Build with AI in VS Code

Use as much or as little AI as your task needs in Visual Studio Code. Get suggestions as you type, collaborate with an agent beside your code, or coordinate tasks across projects in the Agents window. Combine these approaches as your work changes.

You can choose models from multiple providers and work with the Copilot, Claude, or Codex agent harness, using a supported account or your own API key. Customizations and plugins help you tailor agents to your work.

Continuity lets you pick up agent work across supported interfaces without starting over. Connect to sessions on your own machine through GitHub Environments, or use a cloud session without keeping a local host online.

Try your first agent task

Build your first app with an agent

Build and validate a small app, then review the result. The quickstart uses the GitHub Copilot harness with recommended defaults, so you don't need to configure every option first.

Choose how you work

Switch between different experiences depending on your task, and continue agent sessions across interfaces to pick up where you left off.

An agent uses a model and tools to find relevant code, make changes, and run checks. Both the Chat view and the Agents window run agents. You set the goal, guide the work, and review the results.

For routine work, save a prompt as an automation. Run it locally on a schedule, or use cloud automations Cloud automations is in preview and might change before it becomes generally available. Share feedback about Cloud automations (opens in new tab). for scheduled or event-triggered repository work that continues while your computer is off. Review a manual run before enabling recurring work.

For a focused edit, use inline chat without leaving your code. To compare features for a specific task, use the AI features cheat sheet.

Choose your models, agents, and tools

For a first agent task, use the quickstart without configuring every option. You can adapt the choices below as your needs change.

Choice Options
Models Choose models from multiple providers through GitHub Copilot, or use your own API key (BYOK) or a supported local model.
Agent harnesses Choose between Copilot, Claude, or Codex based on its tools and workflows.
Access and billing Use your existing GitHub Copilot plan, a supported provider account, or an API key. Check requirements for your harness.
Customization Tailor agents with plugins, project instructions, and skills. Connect external tools through Model Context Protocol (MCP) servers.

Continue across interfaces and machines

Continuity means picking up agent work across supported interfaces and machines without starting over. Choose where you interact with an agent while its workspace and execution stay on the host. Session support and requirements differ by harness and destination.

Where you want to work Start here
Across Copilot apps and devices Use GitHub Environments (Preview) to register your development machine and continue its agent sessions from VS Code, the GitHub Copilot app, Copilot CLI, GitHub.com, or GitHub Mobile. Available actions vary by client.
On managed cloud compute Use cloud sessions Cloud sessions is experimental and might change or be removed. for interactive agent work without keeping your own machine online. Reopen the existing cloud session to continue the conversation. Execution stays in the cloud environment, whose availability and lifetime are managed by the service.
Editor or Agents window Use the GitHub Copilot, Claude, or Codex agent harness in VS Code and continue your session across interfaces.
VS Code on another machine Use VS Code-specific remote connections through SSH or a dev tunnel. Dev tunnels also support the browser-based Agents window.

For sessions that run on your own machine, keep the host running and reachable. Cloud sessions use managed compute instead. Start a cloud session for work in the cloud, then reopen that session to continue.

Use your own private repository to start a cloud agent task in VS Code and continue it in Copilot CLI (Experimental).

Where tools run and where the model is hosted are separate choices. An agent can edit files on your machine while sending model requests to a hosted provider.

Stay in control

AI can produce incorrect code or misunderstand your intent. You remain responsible for deciding which changes reach your codebase.

Before using agents on an existing project, review the recommended security baseline. On a managed device, your organization might restrict available agents, models, and tools through enterprise AI policies. If agents don't appear in chat, review the availability troubleshooting steps.

For keyboard, screen reader, and low-vision workflows, learn how to use the Accessible View and other accessibility features.

Next steps