Most "best AI coding tools" lists are written for JavaScript and Python developers. Java teams have different problems: codebases that are a decade old, strict typing, Spring and Jakarta EE conventions, and security reviews before anything new reaches production.
An AI coding tool that writes good React snippets can still get your Spring Boot service, your build file, and your UI layer wrong at the same time. So we looked at today's AI coding assistants and agents with one question in mind: which ones actually help Java developers ship?
Full disclosure: we make Vaadin. That's also why Vaadin isn't on the numbered list. It's a Java web framework with AI tooling, not a standalone coding assistant, so ranking it next to these tools wouldn't be fair. The eight tools below are in no particular order. After the list, we show how to make any of them better at building Java web apps with Vaadin AI.
The short version:
- Agentic work on large codebases: Claude Code
- An AI-native editor: Cursor
- AI inside the IDE you already use: GitHub Copilot
- IntelliJ IDEA users: JetBrains Junie and AI Assistant
- OpenAI-first teams: Codex
- Running many agents in parallel: Google Antigravity
- Spec-driven development and AWS teams: Kiro
- Large-scale Java modernization: Moderne
Building Java web apps? Whichever tool you pick, see "Better together" below for how to make it get your UI framework right.
What makes a great AI coding tool for Java developers
We judged every tool on six criteria that matter for Java teams:
- Codebase context. Does it understand a multi-module Maven or Gradle project, not just the open file?
- Agentic capability. Can it plan, edit several files, run the build and tests, and fix what fails?
- Java and IDE fit. Does it work well in IntelliJ IDEA, Eclipse or VS Code, and does it know Spring, Jakarta EE and modern Java?
- Extensibility. Does it support MCP servers and agent skills, so you can feed it accurate framework docs instead of guesses?
- Enterprise readiness. Is there SSO, admin policy, data-retention controls and a deployment model your security team will approve?
- Entry price. Is there a free tier or trial you can test on a real project?
A note on accuracy: when UI, business logic and data access share one typed language, there are fewer seams for an agent to get wrong. When they're all Java, the compiler catches many of the agent's mistakes before your users do. That's why the framework you build on matters as much as the assistant you pick.
The best AI coding tools at a glance
| Tool | Best for | What it is | Free option | Paid from |
|---|---|---|---|---|
| Claude Code | Agentic work on large codebases | Terminal and IDE agent | No | $20/month (Claude Pro) |
| Cursor | An AI-native editor | AI code editor with agents, also in JetBrains IDEs | Yes (Hobby) | $20/month |
| GitHub Copilot | AI in your existing IDE | IDE extension, chat, coding agent | Yes | $10/month |
| JetBrains Junie | IntelliJ IDEA users | Coding agent in JetBrains IDEs and CLI | Yes (Junie Lite) | $10/month (AI Pro) |
| OpenAI Codex | OpenAI-first teams | Agent in CLI, IDE, cloud and desktop app | Yes (ChatGPT Free) | $20/month (ChatGPT Plus) |
| Google Antigravity | Running agents in parallel | Agent-first desktop app and CLI | Yes | Google AI Pro subscription |
| Kiro | Spec-driven development, AWS teams | Agentic IDE and CLI, also inside JetBrains IDEs | Yes (50 credits/month) | $20/month |
| Moderne | Large-scale Java modernization | Refactoring engine for agents, built on OpenRewrite | Yes (OpenRewrite) | Contact sales |
Prices are list prices from each vendor's pricing page as of October 2026. Most vendors also offer annual discounts and team or enterprise tiers.
The 8 best AI coding tools for Java developers
1. Claude Code: best for agentic work on large codebases
Claude Code is Anthropic's coding agent. It runs in your terminal and has extensions for VS Code and JetBrains IDEs. You describe a task, and it reads the repo, plans, edits files across modules, runs Maven or Gradle, and fixes failing tests.
Why it stands out: a large context window for big multi-module projects, solid multi-step reasoning, plus support for MCP servers, skills, hooks and subagents.
Limitations: No free tier, and heavy agent use pushes you toward the higher plans.
Pricing: included in Claude Pro ($20/month, or $17/month billed annually), Max (from $100/month), Team and Enterprise.
