Best AI Coding Tools for IntelliJ and JetBrains IDEs in 2026

Updated on July 22, 2026 by Steven Jones

GitHub Copilot is the best AI coding tool for most IntelliJ and JetBrains users in 2026. It combines the highest overall score in DIY AI’s JetBrains-compatible shortlist with mature inline completion, chat, agent features and the easiest adoption path for GitHub-centred development teams.

It is not the automatic winner for every workflow. Amazon Q Developer is more useful for AWS-heavy projects; Windsurf Plugin is a strong choice for developers who want Cascade-style multi-file work in JetBrains; and JetBrains AI Assistant provides the most native interface while also serving as a host for external coding agents.

This comparison covers IntelliJ IDEA, PyCharm, WebStorm, PhpStorm, GoLand, Rider, CLion, RubyMine and other supported JetBrains IDEs. It evaluates code quality, repository context, refactoring, test generation, debugging, agent permissions, integration conflicts and the amount of human review each workflow requires.

Windsurf, Codeium and Devin Desktop are not three competing JetBrains plugins

The original version of this comparison treated Windsurf and Codeium as separate current JetBrains products. That is no longer accurate.

Codeium was the former name of the coding assistant and plugin. That JetBrains extension is now called Windsurf Plugin. The separate Windsurf editor was renamed Devin Desktop on 2 June 2026. Devin Desktop is a standalone coding environment, not the JetBrains plugin.

Windsurf’s official announcement of the name change confirms that the standalone editor is now called Devin Desktop, while Windsurf for JetBrains remains available.

Current name	Former name or relationship	Product type	Relevant to this ranking?
Windsurf Plugin	Formerly Codeium	Plugin for JetBrains and other editors	Yes
Devin Desktop	Formerly the standalone Windsurf editor	Standalone AI coding environment	No, because it replaces or sits alongside JetBrains
Devin	Separate from both products	Autonomous cloud coding agent	No, because it is not a conventional JetBrains plugin
JetBrains AI Assistant	First-party JetBrains assistant and agent host	Native IDE integration	Yes

The practical rule: compare Windsurf Plugin with Copilot, Amazon Q and JetBrains AI Assistant. Compare Devin Desktop with standalone coding environments such as Cursor, not with JetBrains plugins.

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Quick verdict: the best JetBrains AI coding tools

Rank	Tool	DIY AI score	Best for	Main limitation
1	GitHub Copilot	9.0/10	Most IntelliJ users and GitHub-centred teams	Deep repository work is not its strongest dataset category
2	Amazon Q Developer	8.6/10	AWS development, Java upgrades and cloud-aware assistance	Its specialist advantage shrinks outside AWS-heavy projects
3	Windsurf Plugin, formerly Codeium	8.4/10	Cascade-style agent work without leaving JetBrains	The plugin should not be confused with the higher-scoring Devin Desktop product
4	JetBrains AI Assistant	8.2/10	Native JetBrains workflows and access to external agents	The base score does not measure every agent it can host

The scores come from the DIY AI code-generation dataset. The dataset measures code accuracy, language support, debugging assistance, integration ease, learning adaptability, repository context, refactoring strength, test generation and documentation generation.

The dataset was originally created before the latest product renames. The former Codeium row, scoring 8.4/10, maps to Windsurf Plugin. The former Windsurf editor row, scoring 8.8/10, maps to Devin Desktop. Applying the 8.8 score to the JetBrains plugin would compare the wrong product.

Best AI plugin for each JetBrains IDE

GitHub Copilot is the best starting point across most JetBrains IDEs, but the alternative changes with the workload rather than the IDE name alone. AWS development, agent-led multi-file work and preference for a native JetBrains interface can justify choosing a different tool.

