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cat README.md

Name: Tabby agent decision packet
Creator: OpenAgent.bot
License: Apache-2.0

Tabby

Self-hosted AI coding assistant with code completion, chat, and agent capabilities that runs entirely on your infrastructure.

# 32K Stars · 1.6K Forks · Apache-2.0 License // verified 2026-06-04

▶ Visit official site 📄 Open repository

tabby/main

$docker run -it --gpus all -p 8080:8080 tabbyml/tabby serve --model StarCoder-15B

Installing Tabby...

Tabby ready

$tabby --help

Reading tabby configuration & environment...

●Waiting for input...

# core strengths

What makes Tabby different

◆

Fully self-hosted

Runs entirely on your infrastructure with no external API calls or data leaving your network.

For regulated industries (healthcare, finance, government), Tabby is one of the few viable AI coding assistants because no code ever leaves the organization.

◆

Multi-editor support

Works with VS Code, JetBrains, Vim, Emacs, and other editors through standardized plugins.

Teams with diverse editor preferences can standardize on one self-hosted AI backend.

◆

Code completion + chat + agent

Provides inline code completion, conversational chat, and agentic task execution from a single self-hosted service.

One deployment covers the full spectrum of AI-assisted development without relying on external services.

◆

Open model support

Works with any open-source model including Llama, DeepSeek, Qwen, and fine-tuned custom models.

Teams can choose, fine-tune, or build custom models for their specific codebase and domain.

# quick start

Your first command

terminal

$docker run -it --gpus all -p 8080:8080 tabbyml/tabby serve --model StarCoder-15B

●Ready. Run --help to explore.

→ Open the GitHub repository → Read the documentation → Visit the project website

# use cases

How developers use Tabby

Air-gapped development

Deploy Tabby in an air-gapped environment with no internet access, running entirely on local GPU infrastructure.

Enterprise compliance

Meet SOC 2, HIPAA, or GDPR requirements by keeping all code and AI processing within your controlled infrastructure.

Custom model fine-tuning

Fine-tune open models on your codebase for better completion quality, then serve them through Tabby.

# comparison

How Tabby compares

Choose Tabby for self-hosted data sovereignty vs GitHub Copilot

Tabby runs entirely on your infrastructure with no data leaving your network. Copilot sends code snippets to GitHub's servers for processing.

# faq

Questions

Q: What should I check before using Tabby?

Start with one safe workflow for Tabby. Inspect official setup instructions, required credentials, execution logs, approval points, and failure recovery before expanding from a sandbox task into production automation.

Q: Is Tabby open source?

Tabby is listed with Apache-2.0 based on the official source links in this profile. Re-check the repository, model card, or docs before production use.

Q: What hardware does Tabby need?

Tabby requires a machine with a GPU for good performance. It supports NVIDIA GPUs and Apple Silicon, and can run CPU-only with reduced speed.

Decision brief

Should you use Tabby?

JSON

Best for

Enterprise teams with strict data sovereignty and compliance requirements
Developers working in air-gapped or offline environments
Teams that want full control over their AI coding infrastructure and model selection

Not for

Individual developers who prefer cloud-hosted coding assistants
Users who want frontier models without managing local GPU infrastructure

Trust and freshness

Verified 2026-06-04
License: Apache-2.0
Repo: TabbyML/tabby
Open-source signal

Deployment

local, self hosted, cloud

Permission surface

shell/files

Decision signals

Local first, Self-hostable

Agent packet

Structured decision data for Tabby

This packet is the compact machine-readable view agents should use before following source links or taking action.

Full JSON Agent packet Markdown brief

Capabilities

local inference, workflow orchestration

Constraints

open source, self hosted, local first

Deployment

local, self hosted, cloud

Permission surface

shell/files

Recommended workflows

Coding agent workflow, Local or private AI stack

Overview

What Tabby does

What it is

Tabby is an open-source, self-hosted AI coding assistant that provides code completion, chat, and agent capabilities. It runs entirely on your infrastructure with no external dependencies.

Why it matters

Most AI coding tools require sending code to external APIs. Tabby is designed for teams that cannot or will not let code leave their network. Combined with support for any open-source model, Tabby is the most practical option for enterprise, government, and regulated industry deployments.

