Puter MCP
Puter MCP enables AI tools to interact with Puter: manage files, websites, workers, and more
Self-hosted AI agent for office work: run agents, office tools and documents from any MCP client.
选择你使用的 AI 应用,然后复制设置。
通过 3/3 项检查 · 在你的电脑上运行
第一步:你用哪个 AI?
选择你用的应用,步骤会显示在下方。
可连接 Claude,但需要先准备
不需要密钥,不用安装程序 · 仅限电脑上的 Claude 应用(Windows 或 Mac),网页和手机不可用
用 AgentBridge 告诉我你能帮我做什么,然后做一件给我看✓ 如果 Claude 说正在使用 AgentBridge,或回答中出现 AgentBridge 的工具名称,就表示连接成功
菜单名称已于 2026年11月11日 对照 Anthropic 帮助页面核实 · 你的界面可能因版本略有不同
ChatGPT 暂时无法连接 AgentBridge
AgentBridge 需要安装在你的电脑上,而 ChatGPT 只能连接在网上运行的服务器
Claude Code 暂时无法连接 AgentBridge
开发者只提供了电脑版 Claude 应用的安装文件
A · 使用能连接 AgentBridge 的应用
Cursor 暂时无法连接 AgentBridge
开发者只提供了电脑版 Claude 应用的安装文件
A · 使用能连接 AgentBridge 的应用
VS Code 暂时无法连接 AgentBridge
开发者只提供了电脑版 Claude 应用的安装文件
A · 使用能连接 AgentBridge 的应用
依据这个服务器的连接方式和各应用自己的文档,检查于 2026年11月10日。
在你的电脑上运行,适用于
ClaudeGooseLM StudioMsty StudioCherry StudioVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatRaycast
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
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12日
注册名称已通过 GitHub 账户 Graphene-Lab 验证,该账户拥有此仓库
代码最后更新于昨天
使用 AGPL-3.0
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
提问、分享技巧或报告无法使用。按“有帮助”数量排序。
还没有评价
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。
它在 3 项适用的基础检查中通过了 3 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。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于昨天,最新版本(26.11.11)于昨天进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 MCPB, Docker。
同一分类中有:Puter MCP, XcodeBuildMCP, XcodeBuildMCP
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:↓ 每周下载量i★ 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
Puter MCP enables AI tools to interact with Puter: manage files, websites, workers, and more
XcodeBuildMCP provides tools for Xcode project management, simulator management, and app utilities.
XcodeBuildMCP provides tools for Xcode project management, simulator management, and app utilities.
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。
Add agentic power to your work! An AI personal assistant that automates tasks and your work without sacrificing privacy. We're happy to put this powerful tool in your hands to automate your business while reducing staff costs, providing you with the most advanced AI technology in the office automation industry.
AgentBridge is a self-hosted server that runs its own AI agents behind two primary interfaces in a single process: a full-screen chat terminal (TUI) and an HTTP API that speaks the standard OpenAI protocol, so any OpenAI-compatible client drives the same agents.
Chat in the terminal while scripts, bots, and apps use the same agents on the same port: same process, same conversations, no bridges, no synchronization.
To be explicit about what the compatibility is and is not: AgentBridge is OpenAI-standard on the outside, not on the inside. It *is* a standard OpenAI server to any OpenAI client — SDKs, bots and scripts talk plain POST /v1/chat/completions and reach the same agents. It does not run on a standard agent system: the agent core is our own engine (AIOrchestrator/AgentHarness), and its defining concept is that an agent's abilities are compiled .NET assemblies loaded as plugins from the Tools/ folder and driven in-process — no interpreters, no remote tool servers, no standard agent glue between the agent and its actions. That is the Universal Tool System described below, and it is the architectural difference that sets this product apart.
It is the first agentic harness system for next-generation artificial intelligence that introduces significant innovations, raising the bar for all competitors in the industry. The system encapsulates agentic functions in a well-designed sandbox that prevents companies from suffering damage from an AI that is not properly controlled, while at the same time allowing them to entrust real business tasks to the AI, with a very high level of privacy and confidentiality.
AgentBridge stands apart from the harnesses you are familiar with because it is designed to let even people with no technical skills — your grandma, for example — use it without risking damage: file actions are reversible thanks to versioning, the agent is confined to a space bounded by guardrails built in by design, and no code-agent features are provided, precisely to prevent code from being created or executed. It can also be supplied together with our cloud platform, which synchronizes the file area in real time with a private, encrypted cloud located within your own company. In short, it is a product any employer can make available to their employees to support office work and make business processes more efficient. With traditional harnesses, a badly worded prompt could destroy documents, wipe the work drive, or cause other kinds of damage; our focus, by contrast, has been on creating a safe tool for office use.
