ThinkRail agentic IDE: Pi in Isolated Workspaces
/thinkrail
ThinkRail is an agentic IDE worth using when you want pi coding sessions to run as reviewable, isolated branches instead of loose edits in your checkout. It earns its place through git worktrees, living specs, and a built-in workbench, but platform signing gaps and incomplete extension support keep it from feeling fully settled.
Keeping Agent Changes Separate
AI coding agents often need a clean place to experiment without disturbing the branch a developer is actively using. ThinkRail addresses that by opening a git repository as a project and cutting separate git worktree workspaces from it. Changes can then be reviewed, committed, pushed, and turned into a pull request before the main checkout is touched.
An IDE Built Around Pi
ThinkRail is a desktop or browser-hosted AI coding IDE built around the pi coding agent, which it runs in-process. The app supplies the workbench, editor, workspace isolation, project spec graph, and pull request controls while pi retains model, skill, cost, compaction, and session management.
Workspaces, Specs, and Agent Sessions
- โThinkRail creates separate git worktree workspaces with their own branches and working directories, so concurrent pi agents can modify code without touching the active checkout.
- โFor the active workspace, the workbench brings together Monaco editor tabs, a Files tree displaying live git change marks, terminals, documents, anchored review comments, and inline diffs.
- โThinkRail maintains connected SPEC.md files beside the code. Its dedicated spec tools let the agent read, search, and update project intent while the read-only Specs view presents the graph as a tree.
- โSeveral pi sessions can run concurrently in a workspace, each with its own model, token display, and cost readout. Developers can steer a running turn, queue a follow-up, delegate work to subagents, and edit the shared plan.
- โBundled workflows cover project setup, design brainstorming, and shipping a pull request. Portable Agent Skills already used by other coding agents are read in place, while MCP servers can be connected and controlled from Settings.
- โThinkRail links workspace state to GitHub, including branch and pull request status, CI checks, review decisions, ready state, merge actions, and agent-generated pull request descriptions.
Desktop and CLI Installation
ThinkRail can be installed as a native desktop app or through the self-contained thinkrail CLI. Download the platform asset and SHA256SUMS from the GitHub releases page, or run the installer: bash curl -fsSL https://raw.githubusercontent.com/JetBrains/thinkrail/main/install.sh | bash On Windows, use: powershell powershell -c "irm https://raw.githubusercontent.com/JetBrains/thinkrail/main/install.ps1 | iex" The installer verifies the binary checksum and adds thinkrail to PATH. The desktop app is also available for macOS 14+, Windows x64, and Linux x64 or ARM64. Linux desktop packages need glibc 2.38+, GTK 3, WebKitGTK 4.1, Ayatana AppIndicator 3, and librsvg 2.
Strengths
- โThinkRail keeps agent work out of the active checkout with explicit git worktree isolation, which makes parallel coding sessions easier to inspect and review.
- โLiving SPEC.md files turn project intent into maintained agent context rather than leaving it buried in chat history.
- โThe IDE includes the editor, terminals, change markers, structured review comments, inline diffs, plan editing, and GitHub pull request controls around the pi agent.
- โThinkRail reuses pi configuration, model providers, Agent Skills, and MCP servers, so it does not require users to rebuild their existing agent setup.
- โThe desktop app and browser-hosted CLI use the same engine host, while app state remains under `~/.thinkrail`.
Platform and Capability Constraints
- โณFull extension support is still planned rather than available. Settings can switch off the extensions currently supplied to the agent, but ThinkRail cannot yet build arbitrary new tools through the promised extension system.
- โณLinux artifacts are unsigned, and the macOS CLI is signed but not notarized. Older desktop packages and locally built DMGs can also trigger Gatekeeper restrictions.
- โณThinkRail deliberately gives pi control over models, skills, compaction, cost, and session state. It exposes that configuration, but the app does not assemble prompts behind the user's back.
- โณThe desktop build depends on platform-specific GTK, WebKitGTK, Ayatana AppIndicator, librsvg, and glibc packages, so Linux setup is less self-contained than the macOS and Windows releases.
- โณGitHub pull request management is a central workflow, but other hosted repository platforms are not described in the project documentation.
ThinkRail Versus Using Pi Alone
Common ThinkRail Questions
ThinkRail is released under the Apache License 2.0. The repository includes the license file, and the README explicitly identifies Apache License 2.0 as the project's license.
ThinkRail can run in a browser through the `thinkrail` CLI, which opens the same application interface hosted by the local engine. A native desktop build is also available for supported macOS, Windows, and Linux platforms.
ThinkRail supports sign-in through JetBrains AI or any provider connected through pi's own authentication. Your model choice is configured through the provider integration rather than through a separate ThinkRail account system.
ThinkRail requires `git` on PATH and an available model provider for agent work. Sign in with JetBrains AI from the app or connect your own provider credentials through pi before starting coding sessions.
ThinkRail opens a git repository as a project and creates separate workspaces as git worktrees. Each workspace has its own branch and working directory, allowing agent changes to be reviewed and merged without altering the active checkout.
ThinkRail always sends basic usage events and error reports tied to a random installation ID. The README says these exclude prompts, code, files, credentials, account identity, file paths, names, and URLs; additional sharing remains optional.
Best use cases
- โขHave a coding agent explore or implement a feature in an isolated branch, then inspect the diff before merging it.
- โขRun multiple pi sessions against separate git worktrees when tasks can proceed independently.
- โขPreserve project intent in living SPEC.md files that an agent can maintain across sessions.
- โขReview an agent-authored pull request, monitor CI checks, and send failing checks back to the agent.
- โขUse an existing pi configuration, Agent Skills, model providers, and MCP servers inside a graphical workbench.
Who should try it โ and who should skip
ThinkRail suits developers who already use pi and want agent work isolated, specs maintained, and changes reviewed in a proper workbench. It is especially useful when concurrent tasks, branch hygiene, and GitHub pull requests are part of the daily workflow. Skip it if you only want a terminal agent, need another repository hosting platform, or require fully signed Linux and notarized macOS artifacts today.
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