为准。
VS Code(.vscode/mcp.json —— 键名是 servers)
{
  "servers": {
    "autonomous-loop-studio-autonomous": {
      "command": "npx",
      "args": ["-y", "mcp-remote"]
    }
  }
}
VS Code 顶层键是 <code>servers</code>,其余与上面那份相同。
Claude Code(命令行)
claude mcp add --transport stdio autonomous-loop-studio-autonomous -- npx -y mcp-remote
Claude Code 不收 JSON 文件,用这条命令直接加。
配置项的键名以你的客户端为准:客户端与 CLI
★ 数取值
2 ★ · autonomous-loop-studio/Autonomous · 取自 GitHub 搜索 API · (2026-11-13 20:00:50 核对)
来源明细
从该仓库的 GitHub 页面直接读取 · 条目类型:开源项目
收录与活跃
收录于 2026-11-12 · 最近见到 2026-11-14 · 共见到 3 次 · 被 2 个来源列出
评分构成
综合分 203 · 2 ★ · 2 个来源 · repo
安装
npx -y mcp-remote
常见问题(答案都来自我们的记录)
怎么安装?
该仓库给出的安装命令是 npx -y mcp-remote。我们的记录里它是仓库自己提供的;不保证在你的客户端上直接可用。
它适合谁 / 用来做什么?
我们把它归在这些标签下: 开源项目。标签来自条目自身,不是我们强加的分类。
谁维护它、还在更新吗?
仓库 autonomous-loop-studio/Autonomous;我们收录于 2026-11-12;最近见到 2026-11-14。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。

# 🧠 Autonomous Loop Studio

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

### A self-hosted AI agent console in plain PHP.
No Docker. No Node. No build step. No account.
Point it at any model — hosted, or running on your own machine — hand the agents
real tools, and watch the whole loop on a live map. It is one folder of PHP
and static files: drop it on the host you already pay for, double-click, and a
team of agents is running.
🚀 Quick start ↓ · ✨ Features ↓ · 🔒 How the guards work ↓ · 🧩 Architecture ↓
## Run it in five minutes
Windows — double-click start.bat. First run installs Apache + PHP (no MySQL), creates your
access token, starts the server and opens the app. Later runs just start Apache.
Linux / macOS — ./start.sh — or ./start.sh 8090 for another port.
Paste the token it prints into Config → Access token, add one provider key, and you have an
agent. Nothing ships with a key, and a fresh copy refuses to serve (503) until a token
exists — you cannot forget to set one.
Then add a provider — OpenAI, Anthropic, Google, OpenRouter, Groq, DeepSeek, xAI, Mistral,
Cerebras, Together, Perplexity, NVIDIA NIM, or Ollama / LM Studio / vLLM on your own LAN.
## What it is
🤖 Any model, any provider. It detects each provider's API shape and its tool dialect —
JSON, Claude XML, raw tool-call tags — and remembers which one actually worked for that model.
🛠️ Real tools, not chat. Read and write files inside a jail, run allowlisted commands
(argv only — no shell), drive a real Chrome/Edge over DevTools, commit with git, call MCP servers.
🕸️ You can watch it. A live 3D map of agents, providers and traffic; every hop also written
out in words in a run trace; routing charts you draw instead of describing.
🔐 Safe by default. exec, run_code, the browser and the MCP server are off until you
switch them on. Every write asks first. No accounts, no cloud, no telemetry.
## What it is not
Not a hosted service, not a wrapper around one vendor, not a SaaS with an account page. There is
nothing to sign up for, and the only outbound calls are the provider endpoints you configure
yourself.
## A fleet for $0
Free tiers and local models are first-class here, not an afterthought — and paced so one exhausted
key does not stop the fleet.
⏱️ Paced to each provider's real ceiling — the pacer knows the host's limit (NVIDIA NIM
40/min, Groq 30, OpenRouter 20, a default for anything unknown), and 0 = AUTO uses it. Global,
per-provider or per-agent, and an agent can lock its own.
🔑 A 429 is not a stall — a rate-limited key rests for 60 seconds and the next call reaches
for another; add extra keys and the API pool rotates them after N requests.
♾️ Loops that keep going — Always-On re-prompts a standing goal on a cadence, with max
iterations and max minutes caps, per-agent hold/release and one master kill switch.
Free tiers have daily caps, so pacing smooths bursts rather than making them infinite, and "always
on" means the host stays awake — the tab-hidden worker, or a machine that does not sleep. Against
Ollama, LM Studio or vLLM on your own box there is nothing to pace at all.
## A look at it
Glass desktop and Classic (the default) · a routing chart · Brain Lab · the built-in browser.
## Everything else, below
Quick start · Is it safe? · What's inside ·
收录徽章(可选)
想把收录标记放进 README 就复制这行,可选,不影响收录。
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来源
github · https://github.com/autonomous-loop-studio/Autonomous
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