Overview

Aider vs Continue: Terminal-First vs IDE-First AI Coding Assistants

Compare Aider and Continue on AI coding assistant positioning, terminal/IDE integration, codebase comprehension, multi-file editing, and enterprise features.

Projects Compared

Aider

Python · Apache-2.0

47.5k ★

AI pair programming in your terminal. Collaborate with LLMs to edit code, manage Git, and refactor across multiple files with deep developer workflow integration.

pair-programmingcoding-agentterminalgitpython
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Continue

TypeScript · Apache-2.0

35.0k ★

Continue is an open-source AI code assistant extension for VS Code and JetBrains IDE. It can autocomplete code, refactor, and explain code, helping developers improve programming efficiency.

coding-assistantide-extensionllmopen-source
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Feature Comparison

Best for AiderContinue
Core positioning Terminal-native, Git-aware AI pair programming assistant focused on CLI workflows and commits AI coding assistant delivered as a VS Code / JetBrains extension, deeply integrated into the IDE
Best for Terminal and Vim/Emacs users, remote SSH environments, automated Git commit generation VS Code / JetBrains users wanting in-editor inline completion, chat, refactor and Agent mode in one
Learning curve Low, single command install (pip install aider-chat), works via natural-language prompts Medium, requires IDE extension install, model provider config, shortcuts and context commands
Ecosystem maturity Led by Paul Gauthier, focused community, strong benchmarks, fast model adoption Maintained by Continue Dev (commercial), large VS Code Marketplace install base, strong enterprise presence
Integration and deployment Python CLI, works with any OpenAI-compatible model, supports local Ollama and Claude/GPT IDE extension + local/cloud models, supports self-hosted LLMs, Anthropic, OpenAI and Bedrock

GitHub Stats

Metric AiderContinue
Stars 47.5k35.0k
Forks 4.7k5.1k
Language PythonTypeScript
License Apache-2.0Apache-2.0
Last commit May 22, 2026July 20, 2026

Which one should you choose?

Choose based on your primary workflow, language ecosystem, and integration needs. Review each project's documentation and recent GitHub activity before adopting it in production.