JetBrains AI Assistant

AI features built into JetBrains IDEs, with an option to run models locally

AI Coding & DevelopmentOverseas★★★★☆ 4.0

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What is JetBrains AI Assistant?

JetBrains AI Assistant brings completion, chat, commit message generation, test drafting and documentation help into IntelliJ, PyCharm, WebStorm, GoLand and the rest of the JetBrains range. Because it sits inside the IDE, it can use the project's structure, refactoring engine and inspections in a way a generic plugin cannot match, and answers can cite documentation directly. A notable option is routing requests to a locally hosted model through LM Studio or Ollama, which matters to teams whose code cannot leave the network. It is sold as an add-on subscription rather than bundled with the IDE.

Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.

Key features

In-IDE chat, code completion and inline generation across the range
Refactoring-aware suggestions that use the IDE's own code model
Commit message, documentation and unit test generation helpers
Local model support through LM Studio or Ollama endpoints
Documentation lookup that cites the sources behind an answer
One licence covering the full set of JetBrains IDEs

Pros & cons

Strengths

Deep IDE integration that a generic plugin cannot replicate
Optional local models help where data policies are strict
A single subscription covers every JetBrains IDE you use

Watch out for

An extra cost on top of an already paid IDE subscription
Less capable than the leading standalone coding agents
Some features arrive later than the newest IDE releases

Best for & use cases

jetbrains users, java and kotlin teams, enterprise setups and local model deployments

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FAQ

Is it included with my IDE subscription?

No. It is a separate add-on with its own subscription, though one licence covers the JetBrains IDEs you use. A trial period is normally available for evaluation.

Can it run entirely locally?

Partly. You can point it at a locally hosted model through tools such as LM Studio or Ollama, which keeps prompts on your own machine. Some cloud-backed features will still need connectivity.

How does it compare with a dedicated agent?

It is strongest at in-editor assistance that uses the IDE's understanding of your code. For long autonomous multi-file tasks, dedicated agent tools currently go further.