Why teams choose this architecture
AgentBridge is built on the AIOrchestrator library model: one runtime, one agent core, and one consistent operational flow across terminal and API. The result is faster execution, lower integration overhead, and stronger control than traditional multi-server MCP setups. Read the white paper: Why the AIOrchestrator Library Model Outperforms Traditional MCP Deployments.
AgentBridge is not a single project. It is a solution — a set of .NET projects that form one whole — and the repository you are reading is only the *host* that assembles it. The other pieces live in repositories of their own, and each one is a plugin: a library that gives the agent one family of abilities.
| Layer | What it does | Where it lives |
|---|---|---|
| AgentBridge — the host | Terminal UI, OpenAI-compatible API, sessions, voice, TTS, Telegram and SIP bridges, OfficeManager | this repository |
| AIOrchestrator / AgentHarness — the core | The agent loop, the deterministic memory, and the tool contract every plugin implements | Graphene-Lab/AIOffice |
| The plugins — the abilities | One compiled library per domain: Word, Excel, PowerPoint, PDF, podcasts, CAD, maps, ERP, Odoo, Power BI, Markdown | one repository each — list below |
| The support libraries | Deterministic code intelligence that reads a compiled assembly and derives its tool interface from it | UISupportGeneric · GiraffeAI |
flowchart TB
subgraph Host["AgentBridge — the host (this repository)"] TUI["Terminal UI · Web GUI · API"] LOOP["Agent loop (AgentHarness)"] end subgraph Plugins["Tools/ — the plugins folder"] DOC["OfficeTool → .docx .xlsx .pptx .pdf"] WORD["DocumentTool → Word"] SHEET["SpreadsheetTool → Excel"] DECK["PresentationTool → PowerPoint"] MORE["PodcastTool · MapTool · FreeCADTool ErpTool · OdooTool · PowerBITool …"] end TUI --> LOOP LOOP -- "direct method call in-process, no protocol" --> DOC LOOP --> WORD LOOP --> SHEET LOOP --> DECK LOOP --> MORE
No project references a plugin at compile time. The host hands the agent tool *names*; the plugin host scans the plugins folder (Tools/, next to the executable) at startup, loads the assemblies and resolves those names to real types at runtime — then hot-adds any plugin that appears later, 30 seconds after it lands. Adding an ability means dropping a folder in: no rebuild, no registration, no service to start, no schema to keep in sync.
The mainstream way to make an agent agentic is MCP: the model speaks JSON-RPC to a separate process, which does the work and answers. AgentBridge does the opposite, and that is why this product is a solution of libraries instead of a bundle of servers.
The agent uses the library directly. It has to create or modify a Word document? It calls the library that edits Word documents — exactly the way any application calls a low-level library — and gets the result back as a value, in the same process, as a native method call.
Concretely, what changes:
/tools, from tools.json, or from the tools field of an API request.MCP solved a real problem: reaching software you cannot link against, written by someone else, running somewhere else. As the *general* architecture of a system you build yourself, though, it puts a protocol where a function call belongs, and pays for that in latency, in configuration and in context window. Here the software is ours, it is compiled, and it sits on disk — so the agent gets the libraries themselves, and the architecture the agent follows is the one programmers already use.
MCP stays in the product as an interoperability channel, not as an internal one: outside clients can drive these agents over MCP (Use AgentBridge as an MCP server), and the agent can reach third-party MCP servers it is given. What it never does is speak MCP to its own abilities — those are assemblies, called directly.
The plugins — each a repository of its own, each a compiled library:
Writing one yourself is the whole integration: derive from BaseAgentTool, build, drop the folder into Tools/, and the agent can use it. How the pieces fit together — the system tools inside the engine, the plugins loaded from Tools/, and how a name becomes a type — is described in AgentBridge → AIOrchestrator → tools.
The Download for your OS button detects your platform and starts the right archive (~460 MB, self-contained — no .NET installation needed, Kokoro TTS voices included). Prefer the terminal? One line is enough:
| Platform | One-line install | |
|---|---|---|
| Windows (PowerShell) | `irm https://graphenelab.it/AgentBridge/install.ps1 \ | iex` |
| Linux / macOS | `curl -fsSL https://graphenelab.it/AgentBridge/install.sh \ | bash` |
The one-liners download the latest release for your platform into