Best Coding Agents for JetBrains IDEs in 2026

TL;DR

• JetBrains users have three main categories of AI coding tool: true IDE plugins (Kilo Code, JetBrains AI Assistant, Junie, Copilot, Continue), cloud-platform assistants (Amazon Q Developer, Gemini Code Assist), and autocomplete-first tools (Tabnine).
• Cursor, Windsurf, Cline, and most VS Code-first agents are not JetBrains plugins. Adopting them usually means leaving IntelliJ IDEA, PyCharm, WebStorm, GoLand, or another JetBrains IDE.
• For most JetBrains teams, Kilo Code covers the broadest agentic use case: 500+ models, BYOK, five agent modes, MCP support, VS Code coverage, CLI coverage, and model routing to control agentic spend.
• For a broader comparison that includes VS Code agents, terminal agents, and cloud-delegate tools, see Best AI Coding Agents in 2026. If your team uses VS Code instead, see Best Coding Agents for VS Code in 2026.

Best overall: Kilo Code is the best model-neutral coding agent for JetBrains teams that want BYOK, local model support, MCP, and one agent layer across JetBrains, VS Code, and CLI.

What "coding agent for JetBrains" means in 2026

JetBrains IDEs are not just text editors with a plugin panel. IntelliJ IDEA, PyCharm, WebStorm, GoLand, PhpStorm, Rider, RubyMine, DataGrip, and Android Studio all sit on top of a rich project model: inspections, refactors, test runners, debuggers, framework indexes, Gradle and Maven integration, run configurations, database tooling, and language-aware navigation.

That changes what "agentic" should mean. A useful coding agent for JetBrains should do more than answer chat prompts. It should be able to:

• Understand the current project structure instead of treating files as isolated text blobs
• Edit multiple files in a planned sequence, including tests, configuration, and documentation
• Work inside the IDE developers already use for debugging, refactoring, and test execution
• Connect to external tools through Model Context Protocol (MCP), APIs, databases, or internal services
• Support the model and billing strategy the team has chosen, not force every task through one vendor model

The critical distinction for JetBrains users is between true JetBrains plugins and tools that require a separate editor, terminal-only workflow, or VS Code migration. Cursor and Windsurf are polished AI-native editors, but they are VS Code forks. Cline is a strong open-source VS Code agent, but it does not run inside JetBrains. If your team has standardized on IntelliJ IDEA, PyCharm, WebStorm, GoLand, PhpStorm, Rider, or Android Studio, native plugin support matters.

JetBrains AI Assistant is the built-in first-party assistant; Junie is JetBrains' more autonomous coding agent. Kilo Code is different from both: it is a model-neutral agent that works across JetBrains, VS Code, and CLI workflows.

Quick comparison

Pricing approximate as of June 2026. Confirm current tiers with each vendor.

How we evaluated

We focused on tools that run inside JetBrains IDEs or that JetBrains users commonly evaluate when choosing an AI coding tool. The evaluation criteria:

1. Agentic capability: Does the tool execute multi-step tasks, or only answer chat prompts and complete code?
2. JetBrains nativeness: Is it a real JetBrains plugin, or does adoption require leaving the IDE?
3. Model flexibility: Can teams bring their own API keys, use local models, or switch providers freely?
4. Pricing transparency: Are tiers publicly documented, including team and enterprise controls?
5. Cost governance: Can teams route routine work to cheaper models and reserve frontier models for high-value tasks?

For the full decision framework - including terminal-native agents (Claude Code, OpenCode), VS Code-first agents (Cline), and cloud-delegate tools (Devin) - see Best AI Coding Agents in 2026.

True JetBrains plugins

Kilo Code (best open-source, model-agnostic agent)

Best for

• Teams that need agentic coding inside IntelliJ IDEA, PyCharm, WebStorm, GoLand, PhpStorm, Rider, and other JetBrains IDEs without giving up model choice.
• Mid-market teams (10-500 developers) that want centralized billing, usage analytics, and model restrictions across JetBrains, VS Code, and CLI workflows.
• Regulated teams that need BYOK, local model support, auditability, SSO, audit logs, and the option to self-host.

