Agent-Native Calendar
Agent-Native Google Calendar - manage events, sync, and public booking
Description from the maker (original English)
Self-hosted AI agent for office work: run agents, office tools and documents from any MCP client.
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Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026
The registry name was verified with the GitHub account Graphene-Lab, which owns the repo
We have not read this repo from GitHub yet
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This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
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It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (26.11.3) reached the registry 3 days ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need MCPB, Docker on your computer.
In the same category: Agent-Native Calendar, Excel MCP Server, MCP Server for Excel
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Agent-Native Google Calendar - manage events, sync, and public booking
Lets AI create and edit Excel files itself, including formulas, charts and PivotTables, without Excel installed.
Excel automation for AI - Sheets, Power Query, DAX, VBA, Tables, Ranges and more. Windows only.
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
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
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