AI Code Review,
10 Agents Deep

9 specialized agents review in parallel. 1 coordinator synthesizes.
Comprehensive coverage across 9 domains.

Try in 30 Seconds

Install GitHub App →

CLI or GitHub App — your choice. Get started in 30 seconds.

Free & open source · Built by Jansen003 · LLM-powered

Demo Mode — No API Key Required

revhive review --diff HEAD~1

# RevHive Review Report

🚨 Risk Score: CRITICAL (92/100)

1 Critical · 1 High · 8 Medium · 12 Low

## Overview

Review completed with 22 findings across 9 agents.

Severity breakdown:

CRITICAL: 1 · HIGH: 1 · MEDIUM: 8 · LOW: 12

### [CRITICAL] Remote Code Execution via shell injection

SecurityAgent · Line 45

User input passed unsanitized to subprocess.call() allows arbitrary command execution.

### [HIGH] SQL Injection via string interpolation

SecurityAgent · Line 12

User-controlled input interpolated directly into SQL query string.

### [MEDIUM] N+1 Query Pattern

PerformanceAgent · Line 28

Database query executed inside a loop, causing N+1 round-trips.

… 14 more findings

Start in 30 Seconds

Install

pip install revhive-ai

Set Key

export LLM_API_KEY=your-key

Review

revhive review --diff HEAD~1

CLI works standalone. Or install the GitHub App for automatic PR reviews.

10 Specialized Agents

Each agent is a domain expert. They review simultaneously, then the Coordinator synthesizes a single actionable report.

🎨

StyleAgent

Naming conventions & formatting

🔒

SecurityAgent

Injection & auth flaws

⚡

PerformanceAgent

N+1 queries & memory leaks

🧠

LogicAgent

Edge cases & race conditions

🏗️

RepoAgent

Architecture & tech debt

🔧

RefactorAgent

Design patterns & migration

🩹

FixAgent

Auto-fix with root cause

🧪

TestAgent

Unit & regression tests

📝

DocAgent

API & architecture docs

🎯

Coordinator

Deduplicate, prioritize, and synthesize the final report

Reviews Any Language

LLM-powered agents understand code in any language. Optimized patterns for 10+ languages.

Python· JavaScript· TypeScript· Go· Rust· Java· C++· Ruby· PHP· Swift· Kotlin

Works With Your LLM

Not locked into one provider. Bring your own API key.

MiMo

Default, free trial credits

Default

OpenAI

GPT-4o & GPT-4o-mini

Popular

Anthropic

Claude models, native support

Native

DeepSeek

Qwen

GLM

Kimi

Preset names: mimo, openai, deepseek, qwen, glm, kimi, claude

How It Works

Push Code

Open a PR or push to your branch. RevHive triggers automatically.

9 Agents Review

Security, performance, logic, style, and more — all run in parallel.

Get Report

A single consolidated report with severity-ranked findings lands on your PR.

Manual Review vs RevHive

	Manual Review	RevHive
Time per PR	30 – 120 min	Minutes, not hours
Miss rate	Varies by reviewer	Comprehensive coverage across 9 domains
Consistency	Depends on mood & fatigue	Consistent analysis, every time
Coverage	1 – 2 reviewers per PR	10 agents, all domains
Cost	Engineer hours	Free & open source

Code Review for Everyone

RevHive is an open-source project on a mission to make code review accessible, thorough, and private. CLI keeps code local; GitHub App relays diffs securely. No signup required. Free for individual developers and open source projects.

Frequently Asked Questions

Is my code sent to external servers?

In CLI mode, your code stays on your machine and is only sent to your chosen LLM provider. In GitHub App mode, PR diffs are relayed through our server to the LLM provider — your source code is never stored.

How much does the LLM API cost?

A single-file review uses ~35K tokens (~$0.06 with MiMo). You can also use OpenAI, DeepSeek, or any OpenAI-compatible API.

What languages are supported?

Python, JavaScript, TypeScript, Go, Rust, Java, C/C++, Ruby, PHP, Swift, and Kotlin have optimized patterns. Any language works via LLM understanding.

How is this different from GitHub Copilot?

Copilot focuses on code generation. RevHive focuses on code review — 10 specialized agents analyze security, performance, logic, and more. They complement each other.

How accurate are the reviews?

RevHive uses multiple specialized agents to cross-check findings. Results depend on the LLM model used — stronger models produce more accurate reviews. We recommend MiMo or Claude for best results.

AI Code Review, 10 Agents Deep