Junie
by JetBrains
An LLM-agnostic coding agent that uses your IDE's own tools — real debugger included.
Junie is JetBrains' AI coding agent, generally available since 17 June 2026 across IntelliJ IDEA, PyCharm, WebStorm, GoLand, Rider and the rest of the IDE family, plus a standalone CLI. Unlike editor-replacement agents it drives the IDE's real static analysis, safe refactorings and debugger, and it is model-agnostic: bring your own provider key or run models locally.
Junie is JetBrains' agentic coding assistant, first previewed in January 2025 and generally available on 17 June 2026. Its architecture is the differentiator: rather than reimplementing code understanding from source text, Junie drives the same machinery a JetBrains IDE already runs — project indexes, semantic search, static analysis, safe refactorings, build configuration and, since general availability, the real debugger. Agentic debugging lets it set breakpoints, step through execution, inspect variables and iterate against live runtime state, which reaches defects such as race conditions and state mismatches that pure code inspection cannot. General availability added Plan Mode, where the agent produces a structured planning document covering requirements, technical design and delivery stages before touching code; Remote Control, for asynchronous long-running tasks with progress tracked across devices; and pull-request review with project conventions in scope. The IDE integration was rebuilt on ACP, the Agent Communication Protocol, and MCP servers extend the agent to external tools and data. A Junie CLI reached beta in March 2026 and gained IDE connection in May 2026, running the same agent in a terminal, in CI/CD, and against GitHub and GitLab. Junie is deliberately LLM-agnostic: JetBrains' own subscription backend, bring-your-own-key for Anthropic, OpenAI, Google, xAI, OpenRouter and GitHub Copilot, or local runtimes through Ollama, LM Studio and LiteLLM so code never leaves the machine. JetBrains reports 61.6% resolved and 72.7% pass@5 on SWE-Rebench at roughly $1.14 average cost per task. It requires a paid JetBrains IDE, and JetBrains AI Enterprise adds on-premises LLM deployment.
An engineering team already standardised on paid JetBrains IDEs — the agent's entire advantage is reusing the IDE's indexes, refactorings and debugger, which is worth nothing to a team living in VS Code.
An agent that debugs against live runtime state and refactors semantically using the IDE's own tooling, instead of inferring behaviour from source text.
At a Glance
- Category
- Developer Tools
- Pricing
- Subscription, Usage-based, Freemium
- Target Market
- Enterprise Developers, Engineering Managers, Platform Engineers, CTOs
- Deployment
- Cloud-first, Hybrid, Self-hosted, API-based
- Founded
- 2000
- Headquarters
- Amsterdam, Netherlands
- Team Size
- 500+
- Customers
- JetBrains reports 15M+ users of its tools, 305K paying companies, and 90 of the Fortune Global Top 100 as customers; Junie-specific adoption is not published
Key Features
- ✓Agentic debugging
Sets breakpoints, steps through execution and inspects variables against live runtime state, catching defects that source inspection alone misses.
- ✓Plan Mode
Writes a structured plan covering requirements, technical design and delivery stages before editing code, so intent is reviewable up front.
- ✓IDE-native tooling reuse
Drives the IDE's project index, semantic search, static analysis and safe refactorings instead of reimplementing code understanding from raw text.
- ✓Model agnostic with BYOK and local runtimes
Use Anthropic, OpenAI, Google, xAI, OpenRouter or GitHub Copilot keys, or run Ollama, LM Studio and LiteLLM locally so source never leaves the machine.
- ✓Junie CLI
Runs the same agent in a terminal, in CI/CD pipelines and against GitHub and GitLab, connecting back to the IDE for shared context.
- ✓Remote Control for asynchronous tasks
Long-running agent tasks execute asynchronously with progress tracking that can be monitored from a different device.
- ✓Pull-request review
Reviews pull requests with the project's own conventions and surrounding codebase context in scope, not just the raw diff.
- ✓MCP server support
Model Context Protocol servers extend the agent to external tools, services and data sources beyond the local project.
- ✓AI Enterprise on-premises LLMs
JetBrains AI Enterprise adds self-hosted model options including OpenAI-compatible servers, Hugging Face models and JetBrains Mellum.
Capabilities
Use Cases
- •Runtime bug hunting
Point Junie at a failing test and let it set breakpoints and iterate against live state until it fixes the actual cause.
- •Framework or library migration
Hand it a multi-file mechanical migration and let the IDE's safe refactorings apply the change consistently across the whole project.
- •Pull request review
Junie reviews an incoming pull request against the project's own conventions, surfacing issues before a human reviewer spends time on it.
- •CI/CD and pipeline automation
The CLI runs the agent inside GitHub Actions or GitLab pipelines to repair failing tests or apply repository-wide changes.
- •Policy-restricted or air-gapped development
Teams route Junie to a local Ollama or LM Studio runtime so no proprietary source code ever leaves their own infrastructure.
- •Cost-controlled heavy agent usage
Daily heavy users attach their own provider API key and pay model rates directly rather than draining JetBrains AI credits.