JetBrains IDE	Best starting point	Consider instead when
IntelliJ IDEA	GitHub Copilot	Choose Amazon Q Developer for AWS-heavy Java projects and transformation work.
PyCharm	GitHub Copilot	Choose Windsurf Plugin when agent-led changes across several Python files are the priority.
WebStorm	GitHub Copilot	Choose Windsurf Plugin for more active multi-file JavaScript and TypeScript workflows.
PhpStorm	GitHub Copilot	Choose JetBrains AI Assistant when a native interface matters more than the strongest overall score.
GoLand	GitHub Copilot	Choose JetBrains AI Assistant for native project navigation and code explanations.
Rider	GitHub Copilot	Choose JetBrains AI Assistant when tight integration with the existing Rider workflow is the priority.
CLion	GitHub Copilot	Compare tools using your real compiler, build system and tests before permitting broad agent changes.
RubyMine	GitHub Copilot	Choose Windsurf Plugin when bounded Rails changes need coordinated edits across several files.

Why JetBrains AI comparisons now involve more than plugins

A JetBrains AI setup can now contain several separate layers. There may be an inline completion provider, a chat interface, a local coding agent, a command-line agent and an external cloud agent. Two developers can both say they use Copilot in IntelliJ while working through different interfaces with different permissions.

GitHub Copilot can be used via its dedicated JetBrains plugin, via supported agent integrations in JetBrains AI Assistant, or from the integrated terminal. JetBrains AI Assistant can also connect to compatible agents through Agent Client Protocol. Amazon Q and Windsurf Plugin provide their own agent-led workflows inside the IDE.

Layer	Typical job	What to evaluate
Inline completion	Suggesting the next line, block or method	Latency, acceptance rate and how often suggestions interrupt normal typing
Chat assistant	Explaining code, drafting tests and answering questions	Context selection, factual accuracy and usefulness of follow-up answers
Coding agent	Editing files, running commands and completing multi-step tasks	Permissions, diff size, command approval and recovery from failed tests
Agent host	Providing one interface for several external agents	Which agent is active, account requirements and whether behaviour changes between agents
Standalone AI IDE	Controlling the complete editor and agent experience	Migration cost and what established JetBrains features would be lost

This is why a feature checklist can be misleading. Several products may advertise chat, agents, and repository context, but the depth of access and the amount of control given to the developer can vary widely.

GitHub Copilot for IntelliJ: best overall JetBrains AI assistant

DIY AI score: 9.0/10

GitHub Copilot is the best first installation for most JetBrains users. It scores 9.6/10 for Integration Ease, the highest result among the tools in this comparison, while maintaining strong scores for code accuracy, language support, repository context and test generation.

Copilot works well for inline suggestions, explanations of unfamiliar code, bounded refactors, unit-test drafts, documentation, and agent-led implementation tasks. Developers can adopt those features gradually. A team can start with completion and chat before allowing broader agent actions.

The main limitation is easy to miss because Copilot feels polished. Familiarity can lead developers to trust its output more quickly than they would an unfamiliar agent. Repository Context scores 8.9/10 and Refactoring Strength scores 8.8/10, which are strong results, but neither leads the complete dataset.

Copilot performs best when the task has an explicit boundary. Name the module, describe the required behaviour, identify relevant tests and state which files should not change. A vague instruction such as “clean up this service” gives the agent too much freedom to decide what clean means.

GitHub Copilot dataset scores

Code Accuracy	9.1/10	Language Support	9.2/10
Debugging Assistance	8.9/10	Integration Ease	9.6/10
Repository Context	8.9/10	Refactoring Strength	8.8/10
Test Generation	8.8/10	Documentation Generation	8.9/10

Choose GitHub Copilot if

• Your repositories, pull requests, and developer identities already reside on GitHub.
• You want strong AI assistance without asking developers to replace IntelliJ or another JetBrains IDE.
• The team needs one assistant that can cover completion, chat, tests and controlled agent work.
• Rollout, policy management and developer familiarity matter as much as maximum agent autonomy.

Amazon Q Developer: best for AWS-heavy JetBrains development

DIY AI score: 8.6/10

Amazon Q Developer ranks second because it combines a solid 8.6/10 overall score with features directly available in JetBrains IDEs. These include inline suggestions, chat, workspace context, agentic coding, MCP connections, security scanning and code transformation.

