Tabby

Tabby is an open-source, self-hosted AI coding assistant that provides code completion, inline chat, and contextual suggestions directly within your code editor. It is designed as a privacy-focused, on-premises alternative to cloud-based coding assistants like GitHub Copilot.

Installation involves installing the Tabby extension from your IDE’s marketplace (e.g., VS Code), setting up the Tabby server locally or on your infrastructure, and connecting the IDE extension to your Tabby server.

Tabby can run on consumer-grade GPUs, with the amount of VRAM required depending on the LLM model size (e.g., 7B, 13B, etc.); less VRAM is needed for reduced-precision inference (e.g., int8).

Yes, Tabby can run on CPUs, but performance will be slower compared to GPU usage.

Tabby is highly configurable and does not require an external DBMS or cloud service, allowing you to customize settings via configuration files and HTTP API integration.

Tabby offers several features including AI-powered code completion suggestions based on your code context, real-time inline chat communication with the AI assistant within your editor, context providers to connect to various data sources for enriched coding assistance, and team management for secure access and team-wise analytics.

To update Tabby, check the GitHub repository for the latest releases and changelogs, then follow the upgrade instructions in the documentation.

You can find documentation and community support by visiting the Tabby Help Center or the GitHub repository, and for technical issues, you can contact the support team via the provided channels.

Tabby provides a single environment for both integration and live launch, and test API keys are available for development and QA purposes.

Tabby posts real-time alerts on their status page.

Yes, Tabby integrates easily with Cloud IDEs and supports flexible deployment options.

Tabby is self-hosted, ensuring your code and data remain on your infrastructure, and it requires no external DBMS or cloud services.