Continue Complete Guide: From Beginner to Expert

Overview

Continue is a powerful, open-source AI coding assistant designed to integrate seamlessly into your development workflow within VS Code and JetBrains IDEs (such as IntelliJ, PyCharm, WebStorm, etc.). Unlike many proprietary AI tools that lock users into specific models or cloud services, Continue gives developers full control by allowing them to connect any local or cloud-based large language model (LLM) — from OpenAI’s GPT-4 to self-hosted models like Llama 3, Mistral, or Phi-3.

As of 2026, Continue has evolved beyond simple code completion. It now supports continuous integration (CI) automation, custom AI agents, automated pull request reviews, and even test generation and debugging workflows. Its architecture emphasizes privacy, extensibility, and developer autonomy, making it an ideal choice for teams that want AI assistance without sacrificing security or flexibility.

Whether you're a solo developer running small models on your laptop or part of a large engineering team automating code quality checks in CI pipelines, Continue adapts to your needs. With its modular configuration system, plugin ecosystem, and real-time chat interface embedded directly in your IDE, it acts less like a passive autocomplete tool and more like a programmable AI teammate.

Core Features

Continue stands out due to its rich feature set tailored for modern software development practices. Below is a detailed breakdown of its core capabilities:

Feature	Description	Supported Environments	Key Benefit
Local & Cloud LLM Support	Connect to any LLM via API (OpenAI, Anthropic, Gemini) or run locally using Ollama, LM Studio, Hugging Face, or llama.cpp	VS Code, JetBrains	Full control over data privacy and cost; no vendor lock-in
In-IDE Chat Interface	Native chat panel inside your editor for asking questions, generating code, or explaining logic	VS Code, JetBrains	Context-aware responses with access to current file/project
Custom AI Agents	Define reusable agent workflows (e.g., “write tests”, “refactor function”) using YAML configs	All environments	Automate repetitive coding tasks across projects
CI/CD Integration	Run Continue in CI pipelines to auto-review PRs, generate tests, detect bugs	GitHub Actions, GitLab CI, Jenkins	Improve code quality before merging
Auto-Complete & Inline Suggestions	Smart code suggestions powered by your chosen LLM	VS Code, JetBrains	Faster coding with context-sensitive completions
Privacy-First Design	No telemetry; all code stays local unless explicitly sent to cloud APIs	Any setup	Ideal for enterprise and regulated industries
Extensible Plugin System	Add new models, tools, or UI components via Python/JavaScript plugins	Developer-customized	Adapt Continue to unique workflows

Additionally, Continue includes advanced features such as:

• Context-aware editing: Understands project structure, imports, and dependencies.
• Error explanation and fix suggestions: Highlights runtime/compiler errors and proposes fixes.
• Documentation generation: Automatically creates docstrings, READMEs, and API docs.
• Multi-model routing: Route different tasks to different models (e.g., use GPT-4 for design, Llama 3 for refactoring).

How to Use

Follow these step-by-step instructions to install, configure, and start using Continue effectively in your development environment.

Step 1: Installation

For VS Code:

1. Open VS Code.
2. Go to the Extensions marketplace (Ctrl+Shift+X).
3. Search for Continue.
4. Click Install.
5. Reload the window when prompted.

For JetBrains IDEs:

1. Open your JetBrains IDE (e.g., PyCharm, IntelliJ).
2. Navigate to Settings > Plugins.
3. Search for Continue.
4. Install the plugin and restart the IDE.

💡 Alternatively, download the latest .vsix or .zip package from https://continue.dev and install manually.

Step 2: Initial Setup

After installation:

1. Press Ctrl/Cmd+Shift+P to open the command palette.
2. Type Continue: Start.
3. The first time you run it, Continue will prompt you to create a config.json file in your project root or user config directory (~/.continue/config.json).

Example minimal config:

{
  "models": [
    {
      "title": "My Local Llama",
      "model": "llama3",
      "provider": "ollama"
    }
  ],
  "systemMessage": "You are a helpful coding assistant."
}

Step 3: Connect a Model

Choose one of the following options:

Option A: Use a Local Model (Recommended for Privacy)

1. Install Ollama or LM Studio.
2. Pull a model:

   ollama pull llama3
   

3. In your config.json, add:

   {
     "title": "Llama 3",
     "model": "llama3",
     "provider": "ollama",
     "apiBase": "http://localhost:11434"
   }
   

Option B: Use a Cloud Model (e.g., OpenAI)

1. Get your OpenAI API key from platform.openai.com/api-keys.
2. Add to config.json:

   {
     "title": "GPT-4",
     "model": "gpt-4-turbo",
     "provider": "openai",
     "apiKey": "your-api-key-here"
   }
   

🔐 Never commit API keys to version control. Use environment variables instead:

"apiKey": "${OPENAI_API_KEY}"

Then set export OPENAI_API_KEY=sk-... in your shell.

Step 4: Use the Chat Interface

Once configured:

1. Open the Continue sidebar in your IDE (Ctrl+Shift+C).
2. Ask questions like:
   • “Explain this function.”
   • “Write a unit test for this class.”
   • “Refactor this loop to be more efficient.”
3. Highlight code and right-click → Continue: Send to Continue to provide context.

The assistant will respond with code, explanations, or edits — often offering inline diff-style changes.

