kenn-io/roborev

Continuous background code review database for agents, work faster and smarter with accountability for every line of generated code.

What it solves

Roborev provides continuous, automated code review specifically designed for AI coding agents. It acts as a background quality-control layer that monitors code as agents write it, catching bugs, security risks, and architectural issues in seconds before they compound into larger problems.

How it works

Roborev operates through two primary mechanisms:

  1. Post-commit reviews: A git hook triggers an automated review of every commit made by an agent.
  2. Agent hooks: These integrate directly into your active coding agent sessions (such as Claude Code, Cursor, or Gemini CLI), feeding review findings back into the agent's workflow so the agent can address issues immediately.

Users can interact with findings via a terminal UI (TUI), a native browser UI, or by using the roborev fix and roborev refine commands, which instruct the agent to apply fixes and re-review the code until it passes all checks.

Who it’s for

It is designed for developers who use AI coding agents and want to maintain high code quality without manual intervention. It is particularly useful for those who want to automate the feedback loop between their coding agent and a review process.

Highlights

  • Automated Fix Loop: Supports iterative fixing where the agent applies changes, re-reviews, and repeats until the code passes.
  • Local Execution: Runs entirely on your machine without requiring external hosted services or infrastructure.
  • Broad Agent Support: Compatible with a wide range of popular coding agents including Claude Code, Cursor, Copilot, Gemini, and others.
  • Comprehensive Analysis: Performs various types of checks, including duplication, complexity, dead code, security risks, and test fixture validation.

Related

  • Project
    HexmosTech/git-lrc

    git‑lrc is a Go‑based tool that installs a global Git hook to run AI‑powered code reviews on every commit. It sends the diff to a configurable LLM (Gemini by default), returns inline comments, risk scores, and a slide‑deck summary, and records the review status in the commit message. Free for up to 30 k LOC/month; paid plans start at $32 per 100 k LOC.

  • Project
    appleboy/CodeGPT

    CodeGPT is a Go‑based CLI that uses LLM APIs (OpenAI, Azure, Gemini, Anthropic, Ollama, Groq, OpenRouter, LiteLLM) to auto‑generate Conventional‑Commit messages and short code‑review summaries. It installs a Git `prepare‑commit‑msg` hook, supports many providers, proxy settings, language translation, streaming output, and a flexible prompt/template system. Install via Homebrew, Chocolatey, an install script, pre‑compiled binary, or `go install`. Configure API keys (or dynamic helpers), choose models, and customize prompts. Ideal for developers who want AI‑assisted commit messages and quick reviews directly from the terminal.

  • Project
    github/app

    The GitHub Copilot app is a desktop client that lets you run Copilot‑powered AI agents in isolated git worktrees or cloud environments, manage issues/PRs across repos, and automate prompts. It provides visual “canvases” for agent output, parallel sessions, and an “Agent Merge” flow to auto‑merge PRs after checks. Installable on Windows, macOS, and Linux; requires a Copilot plan or your own model key.

  • Project
    alibaba/open-code-review

    An AI-powered code review CLI tool that combines deterministic engineering with an AI agent to provide high-precision, token-efficient reviews of Git diffs and full files.

  • Project
    iamfakeguru/agent-md

    agent‑md is a lightweight framework that installs rule files, git‑hooks, and helper scripts to turn AI coding assistants (Claude Code, Codex, Cursor, Windsurf) into contract‑driven agents. It provides a single source‑of‑truth markdown file (AGENT.md) plus enforceable checks (type‑check, lint, tests, visual screenshots, state updates) that block the model until the required evidence is present.