AI code editors
The term covers three quite different things, and most of the confusion about which product to use comes from not separating them.
Statements about other products were checked on 23 September 2026 and are cited at the foot of this page.
Three shapes, one name
"AI code editor" is used for all of these, and a product can be more than one of them:
- Completion. It suggests the next few lines as you type. You stay in control of every keystroke and accept or reject inline. This is the original form and still the one most people use daily.
- Chat in the editor. You ask a question and it answers with your open files as context. Useful for understanding code you did not write; it does not change anything unless you paste.
- An agent. You describe an outcome. It plans a change across several files, edits them, runs commands, and reports back. This is where the products genuinely diverge.
Completion and chat are close to commodities now. If you are choosing between products, the agent is the part worth evaluating, because that is where the differences are real and where the failure modes live.
What actually differs between agents
These are the axes that matter in practice, in rough order of how often they turn out to be the thing you care about:
| Question | Why it decides the outcome |
|---|---|
| How much of the repository can it see? | An agent that reads only your open files will confidently break a caller it never looked at. |
| Is it allowed to run commands? | Running your tests is the difference between a change that compiles and a change that works. It is also the thing that makes an agent genuinely risky, so it should be visible. |
| Does it check its own work before it stops? | Most of the observed failures are not bad edits. They are an agent reporting done without having verified anything. |
| What do you review, and when? | A diff you approve before it lands is a different product from edits that have already been written to disk. |
| Who chooses the model? | A list of models leaves you to work out which one can finish the job, and to find out mid-task when it cannot. An editor that chooses for you, and changes model when a check fails, takes that question off your hands. |
What to check before you commit
- Give it a change that touches at least three files, in a repository you know well. One-file demos tell you nothing.
- Watch whether it runs anything, or only writes.
- Break something it just wrote, and see whether it notices.
- Check what happens when it is wrong: can you see the plan before it acts, and reject it?
Where AstraCode fits
AstraCode is an AI-native code editor, built around the agent rather than around the text box. Its agents plan a change across the repository, write it, run your tests, check their own work, and hand you a diff to approve. AstraOne chooses the model for every task and brings in a stronger one when a check fails, so there is no model list to research and no wrong choice to make. Larger work splits across subagents and parallel agents, and rules, skills, hooks and MCP servers shape the agent to your team. Free to start, no card.
If you want the direct comparisons rather than the category, those are on the comparison pages.
Where it runs
- macOS and Linux: the AstraCode editor, from the download page.
- Windows: AstraCode as an extension in the editor you already use, with the same account and the same agent.
- Your terminal and your CI: the AstraCode CLI, for a conversation in the terminal, a task from a pipeline, or a review of a branch.
- JetBrains IDEs and Zed: the same agent inside them, through the CLI.
Sources
- JetBrains AI Assistant — JetBrains' own description of the assistant built into their IDEs. Checked 23 September 2026.
- GitHub Copilot documentation — Copilot as an extension to an existing editor rather than an editor of its own. Checked 23 September 2026.