Overview

Kilo Code is an Apache 2.0 JetBrains and VS Code extension, plus an MIT-licensed CLI, built as a model-agnostic agentic platform. Every major model family - Anthropic, OpenAI, Google, Mistral, local Ollama endpoints, and more - connects through BYOK or the Kilo Gateway at zero markup. Five built-in agent modes (Ask, Architect, Code, Debug, Orchestrator) cover distinct workflow stages, from explaining a codebase to editing files and coordinating larger changes.

For JetBrains teams, the important point is continuity. Kilo Code runs where Java, Kotlin, Python, TypeScript, Go, PHP, Ruby, C#, and Android developers already work. A mixed engineering organization can use the same agent layer across IntelliJ IDEA, PyCharm, WebStorm, GoLand, VS Code, and terminal workflows instead of standardizing on a single editor.

Kilo Code has 325K+ JetBrains installs across IntelliJ, PyCharm, WebStorm, and GoLand, 420K+ VS Code installs, 3M+ Kilo Coders, and 40T+ tokens processed. The native JetBrains extension is still in Early Access and is not yet generally available through the standard JetBrains Marketplace release channel; follow the JetBrains native extension install docs to add the custom plugin repository. For product context, see the JetBrains landing page, IntelliJ page, PyCharm page, and WebStorm page.

Key strengths

• 500+ model catalog; swap providers per task without changing tools.
• True multi-IDE coverage: JetBrains, VS Code, and CLI through one agent platform.
• Five workflow modes for asking, planning, coding, debugging, and orchestrating larger tasks.
• MCP Server Marketplace for community-curated integrations with databases, APIs, ticketing systems, and internal tools.
• Apache 2.0 licensing on extensions, MIT on CLI; fully auditable and self-hostable.
• Kilo Pass bundles (Starter $19/mo, Pro $49/mo, Expert $199/mo) for predictable monthly spend with roughly 40% more credits than the subscription price.

Limitations

• Inline autocomplete quality lags behind Copilot and Tabnine for line-by-line suggestions; Kilo Code's strength is agentic multi-file work, not character-level completion.
• The native JetBrains extension is still Early Access rather than GA; teams should use the installation docs and validate it in their IDE fleet before broad rollout.

Pricing

Individual tier is free and pay-as-you-go: zero platform cost, API credits at provider rates. Teams at $15/user/mo adds centralized billing, usage analytics, and shared BYOK. Enterprise at $150/user/mo adds SSO/OIDC/SCIM, audit logs, model restrictions, SLA, and self-host option.

JetBrains AI Assistant (best built-in assistant for JetBrains loyalists)

Best for

• Developers who want AI chat, documentation help, and code explanation directly from JetBrains.
• Teams already standardized on JetBrains subscriptions that prefer a first-party vendor relationship.
• Users who value IDE-native UX over model configurability.

Overview

JetBrains AI Assistant is the first-party AI layer across JetBrains IDEs. It is tightly integrated into editor actions, commit messages, documentation generation, explain-code flows, and IDE-aware chat. For individual JetBrains users, that native feel is the main advantage: the assistant appears in familiar contexts and follows the conventions of the JetBrains platform.

The tradeoff is flexibility. JetBrains AI Assistant is not a model-neutral agent platform. Teams do not get Kilo-style BYOK economics, broad model routing, or the same level of provider choice. It is a strong default assistant for JetBrains-heavy shops, but less compelling for teams trying to govern AI spend across many providers or across multiple IDE surfaces.

Key strengths

• First-party JetBrains integration with familiar IDE actions and UX.
• Useful for code explanation, documentation, commit messages, and everyday IDE assistance.
• Strong fit for teams that already buy and administer JetBrains tooling centrally.
• No editor migration for IntelliJ IDEA, PyCharm, WebStorm, GoLand, PhpStorm, Rider, RubyMine, or DataGrip users.

Limitations

• No BYOK model economics.
• Model routing and provider choice are controlled by JetBrains.
• Less flexible for organizations that want one AI layer across JetBrains, VS Code, and CLI workflows.

