Continue
Continue is an open-source AI coding assistant extension for VS Code and JetBrains IDEs that lets developers use customizable LLM-powered chat, autocomplete, code editing, and agent workflows with local or hosted models.
Links
Website: github.comOverview
Continue is an open-source IDE extension designed to bring AI-assisted software development directly into popular development environments such as Visual Studio Code and JetBrains IDEs. It provides features like contextual chat with your codebase, inline code editing, tab-based autocomplete, natural-language code generation, debugging help, and agent-style workflows that can interact with project files and development tools.
💡 What is this?
Continue is like having an AI programming helper built into your code editor. Instead of copying code into a chatbot website, you can ask questions about your project, request changes to files, generate new functions, explain errors, or get code completions while you work.
⚙️ How it works
Continue operates as an IDE extension with a configurable AI assistant layer that can connect to a wide range of large language model providers, including hosted APIs and local model runtimes. Developers can configure models for chat, autocomplete, embeddings, reranking, and editing workflows, often through project or user-level configuration files. This makes it possible to use models from providers such as OpenAI, Anthropic, Google, Mistral, Ollama, LM Studio, OpenRouter, and other OpenAI-compatible APIs, depending on current support and configuration.
🎯 Why it matters
Continue matters because it gives developers more control over AI-assisted coding than many closed, single-provider tools. It supports bring-your-own-model workflows, local model usage, team customization, and open-source extensibility, making it attractive for developers and organizations that want AI coding help without fully committing to a proprietary assistant stack.
🛠️ Practical use cases
- •Ask questions about an unfamiliar codebase directly from the IDE
- •Generate, refactor, or edit code using natural-language instructions
- •Use local or private LLMs for AI coding assistance in sensitive repositories
- •Create custom coding workflows and prompts tailored to a team or project
- •Get autocomplete suggestions while writing code
- •Debug errors by asking the assistant to inspect stack traces and relevant files
✅ When to use
Use Continue when you want an open-source, configurable AI coding assistant inside VS Code or JetBrains IDEs, especially if you need flexibility around model providers, local LLM support, custom prompts, or team-specific development workflows.
❌ When not to use
Do not use Continue if you need a fully managed, zero-configuration enterprise AI coding product, if your organization prohibits any AI integration in development environments, or if you prefer a single-vendor assistant with tightly integrated proprietary features and centralized administration.
👍 Advantages
- +Open-source project with transparent development and community contributions
- +Supports multiple model providers rather than locking users into one vendor
- +Can be configured to use local models for privacy-sensitive workflows
- +Works directly inside common IDEs such as VS Code and JetBrains products
- +Provides multiple AI coding modes including chat, autocomplete, inline edits, and agent-style workflows
- +Highly customizable for individual developers and teams
👎 Disadvantages
- −May require more setup and configuration than closed, turnkey AI coding tools
- −Quality depends heavily on the chosen model, prompts, embeddings, and context configuration
- −Local model workflows can require significant hardware resources
- −Some advanced workflows may be less polished than mature proprietary assistants
- −Teams may need to manage model credentials, provider costs, and security policies themselves
⚠️ Limitations
- •AI-generated code can be incorrect, insecure, or inconsistent with project conventions
- •Performance and accuracy vary by model provider and repository size
- •Context window limits can prevent the assistant from fully understanding very large codebases
- •Local models may lag behind top hosted models in reasoning and coding quality
- •IDE extension behavior and features may differ between VS Code and JetBrains environments
🔄 Alternatives to consider
📚 Related concepts to learn
🧪 Suggested experiments
- →Install Continue in VS Code and connect it to a hosted model for chat and code editing
- →Configure Continue with a local model through Ollama or another local runtime and compare results with a hosted model
- →Ask Continue to explain the architecture of an unfamiliar repository
- →Use inline editing to refactor a function and review the generated diff carefully
- →Create a custom prompt or workflow for your team's coding standards
- →Benchmark autocomplete quality across two different models on the same project
🗺️ Ecosystem Map: Ai Coding Ides Clis
AI coding IDEs and CLIs have evolved from simple autocomplete to full agentic development environments. The space is dominated by IDE integrations and standalone AI-native editors that reimagine the coding interface.
Key Concepts
Major Tools
Emerging Tools
Metadata
continueThis data is loaded from the database. Ecosystem context may use the section-level generated map.