Continue.dev: Open Source Copilot Alternative

Imagine a code‑completion tool that feels like a seasoned teammate, but without the hefty subscription fee. That’s the promise of Continue.dev, an open‑source alternative to GitHub Copilot that runs locally, respects your privacy, and can be tweaked to match any workflow. In this post we’ll explore how Continue works under the hood, walk through a couple of hands‑on examples, and share pro tips to squeeze the most out of this powerful assistant.

What is Continue.dev?

Continue is a lightweight framework that stitches together large language models (LLMs) with your editor, turning plain text prompts into context‑aware suggestions. Unlike proprietary services that send your code to the cloud, Continue can be configured to run models like StarCoder, GPT‑NeoX, or even your own fine‑tuned checkpoint on a local GPU.

The core idea is simple: when you hit a shortcut, Continue gathers the surrounding buffer, feeds it to the model, and streams back completions that you can accept, reject, or edit on the fly. It supports VS Code, Neovim, JetBrains IDEs, and even a browser‑based UI, making it a versatile drop‑in for most developers.

Getting Started: Installation & Setup

First, ensure you have Python 3.10+ and git installed. The quickest way to spin up Continue is via its official installer script, which pulls the latest release and creates a virtual environment for you.

curl -sSL https://continue.dev/install.sh | bash
# Activate the environment
source ~/.continue/bin/activate
# Verify the installation
continue --version

Once the CLI is ready, you can add the VS Code extension from the Marketplace or install the Neovim plugin via vim-plug. Below is a minimal init.vim snippet for Neovim users.

call plug#begin('~/.vim/plugged')
Plug 'Continue-dev/continue.nvim'
call plug#end()

lua << EOF
require('continue').setup{
    model = "starcoder",
    max_tokens = 256,
}
EOF

After restarting your editor, a new command palette entry called “Continue: Ask” will appear. Press Ctrl+Space (or your configured shortcut) to invoke it.

How Continue Generates Context‑Aware Suggestions

Continue doesn’t just dump raw completions; it builds a prompt that includes the file’s imports, the function you’re editing, and any recent changes in the same project. This “context window” is trimmed to fit the model’s token limit, ensuring the LLM sees the most relevant information.

Internally, the prompt follows a structured template:

def build_prompt(file_content, cursor_position, project_tree):
    imports = extract_imports(file_content)
    surrounding_code = get_surrounding_lines(file_content, cursor_position, window=20)
    return f"""You are an expert Python developer.
Below are the imports:\n{imports}
And the current context:\n{surrounding_code}
Complete the code at the cursor."""

Because the prompt is deterministic, you can experiment with custom templates by editing ~/.continue/prompt.yaml. This flexibility lets you tailor the assistant for domain‑specific languages or frameworks.

Practical Example 1: Auto‑Generating Unit Tests

One of the most tedious tasks in any codebase is writing comprehensive tests. With Continue, you can generate a skeleton test suite for a function in seconds. Open utils.py and place the cursor inside the function you want to test.

Press the Continue shortcut and type the following request:

Write a pytest test for this function covering edge cases.

Continue will stream back a ready‑to‑run test file. Here’s what a typical output looks like:

import pytest
from utils import calculate_discount

@pytest.mark.parametrize("price, discount, expected", [
    (100, 0.1, 90),
    (200, 0.25, 150),
    (0, 0.5, 0),          # edge case: zero price
    (-10, 0.2, -10),      # edge case: negative price
])
def test_calculate_discount(price, discount, expected):
    assert calculate_discount(price, discount) == expected

Accept the suggestion with Enter, and you now have a fully functional test suite that you can run via pytest. The model inferred the function signature, guessed sensible edge cases, and even added comments for clarity.

Practical Example 2: Refactoring Boilerplate Code

Suppose you’re working on a Flask API and need to add a new endpoint that follows an existing pattern. Instead of copy‑pasting and manually tweaking, ask Continue to generate the boilerplate.

