Preloop

Name: Preloop
Author: preloop

作者：preloop版本 0.17.1i★ –i在云端运行

注册名称io.github.preloop/preloop

安装

概览

作者提供的说明（英文原文）

Open-source AI agent control plane and software factory: govern every tool and model call.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在云端运行
传输方式: Streamable HTTP
分类: 其他
你需要填写：Authorization

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在作者的服务器上运行：你向它提出的内容会经过他们的服务器。
需要你自己填写： Authorization

标为机密的值是你账户的钥匙。千万不要分享给别人，也不要粘贴到聊天里。

你需要

在云端运行无需安装任何东西，通过互联网连接

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

这个地址需要在请求的 header 中放入密钥，而添加连接器的菜单无法设置。请改用 Claude Code、Cursor 或 VS Code。

在终端中运行这条命令

claude mcp add --transport http preloop https://preloop.ai/mcp/v1 --header 'Authorization: your-value'

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "preloop": {
      "url": "https://preloop.ai/mcp/v1",
      "headers": {
        "Authorization": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "preloop": {
      "type": "http",
      "url": "https://preloop.ai/mcp/v1",
      "headers": {
        "Authorization": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个地址需要在请求的 header 中放入密钥，而添加连接器的菜单无法设置。请改用 Claude Code、Cursor 或 VS Code。

你的选择通过 Cookie 保存在这台设备上，无需账户。

需要你填写的值

Authorization 机密 HTTP header
Optional. Preloop access token as 'Bearer ' (from 'preloop auth token'). OAuth-capable clients can omit it.

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

我们还没有读取这个服务器的工具列表；我们会轮流检查各个端点。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 preloop 验证，该账户拥有此仓库
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
… 尚未检查
端点有响应
尚未检查；我们会轮流检查各个端点
… 尚未检查
使用 OAuth 登录
尚未检查；我们会轮流检查各个端点
… 尚未检查
工具描述中没有隐藏指令
尚未检查；我们会在检查端点时读取工具列表

与“其他”中的其他服务器比较i

星标数多于此分类中其他 32466 个服务器的 0%
发布到注册中心的时间比此分类中其他 32466 个服务器的 73% 更新
基础检查结果等于或优于此分类中其他 32466 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

Preloop 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

你需要填写：Authorization

它在我的电脑上运行还是在云端？

它在作者的云端运行。通过互联网连接，无需安装，但你发送的内容会经过作者的服务器。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（0.17.1）于13天前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。它通过 URL 连接，所以接受远程 MCP 服务器的应用也可以使用，例如开启开发者模式的 ChatGPT。

有什么可以替代它？

同一分类中有：Spec Driven Development, Remote Desktop Commander, HotCodePush

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Spec Driven Development

作者：formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

在你的电脑上运行★ 440MIT14个月前更新

安装

Remote Desktop Commander

作者：desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

在云端运行★ 211昨天更新

安装

HotCodePush

作者：hotcodepush-team

Manage over-the-air (OTA) updates for React Native, Expo, Capacitor and Cordova apps.

在云端运行今天更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

Preloop

The open-source AI agent control plane and software factory.

Preloop is one self-hostable service that sits between your AI agents and everything they reach, and runs agents for you when work arrives. Every tool call and every model call is governed, attributed and visible, whether a person started the agent or a ticket did. You see gateway spend by model, agent, session, API key and flow, and cap it with budgets. Imported Copilot usage is displayed separately as vendor-reported data.

It has two halves, and the same policies, budgets and session timeline apply to both:

Control plane. An MCP firewall that allows, denies or holds any tool call for human approval; an AI model gateway with budgets and attribution; policy-as-code in YAML and CEL; one runtime timeline per session.
Software factory. Flows run agents, in hosted containers or on your own runners, when GitHub, GitLab, Bitbucket Cloud, Jira, a webhook or a CI job fires. Presets review pull requests, implement issues, triage tickets, scan for vulnerabilities and collect evidence packs. A human can steer a running agent from the console, phone or watch, and the work lands as pull requests you can review.

Both halves are multi-user: invite colleagues and organize teams in Preloop (OSS). Each user can connect devices, runners and agents. Preloop (OSS) has no roles or access controls between users of an account; those are in Preloop Cloud and Preloop Enterprise.

Who it is for:

Platform and security teams that need agents governed before they reach production systems.
Engineering teams running coding agents that want to see the cost and keep an audit trail.
Teams that want tickets and events turned into bounded agent work they can review.

MCP clients connect to /mcp; existing /mcp/v1 configurations remain compatible. See the tested MCP protocol matrix for modern discovery, legacy initialization, and SDK migration details.

Works with OpenClaw, Claude Code, Codex CLI, Cursor, Gemini CLI, Hermes, OpenCode, Devin, Copilot CLI, and any MCP-compatible agent.

Your first five minutes

# 1. Install the CLI (macOS / Linux / WSL)

curl -fsSL https://preloop.ai/install/cli | sh

Windows, native (PowerShell): irm https://preloop.ai/install/cli.ps1 | iex

Details: docs/windows-cli.md

2. Connect it to a control plane

preloop signup # Preloop Cloud (fastest), or preloop login --url http://localhost:3000 # your self-hosted instance

3. Bring local agents under governance

preloop agents discover

preloop agents discover finds local agent configs and reports what each adapter supports. preloop agents onboard configures them: calls routed through the managed MCP entry reach the MCP Firewall, supported model routes use the Gateway. To talk to a running agent, install the runtime plugin with preloop agents install-plugin (or preloop claude for Claude Code). Full walkthrough: onboard local agents.

Watch it work

Onboarding, the MCP firewall, human approvals, and cutting session cost. Recorded against a real stack, no slideware.

