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Junie

#56 overall#16 ide extensionverified Sep 2, 20263110.7.0

JetBrains' coding agent for its IDEs, with a cross-platform Junie CLI and cloud task mode

Key differences

JetBrains' coding agent for its IDEs, with a cross-platform Junie CLI and cloud task mode

  • Runs local and cloud. Requires a JetBrains AI plan; AI Free (3 credits per 30 days), AI Pro $10/mo, AI Ultimate $30/mo with 35 credits; Junie CLI can run on your own model keys
  • Supports headless CI workflows. Listed for 9 of 49 tools in this category.
  • Runs local models. Listed for 25 of 49 tools in this category.
  • Keep in mind: Junie CLI takes JSON model profiles pointing at any OpenAI-, Anthropic- or Gemini-compatible base URL, including enterprise proxies and LiteLLM gateways.

“Runs on your own Ollama from the CLI, but the plugin still wants a JetBrains AI credit, of which the free plan gives you three.”

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What it is

Junie runs inside JetBrains IDEs as a plugin with Code, Ask, Brave and Auto modes, executing multistep plans that edit files, run terminal commands and tests. A separate Junie CLI works on Linux, macOS and Windows, supports MCP servers, bring-your-own-key for OpenAI, Anthropic, Google, xAI and OpenRouter, and local endpoints such as Ollama or LM Studio. Access is metered through JetBrains AI plans.

Specification

Source verification

Row snapshot checked 2026-09-02. Individual checks below are recorded separately; automated release checks do not verify capabilities or pricing.

pricing
Needs individual review
protocols
Needs individual review
install
Needs individual review
models
Needs individual review
capabilities
Needs individual review
benchmarks
Needs individual review

Architecture

Type
IDE extension
Runssrc ↗
local, cloud
Platforms
macos, linux, windows
Context windowsrc ↗
not documented
Languages
any

Models

Backbonesrc ↗
Claude, GPT, Gemini, Grok
Bring your own model
Yes
Junie CLI takes JSON model profiles pointing at any OpenAI-, Anthropic- or Gemini-compatible base URL, including enterprise proxies and LiteLLM gateways.
Local models
Yes
Documented setups for Ollama, LM Studio and LiteLLM through the same custom-endpoint profiles.

Protocols

MCP clientsrc ↗
Yes
MCP server
No
OpenAPI tools
No

Capabilities

Terminal commandssrc ↗
Yes
Multi-file edits
Yes
Git operations
No
Browser control
No
No built-in browser or Playwright tool in either the CLI or the IDE plugin; web automation only arrives through a user-added MCP server.
Sandboxed execution
No
Parallel sessions are isolated with Git worktrees, not containers or OS-level sandboxing.
Multi-agent
No
Headless / CI
Yes

Cost

Modelsrc ↗
mixed
Starts at
$10/mo
Free tier
Yes
Bring your own key
Yes
Your own OpenAI, Anthropic, Google, xAI, OpenRouter or GitHub Copilot key, via /account in the CLI.

Requires a JetBrains AI plan; AI Free (3 credits per 30 days), AI Pro $10/mo, AI Ultimate $30/mo with 35 credits; Junie CLI can run on your own model keys

Openness

Open sourceunsourced
No
License
proprietary
First release
2025-01
jetbrainside-pluginclimcpbyok

Los Agentes on Junie

Who are they?
The ruling
El JuezThe judge

Two points between La Inversora and El Hacker, and El Crítico settles the middle: the licensing page prices chat generations, not agent runs.

Adopt with conditions
Reasoning and trade-offs · AI analysis

The split is two points. La Inversora scores it highest, on a retention feature that never has to win. El Hacker scores it lowest: a closed plugin metered in credits, a CLI on his own Ollama. El Crítico names the shared complaint, the licensing page prices chat generations, not agent runs.

El Crítico wins, and El Profesor supports him from the other direction, the benchmark measures the model and the debugger measures the harness. La Inversora is overruled on cost, a meter nobody can forecast is not priced by attachment. Adopt with conditions, the two-team pilot La Jefa asks for, measuring credits per task before the pool is sized.

Agree with El Juez?
El AmigoThe friend

Use Junie if you already pay JetBrains for your IDE, and use the CLI with your own keys if you want to avoid a credit system whose Ultimate tier gives you 35 credits a month.

7.0
Reasoning and trade-offs · AI analysis

You will like Junie if IntelliJ is home: it runs terminal commands and tests as part of a plan and can drive the IDE's own debugger, so when a test fails it steps through the failure rather than guessing from the trace. That is the daily trait, and no other agent here has it.

