The leading open-source AI code assistant that integrates any LLM into VS Code and JetBrains.
Continue is a modular, open-source AI code assistant designed to eliminate vendor lock-in by providing a bridge between any Large Language Model (LLM) and the developer's IDE. Technically, Continue functions as an orchestration layer that manages context through a sophisticated RAG (Retrieval-Augmented Generation) pipeline, indexing local codebases into a SQLite-based vector store. It allows developers to seamlessly switch between cloud providers like OpenAI and Anthropic, or local inference engines like Ollama and LM Studio. By 2026, Continue has solidified its position as the enterprise standard for 'Bring Your Own Model' (BYOM) architectures, offering a 'Control Plane' for teams to manage prompts, context policies, and model routing across entire organizations. Its architecture is uniquely extensible through .continuerc configuration files, allowing teams to define custom 'Slash Commands' and 'Context Providers' that pull data from Jira, GitHub Issues, or internal documentation, making it a highly customizable operating system for AI-assisted software development.
Continue is a modular, open-source AI code assistant designed to eliminate vendor lock-in by providing a bridge between any Large Language Model (LLM) and the developer's IDE.
Explore all tools that specialize in autocomplete code. This domain focus ensures Continue delivers optimized results for this specific requirement.
Explore all tools that specialize in generate unit tests. This domain focus ensures Continue delivers optimized results for this specific requirement.
Explore all tools that specialize in refactor code. This domain focus ensures Continue delivers optimized results for this specific requirement.
Explore all tools that specialize in generate code documentation. This domain focus ensures Continue delivers optimized results for this specific requirement.
Explore all tools that specialize in llm integration. This domain focus ensures Continue delivers optimized results for this specific requirement.
Uses LanceDB for local vector storage to index code snippets, enabling RAG across thousands of files.
A plugin system to pull external data (Jira, Slack, SQL schemas) directly into the LLM prompt context.
A dedicated engine for multi-line code completions that works with any fine-tuned 1B-7B parameter model.
User-defined shortcuts that trigger specific prompts or scripts (e.g., /edit, /test, /share).
Configuration is handled via JSON, allowing for version-controlled IDE settings.
Native support for Ollama, allowing for 100% offline code assistance.
Automatically route chat queries to large models (Claude 3.5) and autocomplete to fast models (DeepSeek).
Install the Continue extension from the VS Code Marketplace or JetBrains Plugin Marketplace.
Open the Continue sidebar and select 'Add Model' to configure your primary LLM.
Choose between 'Cloud' (API Key based) or 'Local' (Ollama/LM Studio) inference providers.
Allow Continue to perform initial codebase indexing for RAG-based context awareness.
Configure the config.json or .continuerc file for custom model parameters.
Set up 'Tab Autocomplete' by selecting a lightweight model like StarCoder2 or DeepSeek-Coder.
Integrate custom context providers (e.g., GitHub, Docs) in the configuration settings.
Test the connection by highlighting code and using 'Cmd+L' (Mac) or 'Ctrl+L' (Windows) to add it to chat.
Create a custom Slash Command in the config file for repetitive tasks like 'fix-imports'.
Sync configuration across the team using a shared .continuerc file in the repository root.
All Set
Ready to go
Verified feedback from other users.
"Users praise its flexibility and the ability to use local models, though some find the JSON configuration complex initially."
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