Master JetBrains AI with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Choose and Install JetBrains IDE: Download and install your preferred JetBrains IDE (IntelliJ IDEA, PyCharm, WebStorm, etc.) with a valid license. Ensure you have version
1+ for AI Ultimate support or
1+ for AI Free tier access. Select AI Subscription Tier: Visit jetbrains.com/ai
ides/buy/ to choose between AI Free (limited), AI Pro ($10/month), AI Ultimate ($30/month), or AI Enterprise ($60/month per user). Consider your usage patterns and team size when selecting. Activate JetBrains AI License: In your IDE, navigate to Settings → JetBrains AI and sign in with your JetBrains account. Activate your chosen AI license tier and verify your monthly credit allocation in the AI widget in the toolbar. Configure AI Features and Models: Access the AI Chat tool window, explore available models, and configure preferences for code completion, refactoring assistance, and debugging insights. Set up MCP integrations if needed for external tool connections.
💡 Quick Start: Follow these 4 steps in order to get up and running with JetBrains AI quickly.
Explore the key features that make JetBrains AI powerful for coding agents workflows.
Native integration with JetBrains IDEs leveraging existing code analysis capabilities for highly contextual AI assistance that understands project architecture, dependency graphs, and code relationships at the enterprise level.
Working on a large Spring Boot microservices application, the AI analyzes your service architecture, understands bean dependencies, and suggests refactoring that considers cross-service impacts, ensuring changes don't break API contracts or dependency injection configurations.
AI that understands your specific project structure, coding conventions, architectural patterns, and team standards to generate consistent, high-quality code that seamlessly integrates with existing codebases without manual adjustment.
When adding a new REST controller to an existing enterprise application, the AI automatically follows your established patterns for exception handling, validation, logging, and response formatting, generating code that passes code reviews without modification.
AI assistance during debugging sessions that understands execution context, variable states, call stacks, and potential root causes of issues, providing targeted suggestions based on runtime analysis and historical patterns.
During a complex debugging session with a NullPointerException deep in a multi-threaded application, the AI analyzes the call stack, variable states, and execution flow to suggest specific areas to investigate and potential fixes based on common patterns in similar scenarios.
Specialized AI assistance for different programming languages and frameworks with deep understanding of language-specific best practices, patterns, and cross-language interactions in polyglot projects.
Working on a full-stack application with Java backend and React frontend, the AI provides Java-specific suggestions for your Spring controllers while understanding how API changes will affect your TypeScript frontend components, ensuring consistency across the entire stack.
AI-powered refactoring suggestions that analyze the entire codebase impact, considering dependencies, inheritance hierarchies, and architectural constraints to ensure safe, comprehensive code improvements.
When refactoring a core utility class used across 200+ files in a large enterprise application, the AI analyzes all usage patterns, suggests the safest refactoring approach, and identifies potential breaking changes before you make them.
Full Model Context Protocol support enabling integration with external tools, databases, APIs, and custom workflows while maintaining enterprise security and privacy standards.
Connect JetBrains AI to your internal documentation system, Jira instance, and deployment pipeline, allowing the AI to understand ticket requirements, suggest code changes based on internal standards, and validate changes against deployment constraints.
JetBrains AI is supported across the full commercial IDE lineup, including IntelliJ IDEA, PyCharm, WebStorm, GoLand, Rider, PhpStorm, RubyMine, CLion, RustRover, and DataGrip, as well as the Android Studio plugin variant. Both AI Assistant and the Junie agent are available in these environments, though some features roll out to specific IDEs first before becoming generally available.
Yes. JetBrains AI is model-agnostic and lets you route requests to Anthropic Claude, OpenAI GPT, and Google Gemini models, as well as JetBrains-hosted local models for offline use. You can switch models per chat or task, which is useful for balancing latency, cost, and answer quality for different kinds of work.
JetBrains states that customer code is not used to train third-party models, and enterprise customers can opt out of all data collection. Local models keep code entirely on-device, and cloud-routed requests can be configured with data-residency preferences. Admins also have access to audit logs and centralized policy controls.
AI Assistant is the inline and chat-based helper that handles completion, code generation, refactoring suggestions, and conversational Q&A. Junie is JetBrains' agentic coding companion that takes a higher-level task description, plans a sequence of edits, executes them across multiple files, runs tests, and iterates autonomously while you supervise from the IDE.
Yes, JetBrains AI ships with comprehensive MCP support, allowing teams to connect custom MCP servers for internal documentation, issue trackers, databases, deployment systems, and proprietary tooling. This lets the assistant reason about organization-specific context that is not present in the public training data of the underlying models.
Now that you know how to use JetBrains AI, it's time to put this knowledge into practice.
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Tutorial updated March 2026