Continue.dev: Open-Source AI Code Agent Guide
In the fast-paced world of software development, speed and quality are paramount. While AI coding assistants like GitHub Copilot and Claude have revolutionized how we write code, they often address only one part of the problem: the typing. The modern development lifecycle is filled with friction points that slow us down—context switching between our editor and a browser, manually providing context to an AI, and the ever-present bottleneck of code reviews. These seemingly small inefficiencies accumulate, hindering our ability to ship production-ready code as fast as we think.
What if there was a tool that didn't just help you write code, but helped you ship it? A tool that understands your entire project, automates the tedious parts of your workflow, and integrates seamlessly into your existing environment?
This article explores Continue.dev, an open-source AI code agent designed to do just that. Continue automates the repetitive and time-consuming tasks that are killing your speed, from understanding complex codebases to streamlining pull request reviews. You'll discover everything from installation and configuration to mastering its most powerful features like custom AI agents. By the end, you'll understand how to leverage Continue to reduce friction, improve code quality, and ultimately, ship faster than ever before.
Understanding the Continue.dev philosophy
Before diving into the practical aspects, it's crucial to understand what makes Continue.dev different from other AI tools you might have used. While tools like GitHub Copilot are fantastic for inline code completion and boilerplate generation, their scope is often limited to the immediate file or a small context window. This leads to a common, inefficient workflow for more complex tasks.
Imagine you're dropped into a new, unfamiliar codebase. You encounter a complex function and need to understand its purpose and how it fits into the larger application. The typical process involves highlighting the code, copying it to your clipboard, opening a web browser and navigating to ChatGPT, Claude, or a similar service, pasting the code and writing a prompt, realizing the AI lacks context (so you go back to your editor, find related files, copy their contents, and paste them into the chat), and finally, after several rounds of manual context-feeding, you get a somewhat useful answer.
This entire process is riddled with friction. Continue.dev aims to eliminate it entirely.
Key principles of Continue.dev
Continue lives inside your IDE (this article focuses on its excellent VS Code extension). There is no need to switch to a browser. All interactions happen within the environment where you do your work.
This is arguably Continue's most powerful feature. When you ask it a question, it doesn't just see the code you've highlighted. It automatically gathers context from your entire workspace, including open files, related code snippets, and even Git diffs. This results in incredibly accurate, relevant, and project-aware responses.
Continue is not tied to a single AI provider. It allows you to connect to a wide range of models, including those from OpenAI (GPT-4, etc.), Anthropic (Claude 3), Google (Gemini), and many more. Crucially, it also supports local models via Ollama, enabling you to run powerful AI completely free and privately on your own machine.
Continue's goal is to improve the entire software development lifecycle. Its most advanced features, "Agents," allow you to define custom rules and automate complex processes like code reviews for pull requests, ensuring consistency and quality across your team. It helps you ship, not just type.
Installing and configuring Continue.dev
Setting up Continue is a straightforward process that takes only a few minutes. This section covers installing the VS Code extension and connecting it to both cloud-based and local AI models.
Installing the VS Code extension
First, you need to add the Continue extension to your Visual Studio Code editor. Open your VS Code application and click on the Extensions icon in the Activity Bar on the left side of the window (it looks like four squares). In the search bar at the top of the Extensions view, type Continue. Look for the result named "Continue - open-source AI code agent" from the publisher "Continue". It should be the top result with millions of downloads. Click the blue Install button.
After the installation is complete, VS Code may prompt you to reload the window. If so, click "Reload Required" to finalize the installation. Once reloaded, you will see a new hexagonal Continue logo in your VS Code Activity Bar. This is your new AI control center.
Connecting your first AI model
With the extension installed, the next step is to connect it to an AI model that will power its responses. Continue makes this incredibly easy.
Click on the Continue icon in the sidebar to open the chat panel. At the bottom of the panel, you will see a dropdown menu that shows the currently selected model (it may default to a pre-configured option). Click on this dropdown. From the menu that appears, select "+ Add Model".
This will open a configuration window where you can choose your provider and enter your credentials.
Configuring a cloud-based model
Starting with a powerful cloud-based model like GPT-4 requires an API key from the respective provider (e.g., OpenAI). In the "Add Chat model" window, click on the Provider dropdown and select your desired provider from the list, such as OpenAI or Anthropic. Next, click the Model dropdown and choose the specific model you want to use (e.g., gpt-4-turbo-preview). In the API key field, paste your secret API key from the provider's website, then click the "Connect" button.
Continue will securely save this configuration and the model will now be available for you to use. You can repeat this process to add multiple models from different providers, giving you the flexibility to switch between them depending on the task.
Configuring a local model with Ollama
One of Continue's best features is its support for local models via Ollama. This allows you to run powerful language models directly on your computer, which is completely free (besides the cost of your hardware), private, and works offline.
Before configuring it in Continue, you need to have Ollama running on your system. Visit the Ollama website, download the application for your operating system (macOS, Windows, or Linux), and install it.
Once Ollama is running, open your terminal and pull a model. For example, to get Meta's latest Llama 3 model, you would run:
You can choose from many other models available on the Ollama library, such as codellama for coding-specific tasks or mistral.