Ideal For
Best For
- ✓Java, Kotlin and JVM codebases where JetBrains' static analysis and safe refactorings are strongest
- ✓Debugging runtime-only defects such as race conditions and state mismatches using real breakpoints
- ✓Well-scoped multi-file work: migrations, test fixes and mechanical refactors across a large existing project
- ✓Teams that must control the data path, via bring-your-own-key or a local Ollama, LM Studio or LiteLLM runtime
- ✓Running the same agent in CI/CD, GitHub or GitLab through the Junie CLI
Not Ideal For
- ✗Anyone on VS Code, Neovim or a free JetBrains Community edition — Junie requires a paid IDE such as IntelliJ IDEA Ultimate or PyCharm Professional, so the licence is a precondition rather than an add-on
- ✗Heavy all-day agentic users: credit burn is the single most-cited complaint, with reports of an AI Pro monthly quota exhausted in three days and $80 of Ultimate credits consumed in a single day on a large project
- ✗Greenfield 'build the whole product for me' vibe-coding — practitioners on Hacker News consistently describe giving it 'easy, neatly defined tasks' and verifying the output
- ✗Polyglot teams working mainly outside Java and Kotlin, where reviewers report suggestion quality is noticeably less consistent
Integrations
Deployment
Market & Ratings
JetBrains reports 15M+ users of its tools, 305K paying companies, and 90 of the Fortune Global Top 100 as customers; Junie-specific adoption is not published
Market Analysis
Pros
- ✓Agentic debugging against live runtime state is a genuine capability gap versus agents that only read source
- ✓Model-agnostic by design, so teams can optimise for cost, quality or data residency without leaving the tool
- ✓Independently strong benchmark showing, at 61.6% resolved and 72.7% pass@5 on SWE-Rebench with roughly $1.14 average cost per task
- ✓Practitioners repeatedly praise cost-effectiveness and speed relative to rivals, with one Hacker News commenter calling it 'cheap, very fast and most importantly actually listens to you'
- ✓Plan Mode makes the agent's intent reviewable before it edits code, which suits teams that will not accept opaque bulk changes
- ✓AI Enterprise on-premises LLM hosting gives regulated organisations a path most competing agents lack
Cons
- ✗Credit consumption is the dominant complaint across every surface: an AI Pro monthly quota exhausted in three days, and r/JetBrains reports of $80 of Ultimate credits burned in one day, pushing serious users past $100/month
- ✗The sibling JetBrains AI Assistant plugin sits at just 2.3 out of 5 across 851 ratings on the JetBrains Marketplace, with reviewers citing IDE slowdowns on large projects, confusing credit accounting and inconsistent suggestions outside Java and Kotlin
- ✗JetBrains admitted deleting negative Marketplace reviews — a representative conceded the approach 'looked shady' — and at least one reviewer said it 'destroyed my confidence and trust'
- ✗Requires a paid JetBrains IDE, so the true entry cost includes a licence most teams outside the ecosystem will not already own
- ✗Practitioner verdicts on capability are mixed rather than glowing: Hacker News comments range from 'nowhere near as good as Claude Code' to 'JetBrains' AI offering peaked last year' and 'okay but unreliable'
- ✗BYOK support inside the IDE lagged the CLI and was reported missing by users during 2026, so verify the exact authentication path for your workflow before committing
- ✗Only SOC 2 Type II and GDPR are stated on the JetBrains trust centre; ISO 27001, SSO scope and data residency for the AI services are not documented there
Pricing
AI Free
$0
- ✓3 AI credits every 30 days
- ✓Junie and AI Assistant access
- ✓Requires a paid JetBrains IDE (Community editions and Android Studio excluded)
AI Pro
From $10/mo
- ✓10 AI credits per month
- ✓Junie agent plus Junie CLI
- ✓$10/mo individual, ~$20/mo commercial
- ✓Included with All Products Pack and dotUltimate
AI Ultimate
From $30/mo
- ✓35 AI credits per month
- ✓Higher agentic throughput for daily use
- ✓$30/mo individual, ~$47/mo commercial
- ✓12-month rollover on purchased top-up credits
AI Enterprise
Contact for pricing
- ✓On-premises and self-hosted LLM deployment
- ✓OpenAI-compatible servers, Hugging Face models, JetBrains Mellum
- ✓Team governance through JetBrains Central
There are two costs and the second one is what surprises people. Junie requires a paid JetBrains IDE (IntelliJ IDEA Ultimate around $169/year personal, PyCharm Professional around $249/year), then AI credits sit on top: 3 per 30 days free, 10 on AI Pro ($10/mo individual, about $20 commercial), 35 on AI Ultimate ($30/mo individual, about $47 commercial), where one credit is roughly one US dollar of model usage. Credits are the real meter and agentic work drains them fast — documented cases include an AI Pro quota gone in three days and $80 of Ultimate credits in a single day on a large project, after which you buy top-ups that carry a 12-month expiry. Bring-your-own-key bypasses JetBrains credits entirely and bills the model provider directly, which reviewers consistently find cheaper for heavy daily use, and local runtimes cost nothing per token. On-premises LLM hosting is gated behind AI Enterprise, which is quoted rather than listed.
Security & Compliance
Connect
Sources
This page was written from 8 sources, 6 on domains other than jetbrains.com.
- 1.blog.jetbrains.com — junie coding agent out of beta
- 2.webdeveloper.com — jetbrains junie ga agentic debugging
- 3.andrew.ooo — jetbrains junie ga out of beta june 2026
- 4.infoworld.com — jetbrains ai assistant panned in jetbrains marketplace
- 5.hn.algolia.com — hn.algolia.com
- 6.aiproductivity.ai — jetbrains ai assistant
- 7.jetbrains.com — trust centervendor
- 8.jetbrains.com — junievendor
Stay Ahead of the Curve
Weekly enterprise AI insights for technology leaders. No spam, no vendor pitches—unsubscribe anytime.
SubscribeRelated Products
Raindrop
AI agent monitoring that catches silent production failures: Sentry for AI agents
GitLab Duo Agent Platform
Agentic AI across the whole GitLab DevSecOps lifecycle: planning, coding, code review, CI/CD and security agents under one governance model
CodeRabbit
AI code review and agentic change management for teams shipping human- and machine-written code
Qodo
Agentic code review and governance layer for teams shipping AI-generated code at scale