
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 an 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 is saved locally.
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.
Sidekick accesses files, folders, and websites from your experts, which can be individually configured to contain resources related to specific areas of interest. Activating a expert allows Sidekick to access the relevant information.
Because Sidekick uses RAG (Retrival Augmented Generation), you can theoretically put unlimited resources into each expert, and Sidekick will still find information relevant to your request to aid its response. This is in sharp contrast to most services, including OpenAI's ChatGPT, which can only ingest ~45 pages of text.
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
, allowing Sidekick to reply with information from that expert.
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.
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.
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 availible on macOS 15.2 or above, and requires Apple Intelligence.
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!
Markdown in 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.
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. Sidekick also supports speculative decoding, which can increase performance by up to 51%.
Optionally, offload generation to speed up generation while extending the battery life of your MacBook.
Requirements
- A Mac with Apple Silicon
- RAM ≥ 8 GB
Download
- Download the disk image from Releases, and open it.
The main goal of Sidekick is to make open, local, private models accessible to more people, and allow a local model to gain context of select files, folders and websites.
Sidekick is a native LLM application for macOS that runs completely locally. Download it and ask your LLM a question without doing any configuration. Give the LLM access to your folders, files and websites with just 1 click, allowing them to reply with context.
- No config. Usable by people who haven't heard of models, prompts, or LLMs.
- Performance and simplicity over developer experience or features. Notes not Word, Swift not Electron.
- Local first. Core functionality should not require an internet connection.
- No conversation tracking. Talk about whatever you want with Sidekick, just like Notes.
- Open source. What's the point of running local AI if you can't audit that it's actually running locally?
- Context aware. Aware of your files, folders and content on the web.
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 3.1
- Google for training Gemma 2
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