Now, go back to the "Add Chat model" window in VS Code. Click the Provider dropdown and select Ollama. In the Model field, type the name of the model you just pulled (e.g., llama3). Click "Connect".
You have now configured both a cloud and a local model! You can easily switch between them at any time using the model selector dropdown at the bottom of the Continue chat panel. This flexibility is a core strength of the platform.
Enhancing your day-to-day coding
With setup complete, exploring how Continue transforms your daily coding tasks reveals its true power. Beyond simple chat, its deep IDE integration and contextual awareness provide a superior developer experience.
Understanding code in context
Understanding a new codebase is a common challenge. Continue solves this without ever leaving VS Code. Navigate to a file in your project that contains a function or block of code you want to understand. Use your mouse to highlight the relevant lines of code.
Press the keyboard shortcut Cmd+L (on macOS) or Ctrl+L (on Windows/Linux). This action does two things: it focuses the Continue chat input field and automatically adds the highlighted code as context for your next query.
Now, simply type your question. Instead of a generic prompt like "explain this," you can ask something much more powerful, such as: "explain how this fits into my codebase". Press Enter.
Continue will generate an explanation that is far more insightful than what you'd get from a standard chatbot. Because it has access to your entire project, its response will reference other files, explain the function's role within the application's architecture, and provide a breakdown that is specific to your code.
Refactoring and editing with interactive diffs
Writing code is an iterative process. Refactoring and making changes are constant activities. Continue streamlines this with an interactive and safe editing experience. When you ask it to modify code, it doesn't just blindly overwrite your work. Instead, it presents the changes as a diff preview.
Highlight a piece of code you wish to refactor or change. Use the Cmd/Ctrl+L shortcut to bring it into the chat context. Enter a prompt describing the change you want. For example: "refactor this function to be more efficient and add error handling".
When Continue generates its response, it will include a side-by-side diff view directly in the chat panel. The left side shows your original code, and the right side shows the proposed changes, with additions highlighted in green and deletions in red.
This diff view is a game-changer. It puts you, the developer, in complete control. You can carefully review every single change before it's applied to your file. Above the diff, you'll find buttons to Accept or Reject the changes. This safety net encourages experimentation and allows you to leverage AI for complex refactoring tasks with confidence.
Automating code reviews with custom AI agents
This is where Continue moves beyond a personal coding assistant and becomes an indispensable tool for team collaboration and workflow automation. Code reviews are essential for maintaining quality, but they are often a significant bottleneck. A pull request (PR) can sit for hours or days waiting for a human reviewer, and much of the feedback can be repetitive (e.g., style nits, forgotten comments, adherence to project standards).
Continue's "Agents" can automate the first pass of a code review, providing instant feedback and freeing up developers to focus on more critical architectural decisions.
Defining your team's coding standards
An Agent is essentially a set of instructions and rules that you provide to the AI. You can create a custom agent that understands what "good code" means for your specific project or team.
In the root of your project, create a new folder named .continue. Inside that folder, create another folder named agents. Inside the .continue/agents/ directory, create a new Markdown file. The name of this file will be the name of your agent. For example, my-review-agent.md.
Open this file and use standard Markdown to define your coding rules. This is your chance to codify your team's standards. Structure it logically with headings.
Here is an example of what your my-review-agent.md file might look like:
Running the review agent on your pull request
Once your agent is defined, you can run it on your current set of changes before you even push your code. Make sure you have some changes staged or committed on a feature branch. Continue will analyze the diff between your current branch and your main branch (e.g., main or master).
In the Continue chat panel, invoke your agent using the @agent command followed by your prompt. For this example, you would type:
Press Enter.
Continue will now perform a comprehensive review. It will read your my-review-agent.md file, analyze every changed file in your PR, and compare the code against your defined rules. The output will be a structured report, broken down by file, highlighting potential issues, and suggesting improvements. It can identify everything from simple formatting violations to more complex logical flaws or security vulnerabilities you defined.
This instant feedback loop is incredibly powerful. It allows you to catch and fix issues before they ever get to a human reviewer, resulting in cleaner PRs, fewer review cycles, and a much faster merge process.
Furthermore, these agents are not confined to your local editor. They can be integrated into your CI/CD pipeline (e.g., GitHub Actions) to automatically review every pull request that is opened, ensuring that standards are enforced consistently across the entire team.
Final thoughts
Continue.dev fundamentally rethinks the role of AI in software development. It moves beyond the simple act of code generation to address the entire workflow, tackling the friction points that truly slow us down.
By integrating deeply into the IDE, providing full-project context, and remaining model-agnostic, it offers a flexible and powerful platform that adapts to your needs. The ability to use free, local models with Ollama makes it accessible to everyone, while its advanced agent-based automation provides immense value for teams striving for speed and quality.
The distinction is clear: GitHub Copilot helps you type faster, but Continue.dev helps you ship faster. It automates repetitive reviews, provides deep codebase insights, and keeps you in a state of flow within your editor. It handles the tedious work so you can focus on what you do best: solving complex problems and building great software.
If you're looking for a way to streamline your development process, reduce bottlenecks, and deliver higher-quality code more efficiently, Continue.dev is an essential tool to add to your arsenal. It is open-source and easy to install, so there's no reason not to give it a try and experience the future of AI-powered development.
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