Its strongest practical case is not ordinary autocomplete. Amazon Q becomes more useful when the repository contains Java services, AWS SDK integrations, Lambda functions, CloudFormation templates, IAM policies or migration work. It can keep Cloud terminology and application code within the same working context.

Amazon Q scores 8.8/10 for Debugging Assistance and 8.7/10 for Test Generation. Those scores make it a stronger all-round JetBrains recommendation than the renamed Windsurf Plugin row. It also avoids the branding ambiguity surrounding Codeium, Windsurf and Devin Desktop.

The hidden limitation is over-specialisation. A company may run its infrastructure on AWS while most developers rarely touch AWS-specific code. In that situation, the Cloud advantage does little to compensate for Copilot’s stronger integration and language scores.

Amazon Q Developer dataset scores

Code Accuracy	8.7/10	Language Support	8.6/10
Debugging Assistance	8.8/10	Integration Ease	8.7/10
Repository Context	8.5/10	Refactoring Strength	8.5/10
Test Generation	8.7/10	Documentation Generation	8.6/10

Choose Amazon Q Developer if

• AWS services appear in developers’ daily work, not only in the hosting account.
• You need assistance with Java upgrades, cloud configuration or AWS-oriented security checks.
• The team wants agentic coding and MCP support inside JetBrains.
• Cloud-aware debugging is more valuable than Copilot’s broader ecosystem adoption.

Windsurf Plugin: the former Codeium plugin for JetBrains

DIY AI score: 8.4/10

Windsurf Plugin is the current JetBrains product previously known as Codeium. Its 8.4/10 score comes from the Codeium row in the original DIY AI dataset, not from the higher-scoring standalone Windsurf editor row.

This is the most important correction in the updated article. The old 8.8 Windsurf score belongs to the coding environment now called Devin Desktop. It should not be presented as the score for Windsurf Plugin simply because both products continue to use Windsurf-related branding.

Inside JetBrains, Windsurf Plugin provides autocomplete, chat and Cascade-style agent functionality. It suits developers who want the assistant to plan changes, work across files and take a more active role than a conventional completion tool.

The plugin scores 8.7/10 for Integration Ease, 8.8/10 for Language Support and 8.5/10 for Refactoring Strength. Repository Context is 8.3/10. These are respectable results, but they do not support ranking the plugin above Amazon Q or Copilot on overall performance.

There is another practical limitation. A plugin operates inside APIs, panels and permission systems controlled by the host IDE. Devin Desktop controls its complete editor environment and can therefore offer a different agent experience. Similar branding does not guarantee feature parity.

Windsurf Plugin dataset scores

Code Accuracy	8.5/10	Language Support	8.8/10
Debugging Assistance	8.3/10	Integration Ease	8.7/10
Repository Context	8.3/10	Refactoring Strength	8.5/10
Test Generation	8.1/10	Documentation Generation	8.0/10

Choose Windsurf Plugin if

• You specifically want the Windsurf and Cascade workflow inside IntelliJ or another JetBrains IDE.
• Multi-file editing is more important than having the highest test-generation score.
• You do not want to move the project into Devin Desktop.
• Your team understands that old Codeium settings and package references belong to the same plugin lineage.

JetBrains AI Assistant: best native interface and agent host

DIY AI score: 8.2/10

JetBrains AI Assistant has the lowest overall score in this shortlist, but the number should be interpreted with caution. It measures the first-party assistant features represented in the DIY AI dataset. It does not score every external agent that can now run through the JetBrains AI interface.

The native assistant covers completion, chat, code explanations, documentation, commit assistance and smaller code changes. It scores 8.9/10 for Integration Ease because it fits the structure and conventions of JetBrains IDEs without feeling like a separate editor bolted on.

JetBrains AI Assistant can also act as an agent host. Through the Agent Client Protocol and supported integrations, developers can select external agents to edit files, run commands, and execute tests. At that point, the output quality depends partly on the selected agent, its model, instructions, and permissions.