Pricing

JetBrains AI features are sold through JetBrains AI plans and may also be bundled with some JetBrains subscriptions. Confirm current availability and seat pricing with JetBrains.

Junie (best JetBrains-first autonomous agent)

Best for

• JetBrains users who want a first-party autonomous coding agent rather than a general chat assistant.
• Teams that prefer JetBrains-native agent UX and centralized JetBrains procurement.
• Developers who want agentic behavior without configuring providers, keys, or external gateways.

Overview

Junie is JetBrains' agentic coding product. Where AI Assistant focuses on embedded assistance, Junie is aimed at higher-level software tasks: understanding the project, planning changes, editing files, and iterating through implementation. It is the most relevant first-party answer to agentic tools for JetBrains users.

The limitation is similar to JetBrains AI Assistant: it is not designed around model freedom. If your team wants to choose among Claude, OpenAI, Gemini, Mistral, local models, and cheaper routing options per task, Kilo Code is the more flexible architecture. If your priority is a first-party JetBrains agent with minimal setup, Junie is the natural candidate.

Key strengths

• Built by JetBrains specifically for JetBrains IDE workflows.
• Better fit for autonomous tasks than a chat-only assistant.
• Works in the IDE family where many Java, Kotlin, Python, Android, and backend teams already live.
• Reduces vendor sprawl for organizations standardized on JetBrains.

Limitations

• No BYOK model marketplace.
• Less suitable for teams that need shared AI governance across JetBrains, VS Code, and CLI users.
• Enterprise cost control depends on JetBrains' packaging rather than task-level model routing.

Pricing

Junie availability and pricing are tied to JetBrains AI packaging. Confirm current plan requirements with JetBrains.

GitHub Copilot (best for GitHub-native teams on JetBrains)

Best for

• Teams whose primary workflow runs through GitHub PRs, issues, and Actions.
• Developers who want mature autocomplete inside JetBrains IDEs.
• Enterprises that need Microsoft and GitHub procurement, SSO, and contractual no-training guarantees on business tiers.

Overview

GitHub Copilot is no longer just autocomplete. Across GitHub and IDE surfaces, Copilot has moved toward agentic workflows, code review, issue handling, and multi-agent routing through GitHub's Agent HQ strategy. In JetBrains IDEs, Copilot remains strongest as a coding assistant and completion engine, especially for teams already living in GitHub.

The main constraint is vendor control. Copilot does not offer BYOK, and model access is governed by GitHub and Microsoft. The June 2026 AI Credits transition also means agentic usage is increasingly metered, which matters for teams that are moving from autocomplete to long-running agent tasks.

Key strengths

• Mature JetBrains plugin with strong inline completions.
• Deep GitHub integration for PRs, issues, and Actions-adjacent workflows.
• Business and Enterprise tiers include stronger governance and no-training guarantees.
• Familiar procurement route for organizations already buying Microsoft and GitHub products.

Limitations

• No BYOK; model routing is controlled by GitHub and Microsoft.
• Agentic usage can introduce variable billing through AI Credits.
• Less model-neutral than Kilo Code and less JetBrains-native than JetBrains' own AI products.

Pricing

Copilot Pro is $10/month with $10 in monthly AI Credits. Pro+ is $39/month with $39 in monthly AI Credits. Business is $19/user/month with $19 in monthly AI Credits, and Enterprise is $39/user/month with $39 in monthly AI Credits. Code completions and Next Edit Suggestions remain included; agentic usage is metered through credits. For a direct billing and feature comparison, see Kilo Code vs GitHub Copilot.

Continue (best configurable assistant for BYOK teams)

Best for

• Teams that want maximum configurability over models and context providers.
• Developers comfortable managing YAML configuration and provider keys.
• Organizations evaluating open-source AI assistants with local-model or on-premises requirements.

Overview

Continue is a configurable open-source coding assistant for VS Code and JetBrains. Its strength is explicit configuration: teams can declare model providers, context providers, and permissions in code. That makes it attractive for organizations that want policy-as-code control over AI assistance.