Step 5: Create Custom Agents (Advanced)

Agents allow automation of common workflows. To define one:

1. In your config.json, add an agents array:

"agents": [
  {
    "name": "Write Tests",
    "prompt": "Generate pytest unit tests for the selected code. Include edge cases.",
    "description": "Creates comprehensive tests"
  },
  {
    "name": "Fix Error",
    "prompt": "Analyze the error message and suggest a fix.",
    "description": "Debugs runtime issues"
  }
]

2. Select code → Right-click → Continue: Run Agent → Choose agent.

This enables consistent, repeatable AI interactions across your team.

Step 6: Integrate with CI (Optional)

To use Continue in CI pipelines (e.g., GitHub Actions):

1. Set up a script that runs Continue headlessly:

# .github/workflows/continue.yml
name: Code Review
on: [pull_request]

jobs:
  review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Run Continue Agent
        run: |
          npx continue ci --agent="Review PR" --output=comments
        env:
          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}

2. Configure the CI agent in config.json:

"ci": {
  "agents": ["Review PR", "Generate Changelog"]
}

This allows automated feedback on every pull request — catching bugs early and enforcing standards.

Pricing

Plan	Cost	Features
Open Source (Community Edition)	Free	Full functionality, local/cloud LLMs, agents, CI support, no usage limits
Enterprise Support Add-on	Contact Sales	SLA guarantees, private deployment consulting, team training, SSO integration
Cloud Hosting (Beta – Coming Q3 2026)	TBD	Managed Continue instances with dashboard monitoring (not yet available)

✅ No hidden costs: Since Continue is fully open-source (GitHub repo), there are no subscription fees, paywalls, or token limits. You only pay for the LLMs you choose to use (if any).

Use Cases

Continue excels in several high-impact development scenarios:

1. Privacy-Sensitive Development Teams

Organizations in finance, healthcare, or government often cannot send code to third-party servers. By connecting Continue to local LLMs via Ollama or llama.cpp, teams get AI-powered coding help while keeping all intellectual property on-premises.

🛡️ Example: A bank uses Continue with Llama 3 running on internal GPU clusters to assist developers without exposing source code.

2. Automated Pull Request Reviews in CI

Engineering leads can define custom CI agents that automatically analyze incoming pull requests for:

• Missing tests
• Security vulnerabilities
• Code style violations
• Performance anti-patterns

These agents comment directly on diffs in GitHub/GitLab, reducing manual review load.

🤖 Example: Every PR triggers a Test Coverage Checker agent that replies: “This change reduces test coverage by 7%. Please add tests for edge cases.”

3. Onboarding New Developers

New hires can use Continue’s chat interface to explore complex codebases:

• “What does this module do?”
• “Show me how authentication flows through the app.”
• “Give me a checklist to implement a new API endpoint.”

With pre-configured prompts and agents, Continue becomes a personalized mentor.

👩‍💻 Example: A startup embeds a Onboard Me agent that walks new engineers through setting up their dev environment and explains microservice interactions.

Pros & Cons

✅ Pros

Advantage	Explanation
Truly Open Source	MIT licensed, transparent codebase, community-driven improvements
Model Agnostic	Supports nearly every major LLM — local or cloud — giving ultimate flexibility
IDE-Native Experience	Feels like a built-in feature, not a clunky external tool
Highly Customizable	Define agents, modify prompts, extend with plugins
Strong CI/CD Focus	Unique among AI coders in supporting automated pipeline actions
No Telemetry	Respects user privacy; no data collection by default

❌ Cons

Limitation	Workaround / Note
Steeper Learning Curve	Requires basic understanding of LLMs and config files compared to plug-and-play tools like GitHub Copilot
Local Models Need Hardware	Running Llama 3 or Mixtral locally requires decent RAM/GPU (at least 16GB RAM for 7B models)
Less Polished UI Than Cursor	Interface is functional but not as sleek as commercial competitors
No Built-in Model Training	Does not train fine-tuned models; focuses on inference and orchestration
Community Support Only (Free Tier)	Enterprise users may need paid support for urgent issues

Alternatives

While Continue offers unmatched flexibility, here are some comparable tools depending on your priorities:

Tool	Best For	Key Differences vs Continue
Cursor	Rapid prototyping, AI-first IDE experience	Proprietary, closed-source, limited model choices, better UX but less control
GitHub Copilot	Easy setup, deep VS Code integration	Simpler to use, but tied to Microsoft/OpenAI stack, no local model support
Tabby (formerly Tabnine)	On-premise AI coding at enterprise scale	Strong local model support, but fewer automation features than Continue
Sourcegraph Cody	Large codebase navigation, semantic search	Better for code discovery, weaker in CI automation and agent workflows

🧭 When to pick Continue?
Choose Continue if you value control, customization, and automation — especially in team or CI environments. If you just want fast autocomplete and don’t mind cloud dependence, Copilot or Cursor might suffice.

Disclaimer

This guide is based on publicly available documentation and third-party reviews of Continue as of May 2026. While every effort has been made to ensure accuracy, configurations, features, and pricing may change over time. Always refer to the official website (https://continue.dev) and GitHub repository for the most up-to-date information.

Continue is an open-source project maintained by a growing community. Contributions, bug reports, and feedback are welcome on GitHub. The authors of this guide are not affiliated with the Continue development team but aim to provide practical, hands-on insights for developers adopting AI-assisted programming responsibly.