This creates a common scoring mistake. A developer may run a strong external agent through JetBrains AI Assistant and conclude that the first-party assistant deserves the agent’s score. The interface and the agent need to be evaluated separately.

JetBrains AI Assistant dataset scores

Code Accuracy	8.3/10	Language Support	7.9/10
Debugging Assistance	8.2/10	Integration Ease	8.9/10
Repository Context	8.0/10	Refactoring Strength	8.3/10
Test Generation	8.0/10	Documentation Generation	8.1/10

Choose JetBrains AI Assistant if

• You want the AI interface to follow JetBrains conventions and project navigation.
• You plan to access several compatible agents from a single tool window.
• Your team prefers first-party IDE controls over another independent plugin.
• You are prepared to evaluate the host, agent and model as separate components.

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Copilot vs Amazon Q vs Windsurf Plugin vs JetBrains AI Assistant

Criterion	GitHub Copilot	Amazon Q Developer	Windsurf Plugin	JetBrains AI Assistant
Overall score	9.0/10	8.6/10	8.4/10	8.2/10
Best use	General JetBrains development	AWS-heavy projects	Cascade-style plugin workflow	Native interface and external agents
Integration ease	9.6/10	8.7/10	8.7/10	8.9/10
Repository context	8.9/10	8.5/10	8.3/10	8.0/10
Refactoring strength	8.8/10	8.5/10	8.5/10	8.3/10
Test generation	8.8/10	8.7/10	8.1/10	8.0/10
Agent workflow	Strong and increasingly broad	Strong, with AWS and MCP context	Central to the plugin experience	Depends on the selected first-party or external agent
Primary risk	Over-trusting familiar output	Choosing AWS specialisation without needing it	Confusing plugin and desktop product scores	Confusing the host with the active agent

Which JetBrains AI tool is best for multi-file refactoring?

GitHub Copilot is the safest default for bounded refactors where the affected module, tests and protected files are stated clearly. Windsurf Plugin is more attractive when the assistant needs to plan and apply coordinated edits across several files, while Amazon Q Developer has a stronger specialist case for Java transformations and AWS-connected projects.

Tool	Best refactoring use	Main review risk
GitHub Copilot	Controlled interface, method and module changes	Vague prompts can produce a wider diff than necessary.
Amazon Q Developer	Java upgrades and AWS-related transformations	Its specialist advantage is smaller in unrelated repositories.
Windsurf Plugin	Agent-led edits across several connected files	Broad changes require close inspection of missed call sites.
JetBrains AI Assistant	Native refactoring workflows using a selected external agent	Results depend on the active agent, not only the JetBrains interface.

Judge the accepted diff rather than the number of files changed. A useful refactor preserves behaviour, updates dependent call sites and passes the existing checks without unrelated formatting or configuration edits.

Why Devin Desktop is not included in the JetBrains ranking

Devin Desktop has a provisional 8.8/10 score inherited from the former standalone Windsurf editor row. That makes it a capable coding environment, but not a JetBrains plugin.

Choosing Devin Desktop means adopting a separate AI-first editor or running it alongside IntelliJ. That decision has a wider cost than installing a plugin. JetBrains users may depend on language-specific inspections, database tools, framework navigation, refactoring controls, profilers, debuggers and years of customised shortcuts.

A developer should move to Devin Desktop because its overall agent workflow justifies replacing those features, not because an outdated article says Windsurf has a higher JetBrains score. The current JetBrains extension’s relevant score is 8.4/10.

Do not run four inline completion engines at once

Installing several AI plugins may seem like an easy way to make a comparison, but the results are usually noisy. Plugins can compete for inline suggestions, keyboard shortcuts, context collection and tool-window space. The developer may not even be certain which assistant generated a completion.

A cleaner test uses one automatic completion provider at a time. A second plugin can remain available for a distinct agent or cloud task, but its inline completion should be disabled during the comparison.

