Guaardvark

Name: Guaardvark
Author: guaardvark

by guaardvarkversion 3.3.0i★ –iRuns on your computer

Registry nameio.github.guaardvark/guaardvark

Install

Overview

Description from the maker (original English)

Local AI studio on your GPU: images, video, music, voice, RAG. Needs the Guaardvark app installed.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Design, images & audio

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "guaardvark": {
      "command": "uvx",
      "args": [
        "guaardvark",
        "mcp",
        "serve"
      ]
    }
  }
}

Run this in your terminal

claude mcp add guaardvark -- uvx guaardvark mcp serve

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "guaardvark": {
      "command": "uvx",
      "args": [
        "guaardvark",
        "mcp",
        "serve"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "guaardvark": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "guaardvark",
        "mcp",
        "serve"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

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

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account guaardvark, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Design, images & audioi

More stars than 0% of the other 2175 servers in this category
Published to the registry more recently than 65% of the other 2175 servers in this category
Basic checks equal to or better than 100% of the other 2175 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Guaardvark safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

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).

Is it still maintained?

The GitHub repo last changed –, and the latest version (3.3.0) reached the registry 17 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Figma Context MCP, Claude Real Video, Figma MCP Server

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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by GLips

Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.

Runs on your computer★ 16,165MITUpdated 5 months ago

Install

Claude Real Video

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Let any LLM watch a video locally — and search everything it has ever watched.

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Figma MCP Server

by figma

Figma’s official server gives AI your design data so it can turn designs into code or describe them accurately. Connects to Figma’s cloud.

Runs in the cloud★ 2,152Updated 13 months ago

Install

README from the maker

Open the README

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.

Guaardvark

The self-hosted AI studio. Chat with your own files, hand tasks to agents that use a real desktop and browser, run parallel coding agents, and generate video, images, music and voice. Everything runs locally, on your own GPU.

Quick start · Features · Videos · FAQ · Full capability list · guaardvark.com

https://github.com/user-attachments/assets/6ee72d6a-3a25-4380-92a9-a4b66e8b7e05

Why Guaardvark

It runs on your machine. Language models, retrieval, agents and every generator run on your own hardware, through Ollama, ComfyUI and Guaardvark's own GPU services. No cloud API keys, no per-token or per-minute fees, nothing reported home. Web access, posting and every other outbound path stay off until you switch them on or start them, and all of them are declared in one file, egress.json.
One 16 GB card runs the whole studio. Every image model and every video model except the two larger MiniMax H3 builds run on a single 16 GB NVIDIA card, along with chat and RAG, screen agents, coding swarms, music and voice. One orchestrator hands the GPU to each job, so a render and a language model never fight over VRAM (what runs on 16 GB).
Your coding agent can drive it. A built-in MCP server, fifteen agent skills and plugins for Claude Code, Grok Build and Antigravity let Claude Code, Grok, Antigravity, Cursor, Codex, Gemini CLI and other agents run every flow: "make a music video from this song", "film this script", "train a LoRA of this character", "swarm this refactor".
You approve what matters. Nothing posts and no voice is cloned without an explicit yes from you, and fixes Guaardvark writes for its own code wait for your review. The MCP server hides desktop, shell and browser tools unless you allowlist them.

Quick start

Linux with an NVIDIA GPU (16 GB for video, 12 GB for the Wan 2.2 5B; chat, RAG and agents run on less, see the hardware tiers). A fresh Ubuntu desktop has no curl or git; run sudo apt install -y curl git first.

curl -fsSL https://guaardvark.com/install.sh | bash

Then open http://localhost:5173. The script is install.sh from this repository: it clones to ~/guaardvark and runs ./start.sh, which installs everything on the first run. To read it before it runs, download it first (curl -fsSL https://guaardvark.com/install.sh -o install.sh, read it, then bash install.sh), or install by hand.

Add it to Claude Code (two lines, no clone):

/plugin marketplace add guaardvark/guaardvark

/plugin install guaardvark@guaardvark

Other ways in: Docker · macOS on Apple Silicon · the CLI from PyPI · the full install guide. The current release is in the VERSION file.

