JetBrains Counters AI Coding Rivals with Lightweight ThinkRail IDE

Introduction

JetBrains recently released ThinkRail, a new open-source IDE hosted on GitHub under the Apache-2.0 license. Unlike IntelliJ IDEA or PyCharm, ThinkRail is a lightweight application under 50 MB, built specifically as a workbench for AI coding agents. The project originated from JetBrains' internal Innovation Hub incubator and has garnered nearly 4,000 stars since its public debut in July.

The interface provides a conventional graphical environment — editor tabs, file tree, terminal, and diff views — but these elements are primarily designed for AI agents to operate within. The human role shifts to supervision: reviewing agent output, leaving comments, and approving changes.

The UI and interaction experience are notably polished, adding significant usability value.

Core Features

Complete Agent-Centric Workbench

ThinkRail's workspace is fully draggable and includes editor tabs, a file tree with Git change indicators, and a terminal. A standout feature is inline commenting: users can attach comments to specific files or diff hunks, which are fed directly into the chat context, eliminating the need for screenshots and verbal descriptions.

The chat panel supports multiple concurrent sessions, each potentially using a different model. Token usage and cost are displayed in real time. Users can intervene mid-task to redirect the agent, and queue subsequent tasks while the current one runs.

Isolated Workspace per Task

Each task gets its own isolated workspace using git worktree, creating a separate directory with an independent branch. This prevents conflicts when multiple agents modify the same codebase simultaneously. The author notes that other tools often suffer from agents clobbering each other's changes in a shared directory.

Workspaces are automatically managed: they collapse when the corresponding pull request is merged or after a week of inactivity, and restore when revisited. The top bar shows branch divergence from remote, PR status, CI results, and review outcomes. Failed checks can be sent back to the agent for automatic fixing with one click.

Persistent Memory via SPEC.md Specifications

To address the common problem of agents forgetting project conventions across sessions, ThinkRail introduces a graph of SPEC.md files linked together. Agents read, search, and update these specs during work, preserving design rationale and decisions. A read-only tree view visualizes the entire specification graph. The author found the specs in ThinkRail's own repository to be detailed and well-structured.

Skill and MCP Compatibility

Existing skill files (e.g., from Claude Code) work without format changes. Model Context Protocol (MCP) servers are supported, but with a cautious approach: any write operation requires explicit user approval in chat, and repository-declared MCP servers are disabled by default until manually enabled.

Installation

Installation is a single command per platform:

curl -fsSL https://raw.githubusercontent.com/JetBrains/thinkrail/main/install.sh | bash

On Windows PowerShell:

powershell -c "irm https://raw.githubusercontent.com/JetBrains/thinkrail/main/install.ps1 | iex"

After installation, run thinkrail <repo-path> to open a repository. Prerequisites include Git and credentials for a model provider (JetBrains AI or any custom endpoint). ThinkRail does not manage its own account system.

Author's Perspective: Why "Counterattack"?

The author frames ThinkRail as JetBrains' strategic response to the rise of AI-native coding tools like Cursor (a VS Code fork) and Claude Code (interface-less). Traditional heavy IDE platforms like IntelliJ, while feature-rich, become liabilities in an agent-first workflow where lightweight, purpose-built environments excel.

ThinkRail bypasses the IntelliJ platform entirely, avoids locking users into a proprietary model, and focuses engineering effort on workspace isolation, spec-driven memory, and auditable diffs — areas the author identifies as current gaps in AI coding tooling. While model capabilities converge, few tools seriously address multi-agent orchestration, code review workflows, and long-term project memory.

Community reception on daily.dev is mixed: only ~20% positive, with skepticism about AI-generated documentation and questions about product overlap with JetBrains' existing Air and Fleet. The author's stance: worth installing to experiment, but not yet a daily driver. The key takeaway is that JetBrains has genuinely rethought the IDE for the agent era rather than patching the old platform.

Project Repository

https://github.com/JetBrains/thinkrail