Modern IDEs already know a lot about your codebase, including its structure, symbols, inspections, tests, run configs, and VCS history. The JetBrains Junie AI assistant is JetBrains’ attempt to put an “agent brain” on top of all that, so you can delegate real work instead of just generating snippets.
This matters because most dev time is not spent typing new lines. It’s spent confirming assumptions, chasing errors across files, and doing small but annoying chores that still require care.
What is Junie?
Junie is anAI coding agent that runs in JetBrains IDEs and can handle multi-step tasks from idea to working change, while keeping you in the loop with plans, diffs, and checks. JetBrains positions it as complementary to the JetBrains AI Assistant, which helps in-the-moment (chat, completion, explanations), while Junie is more autonomous and is designed to complete tasks end-to-end.
At a practical level, Junie can:
- Understand project context (not just the open file).
- Search and reason across the codebase.
- Apply edits using IDE inspections and checks.
- Run code and tests, then report results for review.
Availability: JetBrains provides support for Junie across multiple IDEs (e.g., IntelliJ IDEA Ultimate, PyCharm Pro, WebStorm, GoLand, PhpStorm, and others).
Key features to use
Plan-first execution (the underrated part)
One of Junie’s most practical behaviors is that it tends to propose a plan before editing, then executes it step by step. In a hands-on review in which Junie builds a user-management microservice, the author highlights this planning stage as a standout difference from pure “autocomplete-style” tools.
When you’re working in real repos, this helps because you can double-check the approach early:
- What modules will change?
- What new dependencies get added?
- What endpoints, DTOs, or DB migrations are implied?
- Are tests part of the plan, or an afterthought?
Multi-file edits with IDE-aware safety rails
Junie is built to operate with an IDE context: inspections, refactors, navigation, and project structure. JetBrains explicitly calls out that Junie uses inspections/checks and verifies the project state and tests after changes.
That’s a big deal for maintenance work, such as:
- Refactoring a service layer and updating all call sites.
- Replacing deprecated API usage across the project.
- Introducing a feature flag and wiring it end-to-end.
Approval modes for running commands
Using agents can be risky when they can execute things. JetBrains’ own playbook shows that (unless you choose a more autonomous mode): Junie will ask before running commands and provide a clear “Run command” action in the UI.
If you’re using Junie on a team repo, this is where you should be strict:
- Treat terminal access like code review.
- Avoid letting an agent “just run whatever.”
- Keep secrets out of prompts, logs, and generated docs.
Custom guidelines for consistent output
JetBrains also points to a developer-controlled approach where you can steer Junie’s behavior via project guidelines (for example, a.junie/guidelines.md file), so it follows your conventions over time.
This is especially useful for:
- Naming rules.
- Testing style.
- Error-handling patterns.
- Logging conventions.
- Architectural boundaries (which define what must not be touched).
Credits and quotas (so you don’t get surprised mid-task)
Junie runs through JetBrains’ AI service and uses a credit quota for cloud-model features. JetBrains documents tiers like AI Free, AI Pro, AI Ultimate, and explains how credits renew monthly (and how top-ups work).
A quick, simple takeaway:
- If you want to try it, Free/Pro can be enough.
- If you want to use Junie regularly for agentic workflows, higher quota tiers tend to be smoother.
What makes Junie unique?
1. It behaves like a task-level agent
Junie is designed to take a goal, explore the project, and propose a short execution plan before it changes code. That “plan first, then act” flow is a big shift from simple prompt-to-snippet tools.
2. It’s IDE-native, not a bolt-on chat box
Because it lives inside JetBrains IDEs, Junie can use project structure plus IDE inspections and checks while it edits. This helps a lot with multi-file work where correctness matters more than speed.
3. It’s built for review and collaboration
JetBrains positions Junie as something you can delegate routine work to, or run alongside you on complex changes. The intent is autonomous when safe, collaborative when needed. This is where the JetBrains Junie AI agent framing fits best; it’s meant to act, but not hide its work.
4. Teams can teach your conventions
You can provide project guidelines (such as naming rules, testing style, and anti-patterns) so that Junie’s output aligns with how your repo should look. JetBrains also maintains a public guidelines catalog you can borrow from.
5. It supports async workflows through GitHub
Junie is not limited to the IDE. JetBrains highlights Junie on GitHub for async development (multiple tasks, quick fixes), and there’s also a GitHub Action to integrate Junie into workflows around issues and PRs.
Final thoughts
Junie is JetBrains’ move from AI that can carry a task across the finish line. It pairs agent-style planning and multi-file edits with IDE-native checks, so the output is usually a reviewable diff instead of a pile of snippets. If your team already uses JetBrains IDEs, Junie can reduce busywork and keep changes moving without losing control.