Continue is better positioned as a configurable chat and autocomplete assistant than a full autonomous coding agent. It can be powerful in the hands of developers who tune their setup, but it does not provide the same out-of-the-box multi-mode agentic workflow as Kilo Code.

Key strengths

• Declarative config for models, context, and behavior.
• BYOK and local model support.
• Works in both JetBrains and VS Code.
• Strong fit for teams that prefer open-source, config-driven tooling.

Limitations

• More setup required than first-party assistants.
• Less autonomous than Kilo Code or Junie for long-running multi-file tasks.
• Enterprise governance depends heavily on how the team manages configuration.

Pricing

Free and open-source; enterprise pricing available for support contracts.

Amazon Q Developer (best for AWS teams on JetBrains)

Best for

• Infrastructure teams writing CDK, CloudFormation, Lambda, IAM, ECS, S3, and DynamoDB code.
• Teams that want AWS-native security scanning and cloud-context assistance.
• Organizations whose primary cloud platform is AWS.

Overview

Amazon Q Developer brings AWS-specific intelligence into JetBrains IDEs. It is most useful when the coding task depends on AWS context: infrastructure-as-code, IAM policies, Lambda handlers, service configuration, dependency updates, and vulnerability remediation.

For general-purpose coding, Q Developer is less flexible than a model-neutral agent. It does not offer broad BYOK routing or the same cross-provider model choice as Kilo Code. But for AWS-heavy teams, the cloud-specific context can be valuable.

Key strengths

• AWS service catalog awareness for CDK, CloudFormation, Lambda, IAM, and related workflows.
• Security scanning and remediation features aligned with AWS development.
• JetBrains plugin support alongside VS Code and AWS Console workflows.
• Strong fit for cloud platform teams standardized on AWS.

Limitations

• Amazon models only; no BYOK.
• Weaker outside AWS-specific work.
• Less useful for multi-cloud, local-model, or provider-neutral AI strategies.

Pricing

Free tier covers basic chat and inline suggestions. Pro at $19/user/mo adds agent capabilities, security scanning, and infrastructure assistance.

Gemini Code Assist (best for GCP and Android Studio)

Best for

• Teams whose primary cloud is GCP.
• Android developers using Android Studio.
• Organizations that want Gemini-backed assistance in Google Cloud workflows.

Overview

Gemini Code Assist supports JetBrains IDEs and Android Studio, with its strongest differentiation in Google Cloud and Android workflows. It can help with Cloud Run, Cloud Build, GKE, BigQuery, logging, and Gradle-heavy Android development.

The limitation is the same pattern as other cloud-platform assistants: it is strongest inside its own ecosystem. For general-purpose model selection, BYOK economics, and cross-provider routing, Kilo Code is more flexible.

Key strengths

• Native fit for GCP and Android Studio workflows.
• Useful for cloud logs, deployment configuration, and Google platform code.
• Contractual no-training posture on paid business tiers.
• Strong option for teams already standardized on Google Cloud.

Limitations

• No BYOK.
• General-purpose coding is less differentiated outside Google and Android workflows.
• Weaker fit for teams that want provider-neutral model governance.

Pricing

Standard $19/user/mo (annualized); Enterprise $45/user/mo (annualized). Free tier available for individual non-commercial use.

Tabnine (best autocomplete-first JetBrains tool)

Best for

• Teams that primarily want inline code completion rather than autonomous agents.
• Enterprises with strict privacy requirements and a preference for controlled deployment options.
• Developers who want fast suggestions without changing their coding workflow.

Overview

Tabnine is an AI code completion product with mature JetBrains plugin support. It is not primarily an autonomous coding agent. Its value is fast, privacy-conscious inline suggestions that stay close to the current file and current editing flow.

That makes Tabnine complementary to agentic tools rather than a direct replacement. A team might use Tabnine for low-latency completions and Kilo Code for multi-file tasks, test fixes, migrations, and model-routed agent work.

Key strengths

• Mature JetBrains plugin support.
• Strong autocomplete focus with low workflow disruption.
• Privacy and enterprise deployment options.
• Good fit for teams that do not want agents executing broader tasks.

