
Sidekick
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.
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Sidekick is a native LLM application for macOS that allows users to chat with a local language model to retrieve information from files, folders, and websites without the need for additional software installation. It operates offline, ensuring data privacy and security. Sidekick offers features such as resource access, image generation, inline writing assistance, advanced markdown rendering, fast generation speeds, and more. The tool aims to provide a simple and powerful solution for accessing local, private models with context awareness of user files and content on the web.
README:
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 accomodating OpenAI compatible APIs for additional model options.
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.
Because Sidekick uses RAG (Retrieval Augmented Generation), you can theoretically put unlimited resources into each expert, and Sidekick will still find information relevant to your request to aid its analysis.
For example, a student might create the experts English Literature
, Mathematics
, Geography
, Computer Science
and Physics
. In the image below, he has activated the expert Computer Science
.
Users can also give Sidekick access to files just by dragging them into the input field.
Sidekick can even respond with the latest information using web search, speeding up research.
In addition to its core local-first capabilities, Sidekick now offers an option to bring your own key for OpenAI compatible APIs. This allows you to tap into additional remote models while still preserving a primarily local-first workflow.
Sidekick supports a variety of reasoning models, including Alibaba Cloud's QwQ-32B and DeepSeek's DeepSeek-R1.
Sidekick uses a code interpreter to boost the mathematical and logical capabilities of models.
Since small models are much better at writing code than doing math, having it write the code, execute it, and present the results dramatically increases trustworthiness of answers.
Create, edit and preview websites, code and other textual content using Canvas.
Select parts of the text, then prompt the chatbot to perform selective edits.
Sidekick can generate images from text, allowing you to create visual aids for your work.
There are no buttons, no switches to flick, no Image Generation
mode. Instead, a built-in CoreML model automatically identifies image generation prompts, and generates an image when necessary.
Image generation is available on macOS 15.2 or above, and requires Apple Intelligence.
Markdown is rendered beautifully in Sidekick.
Sidekick offers native LaTeX rendering for mathematical equations.
Visualizations are automatically generated for tables when appropriate, with a variety of charts available, including bar charts, line charts and pie charts.
Charts can be dragged and dropped into third party apps.
Code is beautifully rendered with syntax highlighting, and can be exported or copied at the click of a button.
Use Tools in Sidekick to supercharge your workflow.
Press Command + Control + I
to access Sidekick's inline writing assistant. For example, use the Answer Question
command to do your homework without leaving Microsoft Word!
Use the default keyboard shortcut Tab
to accept suggestions for the next word, or Shift + Tab
to accept all suggested words. View a demo here.
Use Detector to evaluate the AI percentage of text, and use provided suggestions to rewrite AI content.
Diagrammer allows you to swiftly generate intricate relational diagrams all from a prompt. Take advantage of the integrated preview and editor for quick edits.
Instead of making a PowerPoint, just write a prompt. Use AI to craft 10-minute presentations in just 5 minutes.
Export to common formats like PDF and PowerPoint.
Sidekick uses llama.cpp
as its inference backend, which is optimized to deliver lightning fast generation speeds on Apple Silicon. It also supports speculative decoding, which can further improve the generation speed.
Optionally, you can offload generation to speed up processing while extending the battery life of your MacBook.
Requirements
- A Mac with Apple Silicon
- RAM ≥ 8 GB
Download and Setup
- Follow the guide here.
The main goal of Sidekick is to make open, local, private, and contextually aware AI applications accessible to the masses.
Read more about our mission here.
Requirements
- A Mac with Apple Silicon
- RAM ≥ 8 GB
- Clone this repository.
- Run
security find-identity -p codesigning -v
to find your signing identity.- You'll see something like
1) <SIGNING IDENTITY> "Apple Development: Michael DiGovanni ( XXXXXXXXXX)"
- Run
./setup.sh <TEAM_NAME> <SIGNING IDENTITY FROM STEP 2>
to change the team in the Xcode project and download and sign themarp
binary.- The
marp
binary is required for building and must be signed to create presentations.
- The
- Open and run in Xcode.
Contributions are very welcome. Let's make Sidekick simple and powerful.
Contact this repository's owner at [email protected], or file an issue.
This project would not be possible without the hard work of:
- psugihara and contributors who built FreeChat, which this project took heavy inspiration from
- Georgi Gerganov for llama.cpp
- Alibaba for training Qwen 2.5
- Meta for training Llama 3
- Google for training Gemma 3
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