Find files by what's in them: photos, PDFs, docs, voice memos and video, on your own computer. EmbeddingGemma 2 + llama.cpp; CLI, app and MCP server for agents.
View on GitHub ↗Describe it. im-looking-for finds it. Search the files on your computer by what's in them — photos, PDFs, documents, voice memos, podcasts and videos — on Windows, macOS and Linux. Everything runs on your machine; nothing is uploaded. Built for people and for AI agents: a search window, a one-word CLI, --json everywhere and an MCP server.
20-second promo (mp4). Every screen in it is a capture of the real app, and the terminal shows the verbatim output of ilf a dog on the beach --json.
im-looking-for is built on Google DeepMind's EmbeddingGemma 2 (Apache 2.0, 740M parameters), which puts text, pictures, sound and video into one shared space and understands 100+ languages, run by llama.cpp. It's a cross-platform take on DigUp, the macOS app that first did this on the desktop.
Python 3.10 or newer, from a clone of this repository:
Prefer no Python? Standalone builds for Windows, macOS and Linux are produced by the release workflow (see Releases).
The first run downloads llama.cpp (12–35 MB) and the model (865 MB) once, pinned to exact versions and checked by SHA-256. It picks the llama.cpp build for your computer: Metal on Apple Silicon, Vulkan on Windows (any recent NVIDIA/AMD/Intel GPU), the CPU on Linux and Intel Macs; Linux with a GPU can choose Vulkan in the setup screen or with ilf setup --backend vulkan (needs libvulkan1). If the GPU build can't start or crashes (a laptop without a working GPU driver), it runs on the processor instead and remembers why (ilf status); Library → Model runs on switches builds later.
From the project README.
Add the radar badge to your README — it shows your project was picked up by MCP Radar and links to this page:
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