2. Cursor: best AI-native code editor
Cursor is a VS Code-based editor built around AI. Its agent mode makes multi-file changes, cloud agents work in the background, and Bugbot reviews pull requests. Since March 2026, Cursor's agent is also available inside JetBrains IDEs through the Agent Client Protocol (ACP).
Why it stands out: fast, polished agent workflows, a choice of frontier models, plus MCP, skills and hooks on paid plans. Teams get SSO, privacy mode and usage analytics.
Limitations: The full experience, including Tab completions, is in Cursor's own VS Code-based editor, where Java support comes from extensions. In IntelliJ IDEA you get Cursor's agent, not its editor features.
Pricing: Hobby is free. Individual plans start at $20/month, Teams costs $40/user/month, and Enterprise is custom.
3. GitHub Copilot: best for AI in the IDE you already use
GitHub Copilot is still the easiest way to add AI to an existing Java workflow. It runs in IntelliJ IDEA and other JetBrains IDEs, Eclipse, VS Code, Visual Studio, Xcode and Neovim, with completions, chat, an agent mode, the Copilot CLI, and a coding agent that turns GitHub issues into pull requests.
Why it stands out: no new editor to learn, tight GitHub integration, and policy controls and audit logs for organizations.
Limitations: New features land in VS Code first. Agent skills and the Java upgrade agent (in preview) aren't available in JetBrains IDEs or Eclipse yet, and premium models use up AI credits quickly.
Pricing: Free tier available. Pro costs $10/month, Pro+ $39/month, Business $19/user/month and Enterprise $39/user/month.
4. JetBrains Junie and AI Assistant: best for IntelliJ IDEA users
Junie is JetBrains' coding agent, and AI Assistant adds chat and completions. Both run inside IntelliJ IDEA and use the IDE's own code intelligence: inspections, refactorings and project model. Junie is also available as a CLI.
Why it stands out: the IDE most Java developers already use, agents grounded in IntelliJ's Java analysis, and bring-your-own-key access to Claude, GPT and Gemini models. AI Assistant can also run other agents, including Codex, Cursor and Kiro.
Limitations: It works best inside the JetBrains ecosystem, and AI credits on the lower plans run out quickly with heavy agent use.
Pricing: Junie Lite is free. AI Pro costs $10/month and AI Ultimate $30/month, with discounts for yearly billing. AI Pro is included in the All Products Pack.
5. OpenAI Codex: best for OpenAI-first teams
Codex is OpenAI's coding agent. It runs in the CLI, as an IDE extension, in a desktop app and in cloud sandboxes, where it can work on several tasks in parallel and return pull requests. Since January 2026, it's also built into the AI chat in JetBrains IDEs.
Why it stands out: comes with every ChatGPT plan, runs parallel cloud tasks, and supports plugins.
Limitations: Usage limits depend on your ChatGPT plan, so heavy agent work needs Plus or Pro.
Pricing: included in ChatGPT Free, Go ($8/month), Plus ($20/month), Pro (from $100/month), Business ($20/user/month, billed yearly) and Enterprise.
6. Google Antigravity: best for running agents in parallel
Antigravity 2.0 is Google's agent-first development platform, with a desktop app and a CLI. The Antigravity CLI replaced Gemini CLI, which stopped serving most users on June 18, 2026. Instead of one assistant in your editor, you direct several agents and subagents at once, including background tasks that run on a schedule.
Why it stands out: a free plan with unlimited Tab completions, support for custom MCP servers, and the skills, hooks, subagents and plugins that Gemini CLI users already know.
Limitations: It's a standalone app, not an IntelliJ plugin, and teams still on Gemini CLI have to migrate.
Pricing: The Individual plan is free, with basic weekly limits. Google AI Pro and AI Ultra subscriptions raise the limits, and business plans run through Google Cloud.
7. Kiro: best for spec-driven development and AWS teams
Kiro is AWS's agentic IDE and CLI, and the official replacement for Amazon Q Developer. Before it writes any code, Kiro turns your prompt into a spec of requirements, design and tasks, which gives reviewers a plan to approve first. It also runs inside IntelliJ IDEA through JetBrains AI Assistant.
