JetBrains AI

Join us on November 18, from 4:00–5:00 pm CET, for a livestream with Viktor Kiselev and Guillaume Garnier from the JetBrains Air product team. Your developers want to use the best AI agents for the job. As an engineering leader, you need to know who has access, what’s being used, and what it costs. Air […]

Setting coding agents up to handle several tasks while you continue working is easy. The harder part is knowing where each task stands, what each agent has changed, and what still needs your attention. Recently, we introduced Air in JetBrains IDEs. It brings coding agents into the environment where you already write, review, run, and […]

More and more of your terminal work now runs through tools that push the terminal harder than a shell prompt ever did: AI coding agents like Claude Code and Codex, full-screen apps like Neovim and tmux, system monitors and dashboards. These tools lean on modern terminal protocols, the kind today’s best standalone terminals are built […]

Today, JetBrains Air Governance becomes generally available. Formerly known as JetBrains Central, this is the organization layer of JetBrains Air, a system of products for agentic development. Air Governance gives you one place to manage which AI agents, models, and providers your teams use, set spending limits, and see what that usage costs and delivers. […]

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 same logic written in Python (95% interval […]

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 Company Netflix – Application Development Platform (ADP), GenAI Platform, and Agentic SDLC teams Challenge Coding agents were struggling […]

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. The architecture hasn’t changed since version 2: it’s still a compact, fast model with 2.5B active parameters, […]

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 JetBrains IDEs. It’s available now as a plugin on JetBrains Marketplace or natively […]

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. The eventual solution was Air Context. We got it working, we got it […]

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 files now render as formatted text while you edit – […]

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 developers don’t just use the AI tools we build. They decide for themselves […]

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 ran 80 paired tasks to test the ponytail skill for […]