How to Use Magic.dev for Long-Context Coding

Magic.dev is an AI coding assistant built around extreme context length. Its LTM-2 model family is designed to hold up to 100 million tokens at once, so the agent keeps an entire repository in view instead of guessing from three open files.

That number is not marketing padding. It changes what you can ask: questions about architecture, not just about the function on your screen.

What Is Magic.dev and What Makes It Different?

Magic.dev is a coding agent whose models are trained for repository-scale input. Where most assistants cap at 128K to a few million tokens, this one aims at 100M, which is roughly a mid-sized monorepo including its tests and config.

The companion IDE extension covers VS Code and JetBrains, so you get both autocomplete-style editing and an autonomous agent in the same product.

How Do You Get Started?

• Sign up at the Magic.dev site and request workspace access
• Connect a Git repository so the agent can index it
• Install the VS Code or JetBrains extension
• Let the initial repository index finish before asking anything
• Start with a question that spans modules, not a single file

The indexing step is not optional. The persistent map of your code is what lets repeated questions skip re-reading everything from scratch.

What Kinds of Tasks Does It Handle Well?

Architecture-level questions. “Where is authentication enforced across these four services?” or “Which modules would break if I change this interface?” Multi-file edits and build-fix loops also work, since the agent can run the build and iterate on errors.

Is Magic.dev Free?

No. There is no free tier, and pricing is arranged with the company rather than posted publicly at the time of writing. It is positioned for teams, not solo hobbyists. If you want a free agent, a terminal-based tool is a better starting point.

Who Is It Not For?

Anyone who mostly works in one file at a time. You pay for context you never use. It is also a poor fit if you cannot index your repository for privacy or licensing reasons — the whole value proposition depends on the agent reading it.

What Should You Watch Out For?

Long-context models can be confidently wrong about details they skimmed. Verify claims about specific line numbers. And large indexes take time to build the first run, so do not judge responsiveness in the first ten minutes.

Quick Summary

• Magic.dev targets 100M-token context via its LTM-2 models.
• Repository indexing is required before the agent is useful.
• Strongest at cross-module architecture questions.
• No free tier; pricing is negotiated.
• Skip it if you work file-by-file or cannot index your code.