How to evaluate it

Facts

Known metadata and operating surface

These fields are separated from editorial interpretation so agents can reason over facts and missing checks.

Resource type agent

Category Agents

Maturity active

Difficulty Unknown

License Apache-2.0

Pricing open source

Verified 2026-06-04

Source confidence high

Risk level moderate

Fit matrix

Where Tabby fits in an agent stack

strong

Coding agent workflow

Tabby has multiple signals for coding agent workflow, including matching tags, capabilities, category, or positioning.

Run a small repository change and inspect the diff, tests, and rollback path.
Confirm official docs, current maintenance, license, and runtime constraints before production use.

strong

Local or private AI stack

Tabby has multiple signals for local or private ai stack, including matching tags, capabilities, category, or positioning.

Verify hardware requirements, data path, storage, and whether all calls stay in your environment.
Confirm official docs, current maintenance, license, and runtime constraints before production use.

partial

Connector or protocol layer

Tabby has at least one signal for connector or protocol layer, but should be checked against a real task before adoption.

Connect one low-risk service, then inspect schemas, auth scope, errors, and logs.
Confirm official docs, current maintenance, license, and runtime constraints before production use.

partial

Reusable skill workflow

Tabby has at least one signal for reusable skill workflow, but should be checked against a real task before adoption.

Run one skill end to end and check whether it produces evidence or structured output.
Confirm official docs, current maintenance, license, and runtime constraints before production use.

weak

Browser automation

Tabby is not primarily positioned for browser automation in the current metadata.

Run one non-sensitive website task and inspect clicks, waits, retries, and changed URLs.
Confirm official docs, current maintenance, license, and runtime constraints before production use.

weak

Evaluation and observability

Tabby is not primarily positioned for evaluation and observability in the current metadata.

Add one repeatable test case and confirm results can run again in review or CI.
Confirm official docs, current maintenance, license, and runtime constraints before production use.

Inputs and outputs

What an agent should inspect

Likely inputs

Repositories, files, issues, terminal output, and test results
Prompts, messages, documents, images, or model inputs
Official setup instructions and a small real workflow

Likely outputs

Diffs, commits, explanations, test results, or review notes
A decision on whether this resource fits the target workflow

Evidence

Sources, claims, and missing checks

Claims are marked separately from source links so future crawlers and reviewers can update them without rewriting the page.

GitHub github

Repository source for code, license, issues, releases, and implementation details.

Homepage homepage

Official or project-controlled source for this resource profile.

Docs docs

Documentation source for setup, API shape, and operational behavior.

verified

Tabby is listed as open source.

License metadata: Apache-2.0

verified

Tabby has a recorded GitHub repository: TabbyML/tabby.

Resource facts and GitHub source link.

inferred

Tabby supports these recorded deployment modes: local, self hosted, cloud.

OpenAgent decision signal metadata.

inferred

Tabby is tagged with local inference, workflow orchestration capabilities.

OpenAgent capability taxonomy.

Missing checks

Repository freshness has not been recorded.

Next action

How to start evaluating Tabby

Inspect repository

Check license, recent activity, issues, examples, and security-sensitive code paths.

Open source

Open Homepage

Start from the official source before adopting third-party instructions.

Open source

Read setup docs

Use docs as the source of truth for installation and supported interfaces.

Open source

Deploy Tabby

Deploy Tabby via Docker with GPU support. Once running, install the editor extension and connect to your self-hosted instance.

docker run -it --gpus all -p 8080:8080 tabbyml/tabby serve --model StarCoder-15B

Compare

Alternatives and nearby resources

Use related resources to compare category fit, license, deployment model, and first-workflow behavior.

FAQ

Common questions about Tabby

What should I check before using Tabby?

Is Tabby open source?

Tabby is listed with Apache-2.0 based on the official source links in this profile. Re-check the repository, model card, or docs before production use.

What hardware does Tabby need?

Tabby requires a machine with a GPU for good performance. It supports NVIDIA GPUs and Apple Silicon, and can run CPU-only with reduced speed.

Can Tabby work fully offline?

Yes. Tabby is designed for fully offline operation with no internet access required once deployed.