Limitations

• Not a full coding agent.
• Limited model flexibility compared with BYOK tools.
• Does not cover planning, terminal execution, MCP workflows, or long-horizon implementation the way agentic tools do.

Pricing

Free tier available; Pro and Enterprise plans are paid. Confirm current seat pricing and deployment options with Tabnine.

Tools that require leaving JetBrains

Cursor, Windsurf, Cline, and terminal agents

Many of the best-known coding agents are not JetBrains plugins.

• Cursor: a standalone AI-native editor based on a VS Code fork. Strong multi-file agent experience, but adoption means leaving JetBrains IDEs and migrating workflows.
• Windsurf: a standalone AI-native editor with a polished agent and autocomplete experience. Like Cursor, it requires moving out of JetBrains.
• Cline: a popular open-source VS Code agent with strong MCP support, but it is VS Code-only.
• Claude Code and OpenCode: terminal-native agents that can work alongside JetBrains, but they are not embedded JetBrains IDE plugins.

Leaving JetBrains has real costs for teams that rely on IntelliJ inspections, refactoring tools, run configurations, debugging, database tooling, Android Studio workflows, or centrally managed JetBrains settings. If those workflows are core to your engineering organization, the better default is a native JetBrains plugin.

Which JetBrains coding agent should you choose?

By primary use case

• Daily agentic coding in JetBrains with model flexibility: Kilo Code. Installs as a JetBrains plugin; BYOK across 500+ models; five agent modes for different workflow stages.
• First-party JetBrains assistance: JetBrains AI Assistant. Best for embedded chat, explanations, docs, and IDE-native convenience.
• First-party JetBrains autonomous tasks: Junie. Best when the team wants a JetBrains-built agent and does not need BYOK model routing.
• GitHub PR automation and autocomplete: GitHub Copilot. Strongest for teams deeply embedded in GitHub and Microsoft procurement.
• Configurable open-source assistant: Continue. Best for teams that want config-driven BYOK behavior and are comfortable tuning setup.
• AWS infrastructure and IaC: Amazon Q Developer. Best when the coding work is tied to AWS services and security workflows.
• GCP and Android development: Gemini Code Assist. Best for Google Cloud and Android Studio-heavy teams.
• Autocomplete-first workflows: Tabnine. Best when low-latency completions matter more than autonomous task execution.

By budget

• Free / BYOK only: Kilo Code Individual and Continue. Cost is primarily provider API spend.
• Under $20/user/mo base price: Kilo Code Teams ($15), Copilot Business ($19 plus AI Credits), Amazon Q Pro ($19), Gemini Code Assist Standard ($19 annualized).
• Enterprise: Kilo Code Enterprise ($150/user/mo with SSO, audit logs, model restrictions, SLA, and self-host), Copilot Enterprise ($39/user/mo for GitHub-native governance), plus JetBrains, Tabnine, Google, or Amazon enterprise plans depending on platform strategy.

By team size

• Solo or indie: Kilo Code Individual for BYOK model choice, JetBrains AI Assistant for first-party convenience, or Continue for configurable open-source assistance.
• Small teams (2-25): Kilo Code Teams keeps BYOK costs predictable across JetBrains, VS Code, and CLI users; Copilot is a strong add-on if the team is GitHub-heavy.
• Mid-size and enterprise: Kilo Code Enterprise is the strongest fit for multi-IDE, model-neutral governance. Copilot Enterprise fits GitHub-native governance. JetBrains AI Assistant and Junie fit organizations that want first-party JetBrains procurement and do not need broad model routing.

Conclusion

The JetBrains AI coding market is now split between first-party IDE assistance, cloud-platform assistants, autocomplete tools, and model-neutral coding agents. JetBrains AI Assistant and Junie are the natural first-party choices for teams that want JetBrains-native UX. Copilot is strongest for GitHub-centered organizations. Amazon Q Developer and Gemini Code Assist are strongest in their respective cloud ecosystems.

