GitHub Copilot

GitHub's official AI coding assistant spans editors, the terminal, desktop, and the GitHub website, supporting the full workflow from issues and code generation to PR review and merge, with multiple model and agent controls.

AI Developer Tools#Programming#Code Completion#GitHub

Updated: 2026-10-26

What is GitHub Copilot?

GitHub Copilot is GitHub's official AI programming assistant, officially positioned as "your AI programming agent". Its coverage has long gone beyond code completion within the editor: it can participate in the entire workflow from opening an issue to writing, reviewing and merging PRs, and can be used in editors, terminals, desktop applications and GitHub's web interface. The official emphasizes "not one size fits all" — you can choose from multiple leading large models based on speed, accuracy or cost; it also emphasizes "your agent, your way" — you can either use the official Copilot or connect self-built agents or existing third-party agents. The official also provides a case: Grupo Boticário increased developer productivity by 94% with the help of Copilot, and claims to offer multiple plans such as a free tier and a Pro tier.

Who is it for?

Individual developers and teams already using GitHub; enterprises that wish to centrally manage the permissions, usage volume and compliance of AI coding tools; developers who want to seamlessly use AI assistants between editors and command lines; and individual learners new to AI programming who want to get started with a low barrier. For open-source project maintainers, its auxiliary capabilities in issue handling and PR review can also reduce maintenance burdens; for teams with strict norms, the management and control capabilities of the enterprise version provide a means of centralized closure.

Key features

In-editor completion and conversation

It provides real-time code completion in mainstream editors such as VS Code, and also supports conversational generation, explanation, and refactoring of code.

Full Process Participation

From opening an issue to reviewing and merging PRs, the official states that it can participate in every step, reducing context switching.

Multi-platform Support

Available in code editors, terminal CLI, desktop applications, and GitHub web interface.

Multiple Models Available

The official states that you can choose from multiple leading large models to balance speed, accuracy, and cost.

Agent System and Enterprise Management & Control

Supports Copilot's built-in Agents, self-built Agents, and third-party Agents, and manages the progress of multi-step tasks on GitHub; it also provides usage management and security control for MCP integration, suitable for organizational-level access.

How to use GitHub Copilot

Start by completing operations within the editor, and after familiarizing yourself, gradually enable the CLI, desktop applications, and Agent tasks.
when the team is onboarded, first define rules such as "which repositories allow AI commits" and "which operations require manual confirmation", and compile usage conventions for the team (do not include secrets in prompts, do not enable AI commits for sensitive repositories).
Pay attention to official updates—Agent-related capabilities iterate rapidly, so review enterprise-level control items regularly.
Periodically check new capabilities against the official update log, turn on switches that suit your team, and turn off those that are unnecessary, keeping your usage in sync with product evolution.

Things to know

AI-generated code still requires human review, which is the bottom line—and the official also recommends incorporating generated code into the normal review process. The free tier and Pro tier differ in quota and model access scope; the enterprise edition focuses on usage quotas and compliance control, with the specific prices subject to the official GitHub pricing page. Before enterprise access, data compliance should be clarified: code snippets may be sent to the provider of the selected model, so confidential code repositories need to be evaluated; different models have obvious differences in code quality and style, so teams should unify model selection. When accessing a self-built Agent or MCP service, first conduct a security review to avoid exposing sensitive repositories or credentials to third parties. Completion quality depends on context, and private code repositories need to enable indexing for better project-level understanding. The free tier is friendly to individual developers but has limited model choices and quotas; when selecting a large model, consider the access conditions and data compliance in your region to avoid finding it unavailable after launch.