Features

Chat with your own files. Local RAG on pgvector: keyword and vector search fused per query, cross-encoder reranking, source citations, and retrieval that shows the chunks and scores behind an answer. Autoresearch tunes retrieval overnight. ▶ Ep 3
Plan mode. Turn it on with /plan and every request is looked into with read-only tools first, then comes back as a plan card you approve, edit or reject; approved steps run one at a time and pause when one fails. It is offered on a model only once that model passes the plan-mode test.
Agents on a real desktop. Screen agents get an Ubuntu desktop of their own, see it with a vision model, click with closed-loop targeting, and work in a real browser while you watch from any page. ▶ Ep 4
Parallel coding agents. A swarm of coding agents, each in its own git worktree, merged back in dependency order. Runs on Claude Code or fully locally through Ollama.
MCP, both ways. An MCP server that plugs into Claude Code, Cursor, Codex, Gemini CLI, Zed and more with one command, and an MCP client so chat can use tools from other servers. ▶ Ep 21 · ▶ Ep 16
Video generation. 13 local video models across five families (Wan 2.2, LTX, HunyuanVideo, CogVideoX, and MiniMax H3 with its own soundtrack), video LoRAs, a batch queue, frame interpolation, and one click into ComfyUI. ▶ Ep 6
LoRA training and consistent characters. Train character, environment and prop LoRAs from a few reference photos on your own GPU, import LoRAs trained elsewhere, and keep the same character across images, video, music videos and Film Crew productions. Details
Images. Z-Image, FLUX.1, FLUX 3 Klein, Krea 2, MOSS-Image, Qwen-Image 2.1, SDXL and Sana Sprint generation (Sana Sprint runs even on 8 GB cards), an Image Edit page that keeps a face across new scenes and scores the likeness, photo edits by instruction, inpainting and outpainting, and 4K/8K upscaling. ▶ Ep 5
Music and voice. Full songs with vocals, sound effects, three neural text-to-speech engines, consent-gated voice cloning, and hands-free voice chat. ▶ Ep 7
Directors. A beat-synced music-video director, a five-role Film Crew that turns a logline into a finished video, and a built-in video editor. ▶ Ep 8 · ▶ Ep 9
Self-improvement. Ask how part of Guaardvark works and it answers from its own source with checked file:line links. A change it makes to itself is planned, checked, undone when a check fails, and one button reverts it. The System Map draws the whole codebase from its real imports. ▶ Ep 11 · ▶ Ep 14
Command center. A GPU orchestrator, a plugin manager with a live VRAM budget, jobs and scheduling, schema-aware backups, and a multi-machine Interconnector. ▶ Ep 12
Terminal and API. The guaardvark CLI with an interactive REPL, plus REST and Socket.IO. ▶ Ep 20

The complete, enumerated list of models, tools, plugins and pages is in [CAPABILITIES.md](CAPABILITIES.md).

Chat with your own files (local RAG)

Upload documents, build a knowledge base per project, and ask questions. Answers are grounded in your files and cite where they came from.

Hybrid retrieval. Postgres full-text keyword search and vector search, fused with a per-query weighting that leans toward keywords for identifier-like queries and toward meaning for prose.
Cross-encoder reranking. A reranker (one Install in Settings) reads the query and passage together and reorders the candidates. It is admitted against free VRAM and falls back to CPU rather than competing with image or video generation.
Layout-aware parsing. PDF, DOCX and PPTX are parsed for reading order and section headers, and PDF passages carry the page they came from. (Scanned documents need OCR, which is not installed by default; they say so rather than indexing as empty.)
Grounded citations. Every chunk carries its source file and section breadcrumb (and, for PDFs, the page), written into the embedded text as well, so a passage lifted from the middle of a document still says where it came from.
Smart chunking. Code gets AST-informed chunks, so a function stays one chunk; prose gets semantic splitting.
Postgres-backed vector store. Embeddings live in pgvector next to the rest of your data, with an ANN index and a persisted full-text index, covered by the same backups.
Retrieval that shows its work. Live retrieval tests show the actual chunks and scores behind an answer (Episode 3), and every query can return a trace of which retrieval legs ran, so a degraded answer can be told apart from a bad one.
More: index profiles (the same documents projected more than one way), corpus-level summaries for "what are the themes across all of this", a choice of embedding models from 300M to 4B+, entity extraction, and per-project isolation.

Autoresearch tunes retrieval while you sleep. An overnight loop proposes changes to chunking and retrieval settings, scores them with an LLM-as-judge harness, keeps the wins and reverts the regressions, bounded by a wall-clock budget, a run ledger and a circuit breaker. The morning report says what changed and why.

One chat box, three speeds. Every message goes through a three-tier router that picks the fastest path to the right answer (Episode 2):

Tier	Name	Latency	LLM calls	When it fires
1	Reflex	<100 ms	0	Deterministic actions such as media controls, pattern-matched with no inference
2	Instinct	1–3 s	1	Conversation and most requests: single questions, web searches, image generation, vision
3	Deliberation	5–30 s	3–10	Multi-step research, analysis chains, complex agent tasks (a full ReACT loop)

Desktop and vision tools are only offered to the model while the agent screen is active.

Agents that use a real desktop and browser

Guaardvark's agents control a real Ubuntu desktop (Xvfb and XFCE on display :99): the same Applications menu, desktop icons and taskbar you would see if you connected to the box over VNC. They see the screen through a vision model, move the mouse, click, type, browse, and check their own work. The desktop is isolated from yours: its own XDG_DESKTOP_DIR and XDG_CONFIG_HOME, so the agent's files and settings never collide with your session.

One vision brain. Gemma4 sees the screen, decides the next action and returns click coordinates in a single inference call.
Closed-loop targeting. The servo moves, checks where the cursor actually landed, and corrects until it is on the target, rejecting targets the model cannot really see.
Live reasoning. Every step (act

Only the beginning is shown; read the rest in the repo

Guaardvark

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Design, images & audioi

Common questions

Alternatives

Figma Context MCP

Claude Real Video

Figma MCP Server

README from the maker

Guaardvark

Why Guaardvark

Quick start

Features

Chat with your own files (local RAG)

Agents that use a real desktop and browser