Analysis updated 2026-05-18
Let a text-only coding model like DeepSeek describe a screenshot without uploading it to the cloud.
Have an AI coding assistant check its own UI work by taking and reading a screenshot.
Ask a local vision model a focused question about an image already on your clipboard.
Debug an error dialog or terminal output by having the AI read a screen capture directly.
| nopainnullgain/localeyes | 95ge/cfquant | aevella/sky-pc-mcp-companion | |
|---|---|---|---|
| Stars | 26 | 26 | 26 |
| Language | Python | Python | Python |
| Setup difficulty | moderate | moderate | moderate |
| Complexity | 3/5 | 3/5 | 3/5 |
| Audience | developer | developer | vibe coder |
Figures from each repo's GitHub metadata at analysis time.
Requires installing Ollama and downloading a roughly 4 GB vision model first.
LocalEyes gives sight to text only AI coding models, such as DeepSeek or CodeLlama, that are otherwise unable to see screenshots, error dialogs, or user interfaces. It does this by pairing the text model with a separate vision model that runs locally on your own computer through a tool called Ollama, so no images are ever uploaded to a cloud service. Normally, if a text only model needs to understand something visual, a common workaround is uploading a screenshot to a different cloud based model like GPT-4 or Gemini and asking it to describe what is on screen. LocalEyes avoids this by running its own local vision model, so the screenshot never leaves your machine and the coding assistant's workflow does not need to be interrupted. Setup involves downloading a specific vision model called qwen2.5vl through Ollama, installing a Python image library, cloning the repository, and running a small install script. Once set up, there are two ways to use it. In one, you take a screenshot yourself and ask the model a question about it. In the other, the text based model decides on its own, during its normal work, that it needs to look at the screen, and it runs the vision tool itself, for example after finishing a build or while debugging. Under the hood, the tool captures an image from the screen, clipboard, or a saved file, sends it to the local Ollama vision model running on the same machine, and gets back a written description that the text based model can then read and reason about. The vision model and its settings, such as which model to use and how long to wait for a response, can be changed in a configuration file or through environment variables. LocalEyes has been fully tested on Windows and is expected to work, though untested, on macOS and Linux, with some extra setup needed for the Wayland display system on Linux. It is written in Python and released under the MIT license.
LocalEyes lets text-only AI coding models see screenshots and images using a local Ollama vision model, so nothing is uploaded to the cloud.
Mainly Python. The stack also includes Python, Ollama, Qwen2.5-VL.
MIT license: use, modify, and distribute freely, including for commercial purposes, as long as you keep the copyright notice.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly developer.
This repo across BitVibe Labs
Verify against the repo before relying on details.