Sidekick

Sidekick is a RAG toolkit - A native macOS app that allows users to chat with a local LLM that can respond with information from files, folders and websites on your Mac without installing any other software. Powered by llama.cpp.

Overview

Chat with a local LLM that can respond with information from your files, folders and websites on your Mac without installing any other software. All conversations happen offline, and your data stays secure. Sidekick is a local first application –– with a built in inference engine for local models, while accommodating OpenAI compatible APIs for additional model options.

Sidekick supports modern GGUF local models such as Qwen3.5 out of the box through its built-in 'llama.cpp' backend.

Let’s say you're collecting evidence for a History paper about interactions between Aztecs and Spanish troops, and you’re looking for text about whether the Aztecs used captured Spanish weapons.

Here, you can ask Sidekick, “Did the Aztecs use captured Spanish weapons?”, and it responds with direct quotes with page numbers and a brief analysis.

To verify Sidekick’s answer, just click on the references displayed below Sidekick’s answer, and the academic paper referenced by Sidekick immediately opens in your viewer.

Read more about Sidekick's features and how to use them here.

Sidekick accesses files, folders, and websites from your experts, which can be individually configured to contain resources related to specific areas of interest. Activating an expert allows Sidekick to fetch and reference materials as needed.

Sidekick is open-source, written primarily in Swift, with 3,267 GitHub stars under the MIT license. It was last updated on 2026-05-24.

Key capabilities

From the project's documentation:

• A Mac with Apple Silicon
• RAM ≥ 8 GB
• Follow the guide here.
• Georgi Gerganov for llama.cpp

Install

A quick way to get started (always check the official docs for the latest):

brew install --cask arcadi4/tap/sidekick

How it fits a local-AI stack

Sidekick runs on your own hardware, so pair it with a model and a GPU sized to your needs. Use the VRAM calculator to pick a model that fits your card, and see what you can run for hardware guidance. Related RAG toolkits in the directory:

• AnythingLLM
• Haystack
• Khoj
• Onyx

Sources

• Source code & docs: johnbean393/Sidekick
• Official website: https://johnbean393.github.io/Sidekick/

Stats from GitHub, 2026-06-08.