Kilo Code leads the model-neutral category for JetBrains users who want broad model access, BYOK economics, MCP support, and the ability to use the same agent layer across JetBrains, VS Code, and CLI workflows. That flexibility matters most when agentic coding moves from occasional experimentation to daily engineering work with real token budgets and real governance requirements.

For developers considering a full-stack comparison - including VS Code agents, terminal-native agents, and cloud-delegate tools - the Best AI Coding Agents in 2026 companion guide covers Claude Code, Devin, OpenCode, Cline, and the broader open-source BYOK field.

JetBrains users can install Kilo Code from the JetBrains setup page or follow the native extension installation docs. The native JetBrains extension is still Early Access and not yet GA, so teams should validate it before a broad production rollout.

FAQ

What is the best AI coding agent for JetBrains?

Kilo Code is the strongest model-neutral coding agent for JetBrains IDEs in 2026: 500+ models, BYOK, five agent modes, MCP support, multi-file editing, local model support, team controls, and coverage across JetBrains, VS Code, and CLI. Junie is the strongest first-party JetBrains agent for teams that prefer JetBrains-managed models and procurement.

What is the best AI coding agent for IntelliJ IDEA?

Kilo Code is the best fit for IntelliJ IDEA teams that want model choice, BYOK, local models, and the same agent layer across JetBrains, VS Code, and CLI. JetBrains AI Assistant is the best built-in assistant, and Junie is the best first-party JetBrains option for autonomous coding tasks.

Does Kilo Code work in IntelliJ IDEA, PyCharm, WebStorm, and GoLand?

Yes. Kilo Code runs as a JetBrains plugin across major JetBrains IDE workflows, including IntelliJ IDEA, PyCharm, WebStorm, and GoLand, while also supporting VS Code and CLI workflows for mixed-editor teams. The native JetBrains extension is still Early Access and not yet GA; use the JetBrains install docs to add the custom plugin repository.

Is JetBrains AI Assistant a coding agent?

JetBrains AI Assistant is best understood as a first-party AI assistant for JetBrains IDEs. It helps with chat, explanations, documentation, and IDE actions. Junie is JetBrains' more agentic product for autonomous coding tasks.

Can I use Claude in JetBrains IDEs?

Yes. Kilo Code connects to Anthropic's Claude Sonnet, Opus, and Haiku models via BYOK with zero markup on tokens. Continue also supports Anthropic keys through configuration. Copilot and JetBrains first-party products may expose Claude-backed capabilities only through their managed model routing, not through your own Anthropic API key.

What is the difference between Kilo Code and JetBrains AI Assistant?

JetBrains AI Assistant is first-party and deeply integrated with JetBrains UX, but model choice is managed by JetBrains. Kilo Code is model-neutral: it supports 500+ models, BYOK, local models, MCP integrations, team usage analytics, shared controls, and the same agent layer across JetBrains, VS Code, and CLI.

Can I use Cursor as a JetBrains plugin?

No. Cursor is a standalone application based on a VS Code fork, not a JetBrains plugin. Adopting Cursor means leaving JetBrains workflows such as IntelliJ inspections, refactors, run configurations, debugging, and project model integration.

What is the difference between a coding agent and AI autocomplete?

Autocomplete tools complete the current line or block as you type. Coding agents plan and execute multi-step tasks: they read the codebase, edit multiple files, run checks, fix failures, connect to external tools, and iterate toward a completed implementation. Kilo Code and Junie are agentic tools; Tabnine is primarily autocomplete-first.

Does Kilo Code work with local models in JetBrains?

Yes. Kilo Code supports local models via Ollama and LM Studio. Configure a local endpoint in settings and use locally hosted models such as Llama, Mistral, or DeepSeek the same way you would use a cloud provider. This is useful for air-gapped environments, privacy-sensitive work, and teams controlling inference costs.

How does Amazon Q Developer compare to Kilo Code for JetBrains?

Amazon Q Developer is the better fit for AWS-specific work such as IAM, CDK, CloudFormation, Lambda, and security remediation. Kilo Code is cloud-agnostic, supports 500+ models, BYOK, MCP, local models, and broader agentic workflows. For general-purpose coding outside AWS, Kilo Code is the more flexible choice.