Why it stands out: spec-driven development, hooks, steering files, custom subagents and skills, and allow and deny lists for terminal commands. It also knows AWS well.
Still on Amazon Q Developer? New sign-ups closed on May 15, 2026, and the IDE plugins stop working on April 30, 2027. Plan your move now.
Limitations: There's no native Eclipse or Visual Studio plugin, and in IntelliJ IDEA Kiro runs through JetBrains AI Assistant rather than its own IDE.
Pricing: The free plan includes 50 credits a month. Pro costs $20/month, Pro+ $40, Pro Max $100 and Power $200.
8. Moderne: best for large-scale Java modernization
Moderne is built on OpenRewrite, the open-source refactoring engine many Java teams already use for Spring Boot and Java version upgrades. It gives AI agents a predictable way to change code. Instead of an LLM editing file after file, the agent runs tested recipes across hundreds of repositories at once, such as "upgrade all of these repositories to Java 25".
Why it stands out: the same change works the same way every time, across your whole codebase. It covers Java, Spring Boot and Kotlin, plus C#, Python and JavaScript.
Limitations: It's built for migrations and paying down technical debt, not everyday feature work, so pair it with one of the agents above. It's aimed at organizations with many repositories.
Pricing: OpenRewrite is free and open source. The Moderne platform is paid, so contact sales.
Better together: make any of these build Java web apps
Every tool above is a general-purpose assistant. None of them knows your UI framework out of the box, and that's where AI-generated web code usually goes wrong: invented APIs, outdated patterns, and a front end and back end that don't quite fit together.
Vaadin AI closes that gap for Java teams. It isn't another assistant to pick instead of the ones above. It's a set of tools that makes the one you already use better at building Java web apps:
- Vaadin MCP server: a free hosted server that gives any agent semantic search over Vaadin docs, component APIs and version information. It works with Claude Code, Codex, Cursor, Windsurf, Junie, GitHub Copilot and other MCP-capable agents.
- Agent Skills: instruction packs that teach agents how to do common Vaadin tasks well, such as building a form layout, polishing a view or generating an Aura theme. One plugin installs the skills and the MCP server in Claude Code or Codex.
- Vaadin Copilot: a free visual AI assistant that runs inside your app in dev mode. Edit the UI in the browser, describe a view in plain language or import a Figma design, and Copilot writes the Java source code.
Vaadin Copilot: edit the UI in the browser and Copilot writes the Java code.
Because UI, business logic and data access are all Java in a Vaadin app, the agent's mistakes surface at compile time, a full-stack change fits in one pull request, and browserless UI tests can check AI-generated code before it runs. For regulated environments, you can self-host the MCP server in air-gapped networks and restrict Copilot to EU hosting. Copilot, the MCP server and Agent Skills are free.
Does the extra context actually help? In VaadinBench, our open benchmark of real Vaadin tasks, Claude Opus 5 passed 50–67% of the tasks on its own and up to 100% with Vaadin's Agent Skills or MCP server. The benchmark is small, five tasks, so the code and results are public for anyone to rerun.
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Here's how to set it up in about five minutes:
- Pick your agent. Claude Code, Codex, Cursor, GitHub Copilot, Junie, Windsurf or any other agent that supports MCP will work.
- Connect the Vaadin MCP server. Point your agent at the free hosted server at
https://mcp.vaadin.com/docs. It now looks up the current Vaadin APIs instead of guessing from old training data. - Add Agent Skills. In Claude Code, run
/plugin marketplace add vaadin/agent-marketplace, then/plugin install vaadin-skills@vaadin-marketplace. This installs the MCP server and the skills in one step. - Start from a working project. Generate one at start.vaadin.com so the agent builds on a known-good Spring Boot and Vaadin setup.
- Refine visually with Copilot. Run the app, open Vaadin Copilot, and adjust layouts, themes and copy in the browser. Copilot writes the changes back to your Java code.
- Let the compiler and tests review the AI. Because the whole stack is Java, a broken call between UI and backend fails the build, not your demo. Vaadin 25.3 also adds a dev loop CLI (in preview) that hot-swaps the agent's changes and reports compile errors with file and line numbers, so the agent can fix them on its own.