Place the cursor after the last route definition and type:

Create a new GET endpoint /users/<int:id> that returns JSON with user details, handling 404 errors.

The assistant will produce a concise snippet:

@app.route('/users/<int:id>', methods=['GET'])
def get_user(id):
    user = User.query.get(id)
    if not user:
        return jsonify({'error': 'User not found'}), 404
    return jsonify({
        'id': user.id,
        'name': user.name,
        'email': user.email
    })

Notice how Continue respects the existing import style and indentation, making the new code feel native to the project.

Real‑World Use Cases

• On‑the‑fly documentation: Ask Continue to generate docstrings for any function, keeping your codebase well‑documented without manual effort.
• Legacy code migration: When moving from Python 2 to 3, prompt the assistant to rewrite print statements, exception handling, and library imports.
• Learning new APIs: Stuck with a new library? Paste a short description and ask for a minimal example; Continue will scaffold a runnable snippet.

Because the tool runs locally, you can safely feed proprietary code to the model without violating NDAs or exposing trade secrets.

Pro Tips for Power Users

• Cache prompts: Enable the prompt_cache option in config.yaml to store recent contexts, reducing latency for repetitive tasks.
• Fine‑tune your model: If you have a domain‑specific dataset (e.g., finance macros), use continue train to adapt StarCoder, then point model_path to your checkpoint.
• Combine with linting: Pipe Continue’s output through black or ruff automatically by adding a post‑process hook.
• Multi‑cursor support: In VS Code, select multiple lines, invoke Continue, and the assistant will generate suggestions for each cursor position simultaneously.

Comparing Continue with GitHub Copilot

Both tools aim to boost productivity, but they differ in key areas. Copilot offers a polished UI and deep integration with GitHub, yet it relies on a cloud service that sends your code to Microsoft’s servers. Continue, on the other hand, gives you full control over the model and data, at the cost of a slightly steeper setup curve.

Feature‑wise, Continue shines in:

1. Privacy: No telemetry unless you enable it.
2. Customizability: Swap models, edit prompts, and add plugins.
3. Cost: Completely free, aside from compute resources.

Copilot still leads in areas like seamless autocompletion for a wide array of languages and instant updates from Microsoft’s research team. The choice ultimately hinges on whether you prioritize openness and control over out‑of‑the‑box convenience.

Community, Contributions, and Roadmap

The Continue project thrives on community contributions. Its GitHub repository follows a classic fork‑pull‑request workflow, and the maintainers welcome additions ranging from new model adapters to UI themes. If you spot a bug, open an issue with a minimal reproducible example; the team is quick to respond.

Upcoming milestones include:

• Native support for Apple Silicon GPUs.
• Integration with LangChain for multi‑step reasoning.
• Better TypeScript and Rust completions via specialized prompts.

Getting involved is as simple as starring the repo, joining the Discord channel, or submitting a PR that adds a missing language snippet. The open‑source nature ensures the tool evolves with the needs of its users.

Best Practices for Maintaining a Healthy Workflow

While Continue can automate many repetitive tasks, it’s essential to keep a disciplined review process. Treat every suggestion as a draft: run your test suite, lint the code, and verify that the generated logic aligns with your design principles.

Here’s a quick checklist you can embed in your CI pipeline:

steps:
  - name: Run tests
    run: pytest
  - name: Lint code
    run: ruff check .
  - name: Verify no TODOs from AI
    run: grep -R "TODO" -n src/ || echo "No TODOs found"

By automating these safeguards, you reap the speed benefits of AI assistance while maintaining code quality.

Conclusion

Continue.dev proves that high‑quality AI‑driven code assistance doesn’t have to come with a price tag or privacy trade‑offs. Its modular architecture, local execution model, and vibrant community make it a compelling Copilot alternative for developers who value control and extensibility. Whether you’re generating tests, refactoring boilerplate, or exploring a new library, Continue can become the silent pair programmer you’ve been waiting for.