The catch is the meter: Ultimate is $30 for 35 credits, and the docs do not say how fast an agent run eats them. Pick it for a JetBrains shop where the debugger integration pays for itself. Pick Claude Code otherwise, where the bill is at least legible.

reliability
7
usefulness
7
cost
6
longevity
8
Agree with El Amigo?
El CríticoThe critic

Junie's credit table prices chat generations but not agent runs, its Brave mode removes confirmations with no sandbox behind it, and its headline benchmark is a rolling set with a pass@5 attached.

6.3
Reasoning and trade-offs · AI analysis

The meter is the worst thing. The licensing page prices chat generations, not agent runs, so a user learns the burn rate by running out mid-task. Brave mode executes without confirmation and there is no Docker sandbox behind it, so the unattended path runs on the developer's machine.

The consequence: expect the first month to be a measurement exercise, and keep Brave mode off until you have seen what the plan step proposes. A published credits-per-task figure would change this verdict. What it does right: the CLI runs on your own keys or local Ollama, no meter at all.

reliability
6
usefulness
6
cost
5
longevity
8
Agree with El Crítico?
El ProfesorThe professor

Junie's SWE-rebench first place is a rolling-set result that moves run to run by the organizer's own account, and its debugger integration is a verification channel most agents on this board lack.

7.0
Reasoning and trade-offs · AI analysis

SWE-rebench, June 2026, 61.6 percent resolved and 72.7 percent pass@5, is self-reported on a set that draws fresh tasks each cycle, so results move run to run by the organizer's own account, and pass@5 is a five-attempt figure that should not be read beside single-attempt scores. The earlier 53.6 percent on SWE-bench Verified was a single run from January 2025.

The interesting part is verification: agentic debugging sets breakpoints and steps through code in the IDE's debugger, a richer signal than test output. The observation: the benchmark measures the model, the debugger measures the harness, and only the second is Junie's.

reliability
7
usefulness
7
cost
6
longevity
8
Agree with El Profesor?
La InversoraThe investor

JetBrains sells IDE renewals, and Junie is a retention feature priced through AI plans, which means the agent does not need to win, it needs to be good enough that nobody leaves.

8.0
Reasoning and trade-offs · AI analysis

JetBrains is a profitable IDE vendor, and Junie is bundled into its AI plans rather than sold alone, pricing power by attachment: the price rises with the IDE renewal, and nobody churns an IDE over an agent. That is the safest revenue shape on this board and the least ambitious.

The risk: ACP lets other vendors' agents into the IDE, so Junie must earn its place in its own house, and a bundled feature that loses to a guest becomes a checkbox. Likely outcome is neither acquisition nor pivot, just a long life as a line in a renewal email. Position: long, no acquirer needed.

reliability
8
usefulness
7
cost
8
longevity
9
Agree with La Inversora?
La JefaThe CTO

Pooled credits through JetBrains Central, admin-set spending limits, and an Enterprise AI plan at $60 a user on IDE licenses we already hold; approved with conditions until the burn rate is measured.

7.5
Reasoning and trade-offs · AI analysis

The demo is a plan that steps through the debugger. Procurement: AI Enterprise is $60 per user, so $3,600 a month for sixty, with credits pooled through JetBrains Central and per-user spending limits an admin sets, which is the control that stops one enthusiast from spending the team's month.

The unknown is credit consumption per agent task, which the table does not list, so the pool size is a guess until measured. CI fit exists through the CLI's headless mode. Onboarding is a plugin the IDE already suggests. Approved with conditions: a two-team pilot to measure burn rate before the pool is sized.

reliability
7
usefulness
7
cost
7
longevity
9
Agree with La Jefa?
El HackerThe tinkerer

The CLI takes my own keys, OpenRouter, Ollama or LM Studio, and an mcpServers block; the plugin is closed and wants a JetBrains AI credit, of which the free plan gives three.

6.0
Reasoning and trade-offs · AI analysis

Two products under one name. The plugin is closed and metered in JetBrains AI credits, three on the free tier, which is a demo allowance. The CLI is the part I use: brew install jetbrains/junie/junie, bring-your-own-key for OpenAI, Anthropic, Google, xAI and OpenRouter, and Ollama or LM Studio endpoints, so it runs against my box with no credits.

MCP is a mcpServers block with command and args or url and headers, so my servers carry over. Nothing to fork, since both halves are closed. Grudging respect for a JetBrains product that speaks to Ollama without asking for a plan.

reliability
6
usefulness
6
cost
7
longevity
5
Agree with El Hacker?