Continue
Best for: Privacy-conscious developers, Power users desiring multi-model flexibility, Enterprise teams requiring air-gapped environments, Developers leveraging local inference, Complex codebase navigation and refactoring
Capabilities
10/13 supportedWeb Frontend
Build React, Vue, or other frontend applications
Web Backend
Create APIs, server-side logic, and backend services
Mobile Apps
Build native or cross-platform mobile applications
SSR / SEO
Server-side rendering for better SEO performance
Database
Integrate and manage database connections
Deployment
Deploy and host applications automatically
Agentic Mode
Autonomous multi-step task execution
Chat Interface
Interactive conversational AI assistant
Code Generation
Generate code from natural language prompts
Debugging
Identify and fix bugs automatically
Terminal Access
Execute commands in the terminal
Web Browsing
Browse the web for information
Test Generation
Generate unit and integration tests
Technical Analysis
Continue differentiates itself by operating as a high-performance extension layer rather than a standalone IDE fork. It leverages a sophisticated RAG (Retrieval-Augmented Generation) engine to provide codebase-wide context, allowing the LLM to understand project-specific patterns and dependencies. Because it is model-agnostic, developers can swap between local providers like Ollama or LM Studio and cloud providers like Anthropic and OpenAI with simple JSON configuration changes.
The tool's agentic capabilities are particularly impressive, enabling it to execute terminal commands, read files, and perform multi-step refactoring tasks through a structured sidebar interface. While it faces some UI constraints imposed by the VS Code Extension API, it circumvents these through clever use of custom editors and terminal integration. For enterprise teams, the ability to run the entire stack within an air-gapped environment using local models is its most significant technical advantage.
Limitations & Considerations
Known Limitations
- Extension architecture is limited by VS Code's API compared to standalone AI IDEs
- Higher configuration overhead for non-standard setups
- Requires manual setup for optimal RAG performance compared to cloud-indexed tools
Frequently Asked Questions
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