Watch the full playlist →

What you get

Jobs teams otherwise buy from several vendors, in one Apache 2.0 stack:

Capability	What it does	Alternatives
MCP Firewall	Govern every tool call. Allow, deny, require approval, require justification. YAML + CEL. Connect OAuth MCP servers with automatic token refresh.	MintMCP, Lunar.dev MCPX, TrueFoundry
AI Model Gateway	OpenAI-, Anthropic- and Gemini-compatible. Budgets, allowed-model lists, token accounting, attribution.	Portkey, Helicone, LiteLLM, Kong AI
Flows	Start an agent when a tracker, webhook, or CI job fires, with the same firewall, approvals, and cost. `preloop flow trigger`.	Custom CI glue, AgentCore Runtime
Private runners	Run flow agents on your own machines over an outbound WebSocket, no inbound ports.	Self-managed CI runners
Agent Control	Talk to long-running agents from the console, phone, or watch. Steer runs with operator notes.	Terminal access
Cost & Budgets	Spend by model, agent, session, API key, flow, and user, including imported Copilot usage.	FinOps dashboards, vendor billing exports
Human Approvals	Mobile, watch, Slack, Mattermost, email, webhook, or `preloop approvals`. Native `Bash`/`Edit`. Agents can `ask_user`.	Custom Slack bots, Peta Desk
Runtime Observability	One session timeline: tool calls, model calls, policy, approvals, spend, outcomes, and synchronized recordings.	AgentOps, Langfuse, LangSmith
Fleet Map	People, devices, agents, models and observed work in one map.	Spreadsheets
Evidence packs	Apache flow presets write `result.json` plus an evidence directory for CRA / AI Act-style work. Not a certification.	Custom GRC folders

AI Agent → Preloop → [Policy]  → Allow / Deny / Require Approval → Execute

→ [Gateway] → Budget + attribution → Model

The software factory

One loop, end to end: an issue event arrives from GitHub, GitLab, Bitbucket Cloud or Jira. A flow starts an agent in a hosted container or on your own runner. Every tool call it makes passes the same firewall and approvals as your own agents, and every model call is charged to the flow's budget. The agent opens a pull request; the Pull Request Reviewer reviews it; review or CI feedback resumes the implementation on the same branch, and the result is a pull request you can review. You can steer the run with an operator note at any point, and the whole run is one timeline.

21 presets ship in this repository, including:

Issue Triage Assistant and issue refinement
Pull Request Reviewer
Delivery Report: a weekly report with proposals for human decisions and project reruns. See what landed, what the available evidence supports against stated criteria, and which gaps deserve investigation
Automated issue implementation
Security audit presets: SBOM verify, exploit check, release audit. They collect machine evidence for CRA- and EU AI Act-style reviews. That is not a conformity assessment, certification, or legal advice.
Observe / Eval
Component due diligence, architecture and strategy review, repo code health review
Standards compliance walk, release backport, weekly model price review

Or write your own. Trigger from CI with the run-flow GitHub Action; run agents on private runners; let a flow start child flows with flow delegation. Tracker setup: Bitbucket and Jira quickstart; Bitbucket Data Center is available as an opt-in provider.

Policy-as-code

version: "1.0"

metadata: name: "Production Safeguards"

approval_workflows:

name: "deploy-approval" timeout_seconds: 600 required_approvals: 1 async_approval: true

tools:

name: "bash" source: mcp approval_workflow: "deploy-approval" justification: required conditions:
expression: "args.command.contains('deploy') && args.command.contains('production')" action: require_approval

Check a sample call before saving with Test this rule or Simulate in the console. Ship it with preloop policy apply (validate / diff / export also exist).

More capabilities

Model providers: OpenAI, Anthropic, Google, Amazon Bedrock, Azure OpenAI, Alibaba Cloud Model Studio, DeepSeek, Mistral, Moonshot, Z.ai, OpenRouter, any OpenAI-compatible endpoint, and runner-proxied local models. Reviewed model pricing; unpriced models stay visibly unpriced.
Restricted CI identity: a machine identity bound to one project and one flow for CI jobs, with an optional read-only native GitHub App clone mode and separate result publication. Guide.
Account kill switch: blocks gateway and tool traffic and stops managed flow runs, with audited recovery. Guide.
Durable audit delivery: opt-in transactional intents for approvals, policy decisions and tool outcomes, with restart replay and backlog alerts. Operations guide.
Session search: keyword search by default, semantic as an opt-in, every search audited. Saved searches.
DORA exports: the AI-agent slice of the ICT inventory and incident record. Guide.
OTLP export: off by default; emits OpenTelemetry GenAI spans. Guide.
Chat: Slack, Mattermost, and Discord for questions, approval votes and operator notes. Chat connections.
Event-driven employees: managed agents doing bounded tasks from tracker and error events. Guide.
Remote sessions and deployment: [remote sessions](docs/guide/runners/remo

只显示了开头部分；其余内容请在仓库中阅读

Preloop

概览

PopNx MCP 的三大支柱

它能帮你做什么

安装

安装之前

需要你填写的值

它能做什么

基础检查i

作者与仓库一致

仍在维护

有许可证

端点有响应

使用 OAuth 登录

工具描述中没有隐藏指令

与“其他”中的其他服务器比较i

常见问题

替代选择

Spec Driven Development

Remote Desktop Commander

HotCodePush

作者的 README

Preloop

Your first five minutes

Windows, native (PowerShell): irm https://preloop.ai/install/cli.ps1 | iex

Details: docs/windows-cli.md

2. Connect it to a control plane

3. Bring local agents under governance

Watch it work

What you get

The software factory

Policy-as-code

More capabilities