When the app is ready for AI features of its own, Vaadin's AI components add chat, natural-language grids and charts, and forms that fill themselves from an uploaded document. They connect to Spring AI or LangChain4j, and for grids and charts the model gets your database schema, not your rows. The AI components are still in preview in Vaadin 25.3, which added markers that show when AI filled a form field.
See everything Vaadin AI can do →
What about v0, Lovable and Bolt?
AI app builders like v0, Lovable and Bolt are great for quick prototypes, but they generate JavaScript and TypeScript front ends such as React or Next.js. For a Java team, that means a second language, a second build and an API layer in between: more places for AI mistakes and more code to maintain.
If you want the same prompt-to-UI speed in Java, use Vaadin Copilot to generate and edit views visually, and an agent connected to the Vaadin MCP server for everything else. Your UI stays in the same codebase as your Spring services. See how Vaadin AI works.
How to choose the right AI coding tool for your Java team
Start from where your team works and what you're building, not from benchmark charts:
| If your team… | Start with |
|---|---|
| Builds business web apps in Java | Vaadin AI, plus any agent from this list |
| Lives in IntelliJ IDEA and won't switch | JetBrains Junie or GitHub Copilot |
| Wants an agent for big refactors and migrations | Claude Code or Codex |
| Is open to a new, AI-first editor | Cursor |
| Wants to run many agents at once, or uses Google Cloud | Google Antigravity |
| Is on AWS, or needs plans reviewed before code is written | Kiro |
| Has dozens of repositories to upgrade | Moderne |
| Can't send code outside its own network | A self-hosted model with a self-hosted Vaadin MCP server |
Three tips before you roll anything out:
- Pilot on a real ticket. Have two or three developers use each candidate on actual backlog work for two weeks, then compare merged pull requests, not impressions.
- Give the agent context. Connect MCP servers for your frameworks and write a short project instructions file. Context improves output more than switching models.
- Keep a human and a compiler in the loop. AI writes code fast. A typed, single-language stack and a code review process keep it from writing bugs just as fast. More on this in AI coding for Java teams: get the speed without losing control.
Frequently asked questions
What is the best AI coding tool for Java?
It depends on your workflow. Claude Code and Codex are strong agents for large Java codebases. GitHub Copilot and JetBrains Junie fit best if you want to stay in IntelliJ IDEA. If you're building web apps, add Vaadin AI so the agent knows your UI framework.
Can AI coding tools build a complete Java web app?
Yes, with the right context. An agent connected to the Vaadin MCP server can generate a working Spring Boot and Vaadin app with views, forms and data access in one language. You then refine it visually with Vaadin Copilot.
What is an MCP server, and why does it matter for AI coding?
The Model Context Protocol (MCP) lets an AI agent call external tools and data sources. A framework MCP server, such as Vaadin's, gives the agent current documentation and APIs, so it doesn't invent methods that don't exist.
Are AI coding tools safe for enterprise Java code?
Most offer business plans with SSO, admin policies and no training on your code. For stricter requirements, look at on-prem or air-gapped options such as Tabnine (now part of Tricentis), self-hosted open-weight models, and self-hosted MCP servers such as Vaadin's.
What happened to Amazon Q Developer and Gemini CLI?
Both are being retired. Amazon Q Developer closed to new sign-ups on May 15, 2026, and its IDE plugins stop working on April 30, 2027. AWS points users to Kiro. Google replaced Gemini CLI with Antigravity CLI. Gemini CLI stopped serving most users on June 18, 2026, though enterprise license holders kept access.
Is Vaadin Copilot the same as GitHub Copilot?
No. GitHub Copilot is a general coding assistant in your IDE. Vaadin Copilot is a free visual AI assistant that runs inside your Vaadin app during development and turns UI edits and prompts into Java code. They work well together.
How do I add AI features like chat to a Java web app?
Vaadin's AI components connect chat, grid, chart and form components to Spring AI or LangChain4j through one orchestrator. Users can ask questions or build grids and charts in plain language. For grids and charts, the model gets your schema and writes the query, so your rows aren't sent to it.
Ready to build Java web apps with AI? Explore Vaadin AI or start a new project in minutes.