1. Select one provider for automatic code completion.
2. Disable competing inline suggestion engines.
3. Assign separate keyboard shortcuts to each remaining chat or agent interface.
4. Run the same repository task with each candidate.
5. Compare accepted output, not the number of generated lines.

Agent permissions create a bigger risk than weak autocomplete

A poor completion is easy to reject. An agent with file-system, terminal and MCP access can make several connected mistakes before the developer notices the first one.

JetBrains teams should therefore judge permission handling alongside code quality. Check whether commands require approval, whether the agent can edit files outside the project, how secrets and environment variables are exposed and whether changes can be reviewed or rolled back before they spread.

Permission	Reason it may be needed	What can go wrong
Project file access	Multi-file implementation and refactoring	Unrequested changes to configuration, generated files or migrations
Terminal execution	Running tests, builds and package commands	Destructive scripts, altered dependencies or commands run in the wrong environment
MCP tool access	Connecting databases, documentation and external services	Wider data exposure and less predictable tool selection
Git access	Inspecting history and preparing changes	Unwanted commits, branch changes or inclusion of unrelated files
Environment access	Running the application with real configuration	Secrets reaching prompts, logs or third-party services

Start with the smallest permission set that can complete the task. An agent should earn broader access through predictable behaviour rather than receiving unrestricted access because the first demo looked impressive.

Best JetBrains AI tools for code review, static analysis and test validation

AI assistants can explain suspicious code and suggest possible defects, but they should not replace deterministic validation. The strongest setup assigns each layer a separate job instead of expecting one plugin to review, verify and approve its own output.

Validation layer	What it should handle
AI assistant	Review hypotheses, code explanations, missing edge cases and suggested tests
JetBrains inspections	Language-aware warnings, unreachable code and project-specific static checks
Compiler or type checker	Syntax, type compatibility and build correctness
Automated test suite	Behaviour, regressions and integration failures
Security tooling	Dependency, secret, configuration and vulnerability checks

An AI review comment is a lead to investigate, not proof that a defect exists. Run the same inspections and tests after every agent-generated change, including changes that appear too small to break anything.

A repeatable JetBrains AI coding test

A blank-file coding prompt mostly measures fluency. A useful JetBrains evaluation needs existing code, project conventions and executable checks.

Test	Task	What to record
Completion	Write a small method inside an existing class	Suggestions accepted, interruptions and manual corrections
Bug fix	Repair one reproducible issue with a failing test	Root-cause accuracy and unrelated edits
Multi-file feature	Add one bounded feature across two to four files	Context retention, architecture fit and diff size
Refactor	Change an interface while preserving behaviour	Missed call sites and regression failures
Test generation	Add unit tests for an existing service	Meaningful assertions, edge cases and false-positive tests
Agent control	Allow the tool to run the test command	Approval flow, command accuracy and response to failure

Track the time required to reach an accepted change, how much of the generated diff needed rewriting, how many validation steps failed and how long the review took. Generated volume is a poor success metric. A smaller clean change can save more time than a large diff that needs line-by-line repair.

Free, BYOK and offline AI plugins for JetBrains

The cheapest JetBrains AI plugin depends on how usage is billed and where the model runs. Headline subscription prices do not show the full cost of API usage, administration or local infrastructure.

Access model	Best for	Hidden limitation
Free tier	Testing completion quality before committing	Usage caps may prevent realistic repository and agent testing.
Bundled subscription	Predictable individual or team budgeting	The included models and agent allowances may be restricted.
Bring your own API key	Choosing models and paying directly for usage	The user inherits API billing, configuration and data-governance decisions.
Local or offline model	Restricted networks and repositories that cannot use hosted inference	Quality, latency and context length depend on local hardware and plugin support.

For corporate environments, check proxy support, authentication requirements, telemetry controls and whether the plugin can function without sending repository context to a hosted service. A free plugin is not a useful option if it cannot operate within the organisation’s security policy.Free, BYOK and offline AI plugins for JetBrains

The cheapest JetBrains AI plugin depends on how usage is billed and where the model runs. Headline subscription prices do not show the full cost of API usage, administration or local infrastructure.

