Blocks are the components you use to build a custom AI code assistant. These include , , , , , , and .

Introduction

Continue

To quickly understand what you can do with Continue, check out each tab below.

Overview

Get Continue installed in your favorite IDE in just a few steps.

Install

Continue can be deeply customized. For example you might:

These blocks form the foundation of the entire assistant experience, offering different specialized capabilities:

Model Blocks

These are the specialized instructions that shape how models respond:

Prompt Blocks

Rules allow you to provide specific instructions that guide how the AI assistant behaves when working with your code. Instead of the AI making assumptions about your coding standards, architecture patterns, or project-specific requirements, you can explicitly define guidelines that ensure consistent, contextually appropriate responses.

Rules Blocks

Model Context Protocol servers provide specialized functionality:

MCP Blocks

Troubleshooting

Explore Continue's advanced capabilities for power users and complex development scenarios.

Context Providers allow you to type '@' and see a dropdown of content that can all be fed to the LLM as context. Every context provider is a plugin, which means if you want to reference some source of information that you don't see here, you can request (or build!) a new context provider.

Context Providers

Connect with the Continue community to discover, share, and collaborate on AI development tools.

Sharing

When managing your custom assistants within an organization, you might want to take advantage of your usual source control workflows. Continue makes this easy with a  that automatically syncs your YAML files with hub.continue.dev. We are also planning on adding automations for GitLab, BitBucket, Gitee, and others. If you are interested, please reach out to us on .

Source Control

This comprehensive guide will walk you through setting up Ollama with Continue for powerful local AI development. Ollama allows you to run large language models locally on your machine, providing privacy, control, and offline capabilities for AI-assisted coding.

Using Ollama with Continue: A Developer's Guide

Continue makes it easy to code with the latest open-source models, including the entire Llama 3.1 family of models. Llama 3.2 models are also supported but not recommended for chat, because they are specifically designed to be small or multi-modal.

Using Llama 3.1 with Continue

How to Set Up Codestral

Continue can be run even on an air-gapped computer if you use a local model. Only a few adjustments are required for this to work.

Running Continue without Internet

While Continue comes with  out of the box, you might wish to set up your own vector database and build a custom retrieval-augmented generation (RAG) system. This can allow you to access code that is not available locally, to index code a single time across all users, or to include custom logic. In this guide, we'll walk you through the steps it takes to build this.

Custom code RAG

Continue offers several way to provide your custom context to the extension.

Build Your Own Context Provider

You can deploy a model in your , , , , or  using:

How to Self-Host a Model

config.yaml Reference

config.json Reference

Continue's YAML configuration format provides more readable, maintainable, consistent configuration files, as well as new configuration options and removal of some old configuration options. YAML is the preferred format and will be used to integrate with future Continue products. Below is a brief guide for migration from  to .

Hub