If you’ve ever tried to review AI-generated code by copying passages into a chat window, explaining what’s wrong in plain text, then manually tracking whether your agent actually addressed each point — you already understand the problem R3 is solving.
R3 (Review. Revise. Resolve.) is an open-source, locally-running code review tool built specifically for the AI-assisted development workflow. It runs a local web server, presents diffs and documents in a review-friendly interface, and lets you leave structured, line-anchored feedback that your coding agent can consume directly. Version 0.5.0 was released on July 20, 2026.
The Core Problem: Chat Is a Poor Review Interface
Coding agents like Claude Code, Cursor, or Codex produce long output — planning documents, diff patches, refactored modules, or full file rewrites. Reviewing that output in a chat interface forces you into a linear feedback mode:
- Copy a passage that needs fixing
- Describe the issue in prose
- Send the message
- Hope the agent correctly correlates your feedback to the right part of its output
- Receive a revised response and repeat
This works for small, isolated changes. It breaks down when you’re reviewing a 500-line planning document with 12 separate issues, or a refactored module where the context for each comment matters. You lose track of what’s been addressed. The agent loses context for what change corresponds to which feedback. And the chat history becomes an unmaintainable record of what was actually decided.
R3 replaces this with a workflow modeled on the code review tools you already know — GitHub pull requests, GitLab merge requests, or tools like Gerrit — but built to close the loop directly with an AI agent running locally.
How R3 Works
R3 operates as a local server with a web interface. The general flow is:
1. Your agent produces output to be reviewed. This could be a document, a diff, a code file, or any structured text the agent has written.
2. The output is surfaced in R3’s web interface. You see the content in a review-friendly layout where you can navigate to specific lines, sections, or quotes.
3. You leave structured, anchored feedback. Each comment is pinned to the exact line or quoted passage it’s about. You track each comment through a resolution workflow — open, addressed, resolved — rather than relying on memory or manual bookkeeping.
4. The agent reads the structured feedback and iterates. The agent receives a structured representation of your comments (not a chat summary, but the actual anchored feedback data) and addresses each point.
5. You mark comments resolved. When the agent has handled a comment, you mark it resolved in the R3 interface. The loop closes cleanly.
Installing R3
R3 is published as an npm package under @hyperlogue/r3. You can install it globally using npm or run it directly via npx without a global install.
For the exact installation command and current usage instructions, refer to the official R3 documentation at:
- GitHub: https://github.com/hyperlogue/r3
- Live demo: https://hyperlogue.github.io/r3/demo/
The live demo runs the full R3 interface in your browser and is a good way to understand the workflow before setting up a local instance.
Note on commands: The README and npm package installation steps are the authoritative source for exact CLI syntax. This article describes the conceptual workflow rather than reproducing specific commands that may change between versions.
Integrating R3 with Claude Code
R3 is designed to work with any coding agent that can read structured feedback from a local source, including Claude Code. The integration pattern is:
- Start a Claude Code session where the agent will produce output to review.
- Surface the agent’s output through R3 — either by pointing R3 at the file the agent created, or by using the agent to write output to a location R3 is monitoring.
- Leave structured comments in the R3 interface. For code files, you pin comments to specific lines. For documents, you can quote passages.
- Let the agent iterate. The agent reads the structured feedback and updates its output.
- Resolve comments as the agent addresses each one.
Because R3 runs locally with no cloud dependency, it works regardless of your network conditions, and the feedback data stays on your machine. MIT licensed.
What R3 Doesn’t Do (Yet)
R3 is specifically a review interface — it doesn’t run agents or orchestrate multi-step workflows. It’s the feedback layer between you and your agent, not the task runner. Think of it as a specialized UI for one specific phase of the agentic development loop: the review and revision phase.
It also doesn’t integrate directly with version control workflows (PR reviews, branch management) as of v0.5.0. It’s a standalone local tool, not a git integration.
Why This Matters for Agentic Development
The gap R3 addresses is real and growing. As coding agents produce longer and more complex output, the review phase becomes a bottleneck. The default tool for managing that review — the chat interface — isn’t designed for it. R3 is the first open-source tool I’m aware of that treats the agent review loop as a first-class problem worth solving with dedicated interface design.
For developers doing serious agentic work — running multi-hour coding sessions, generating full module rewrites, or producing large planning documents — R3’s structured approach could meaningfully reduce the overhead of the review phase.
It’s early (v0.5.0, ~14 GitHub stars at launch), but the workflow it enables is clearly useful, and the MIT license means it can evolve quickly with community contributions.
Getting Started
The best starting point is the live demo to understand the interface, followed by the GitHub README for installation instructions and the exact CLI commands for your setup.
Sources
Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260719-2000
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