AI IDEs · JetBrains
AI Assistant and the Junie coding agent across JetBrains IDEs.
Release history
We tried, and it builds. We compiled the decision-making part of a Maven-style build tool into a fixed weight matrix and used it to build a real Java library. The output matches Apache Maven's byte for byte. A clean build with tests took 1.49× as long as the…
Netflix plugged JetBrains’ headless code-intelligence engine – the same one behind IntelliJ IDEA – into Claude Code and Codex, then benchmarked it against a variety of scenarios. The details and numbers are available below. At a glance CompanyNetflix –…
Trained with reinforcement learning in real environments, Mellum2.1 is built for coding agents and fast sub-agents that run on your own hardware. Today, we’re releasing Mellum2.1, the next version of the 12B mixture-of-experts model we open-sourced in June.…
Last week, we shared our point of view on where we think agentic development is headed and introduced JetBrains’ answer to it: JetBrains Air, an open system of products for agentic development. Today, we’re launching the Early Access Program for Air in…
Part 1: Parsing, chunking, and vectorization Some time ago, we set out to build the best semantic code search platform we could: a RAG pipeline that gives LLM agents precise, citable evidence from real repositories instead of whatever grep happens to surface.…
Air gets a new multiproject view: open several projects in one window and run agents across them in parallel. The sidebar groups tasks by project, so you always see which agent is working on which project – no more window juggling for multirepo work. Markdown…
Over the past six months at JetBrains, our AI development expenses have increased roughly 10x. When the costs started rising, of course we noticed – and realized that we simply didn’t know how to control them systematically. We didn’t know because our…
Part 3 of a series where we take public “token saver” add-ons for coding agents and run the same paired A/B benchmark against each of them. Part 1 was the caveman skill (advertised −65%, measured −8.5%). Part 2 was rtk (advertised −60–90%, measured +7.6%). We…
The new release of JetBrains Air brings support for GitHub Copilot, OpenCode, Pi, Cline, and other ACP-compatible agents. It also adds IntelliJ-powered navigation and diagnostics for Java and Kotlin and runs Windows tasks in Docker containers. Try Air for Mac,…
Today, we’re launching JetBrains Context, a new repository intelligence layer that helps coding agents work more efficiently and produce higher-quality results on complex codebases. As part of the JetBrains AI for Teams and Organizations rollout, JetBrains…
Does “rtk” reduce Claude Code token usage? Part 2 of a series where we take public “token saving” add-ons for coding agents and run the same paired A/B benchmark against each of them. Part 1 was the caveman skill (advertised −65%, measured −8.5%). TL;DR: rtk…
Agentic coding benchmarks are getting closer to real-world software development. For Kotlin teams, the most important question is how reliably AI agents can complete end-to-end Kotlin tasks, from reading an issue to producing a solution that passes validation.…
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