Access model	Best for	Hidden limitation
Free tier	Testing completion quality before committing	Usage caps may prevent realistic repository and agent testing.
Bundled subscription	Predictable individual or team budgeting	The included models and agent allowances may be restricted.
Bring your own API key	Choosing models and paying directly for usage	The user inherits API billing, configuration and data-governance decisions.
Local or offline model	Restricted networks and repositories that cannot use hosted inference	Quality, latency and context length depend on local hardware and plugin support.

For corporate environments, check proxy support, authentication requirements, telemetry controls and whether the plugin can function without sending repository context to a hosted service. A free plugin is not a useful option if it cannot operate within the organisation’s security policy.

Which JetBrains AI coding tool should you install?

Choose GitHub Copilot if you want the best all-round result, work primarily through GitHub and need an assistant that developers can adopt without replacing their IDE.

Choose Amazon Q Developer if AWS services, Java transformations, cloud configuration and AWS-aware debugging appear regularly in the development workload.

Choose Windsurf Plugin if you specifically want the former Codeium plugin lineage and its Cascade-style workflow inside JetBrains. Use the 8.4 plugin score, not the 8.8 Devin Desktop score.

Choose JetBrains AI Assistant if native IDE integration or access to several external agents through a single JetBrains interface is the priority.

Choose Devin Desktop only after deciding that an AI-first standalone editor offers enough value to justify working outside or alongside JetBrains. Our wider AI coding tools comparison is the more appropriate place to compare Devin Desktop with other complete coding environments and repository agents.

Final verdict

GitHub Copilot remains the best AI coding tool for most IntelliJ and JetBrains users. Its 9.0/10 overall score, 9.6/10 Integration Ease result and broad development workflow make it the strongest default.

Amazon Q Developer ranks second at 8.6/10 and becomes the better choice where AWS knowledge is part of daily development. Windsurf Plugin ranks third at 8.4/10, using the score previously stored under Codeium. JetBrains AI Assistant ranks fourth among base assistants at 8.2/10, although the external agents it hosts must be evaluated separately.

The corrected product map removes the biggest source of confusion. Windsurf Plugin is the renamed Codeium extension. Devin Desktop is the renamed standalone Windsurf editor. Devin is a separate cloud agent. Only Windsurf Plugin belongs in a direct JetBrains plugin ranking.

FAQs

Is Windsurf now called Devin Desktop?

The standalone Windsurf editor is now called Devin Desktop. The Windsurf Plugin for JetBrains still exists under the Windsurf name.

Are Codeium and Windsurf Plugin the same product?

Windsurf Plugin is the current name for the plugin previously known as Codeium. Old settings, folders, logs, and documentation may still contain the name Codeium.

Why does Windsurf Plugin score 8.4 instead of 8.8?

The 8.4 score belongs to the former Codeium plugin row, which now maps to Windsurf Plugin. The 8.8 score belongs to the former standalone Windsurf editor, now called Devin Desktop.

What is the best AI coding plugin for IntelliJ?

GitHub Copilot is the best overall AI coding plugin for IntelliJ, according to DIY AI’s dataset. Amazon Q Developer is the strongest alternative for AWS-heavy projects, while Windsurf Plugin suits developers who prefer a Cascade-style agent workflow.

Can GitHub Copilot run through JetBrains AI Assistant?

Supported JetBrains configurations can expose GitHub Copilot as an agent through the AI Assistant interface. This is separate from installing the dedicated GitHub Copilot plugin, and the available features may differ between the two routes.

Can I use Copilot and Windsurf Plugin together?

Both can be installed, but running two automatic completion engines can produce duplicate suggestions, shortcut conflicts and an unclear testing process. Keep one inline completion provider active and reserve the other for a distinct chat or agent workflow.

Is Amazon Q Developer only useful for AWS code?

No. Amazon Q can assist with general coding, tests, debugging and refactoring inside JetBrains. Its clearest advantage appears when the project also depends heavily on AWS services, infrastructure or Cloud migrations.