Introduction

AI-powered code review is quickly becoming an important part of a developer's workflow. Instead of manually finding bugs and improving code quality, teams prefer to use AI code review tools that not only review diffs but also understand their codebase and team goals.

That's where AI code review tools are useful. In this post, we’re looking at two popular options developers are using right inside their editors: Entelligence AI and CodeRabbit.

What Is Entelligence AI?

Entelligence AI is a developer tool that helps you review code directly inside your editor, without waiting for a pull request. It gives feedback while your changes are in locals, points out potential issues, and suggests improvements in real-time.

It works with VS Code, Cursor, and Windsurf, and runs quietly in the background. When you make changes, Entelligence reviews the code and leaves helpful inline comments. These could be about logic errors, formatting, naming, or even missing edge cases. You can apply the suggestions with one click.

Once you raise a pull request, Entelligence continues to help. It adds a summary of what’s changed, leaves comments in the diff, and even includes diagrams when needed. You can react to its feedback to guide how it reviews in the future.

It also updates documentation automatically when your code changes and shows an overview of all PRs in a dashboard, so you can track what’s open, what’s been merged, and what still needs attention.

What Is CodeRabbit?

CodeRabbit is an AI code review tool that works inside your development workflow. Once installed in your GitHub repo, it automatically reviews pull requests using AI and leaves suggestions as comments.

You can also use CodeRabbit inside your editor (like VS Code, Cursor, and Windsurf) through its extension. CodeRabbit reviews can highlight issues, suggest improvements, and explain parts of the code you select. It supports both real-time editing feedback and Git-aware reviews, so you can use it while coding or after changes are pushed.

It’s a helpful tool when you want quick feedback without waiting for a teammate to review your pull request.

Before we start our comparison, let's Install:

Entelligence AI VS Code Extension⛵

Comparison

Now that we've learned about both tools, it's time to compare them. We'll test them in different situations. We have a file called Ask.js, and we'll make several changes to this code and test both tools in different scenarios.

import React, { useState } from 'react';

export default function Ask() {
  const [question, setQuestion] = useState('');
  const [answer, setAnswer] = useState(null);

  const handleAsk = async () => {
    if (!question) return;
    try {
      const res = await fetch('https://yesno.wtf/api');
      const data = await res.json();
      setAnswer(data);
      const history = JSON.parse(localStorage.getItem('askaway-history')) || [];
      localStorage.setItem('askaway-history', JSON.stringify([{ question, answer: data.answer }, ...history]));
    } catch (e) {
      console.error(e);
    }
  };

  return (
    <div style={{ padding: '1rem' }}>
      <h1>Ask a Question</h1>
      <input value={question} onChange={e => setQuestion(e.target.value)} placeholder="Type your question..." />
      <button onClick={handleAsk}>Ask</button>
      {answer && (
        <div style={{ marginTop: '1rem' }}>
          <h2>Answer: {answer.answer}</h2>
          <img src={answer.image} alt={answer.answer} style={{ maxWidth: '200px' }} />
        </div>
      )}
    </div>
  );
}

Local Changes Review

Let's begin by testing both tools on how well they review local changes. This means, without creating a PR or even for uncommitted changes. To test them, we wrote some buggy code with many issues like memory leaks, bad error handling, and multiple API calls to the same endpoint. We'll see if both tools can catch these problems correctly.

import React, { useState } from "react";

export default function Ask() {
  const [question, setQuestion] = useState("");
  const [answer, setAnswer] = useState(null);
  const handleAsk = async () => {
    if (!question) return;

    // Bad practice: No error handling for fetch
    const res = await fetch("https://yesno.wtf/api");
    const data = await res.json();
    setAnswer(data);

    // Bad practice: Synchronous operation that blocks UI
    for (let i = 0; i < 100000; i++) {
      Math.random();
    }

    // Bad practice: Multiple API calls without batching
    fetch("https://yesno.wtf/api");
    fetch("https://yesno.wtf/api");
    fetch("https://yesno.wtf/api");

    // Bad practice: Not checking response status
    const badRes = await fetch("https://nonexistent-api.com/data");
    const badData = await badRes.json(); // This will throw if response is not OK

    // Bad practice: Unhandled promise
    fetch("https://yesno.wtf/api").then((res) => res.json());

    // Bad practice: localStorage operations without try-catch
    const history = JSON.parse(localStorage.getItem("askaway-history")) || [];
    localStorage.setItem(
      "askaway-history",
      JSON.stringify([{ question, answer: data.answer }, ...history])
    );

    // Bad practice: Memory leak - not cleaning up
    setInterval(() => {
      fetch("https://yesno.wtf/api");
    }, 1000);
  };

  // Bad practice: Function with too many responsibilities
  const fetchDataBadly = () => {
    // Bad practice: Using var instead of const/let
    var url = "https://yesno.wtf/api";

    // Bad practice: Nested callbacks (callback hell)
    fetch(url)
      .then((response) => {
        fetch(url + "?retry=1").then((retryResponse) => {
          fetch(url + "?retry=2").then((finalResponse) => {
            finalResponse.json().then((data) => {
              setAnswer(data);
              // Bad practice: Mutating state directly
              answer.extraData = "modified";
            });
          });
        });
      })
      .catch(() => {
        // Bad practice: Empty catch block
      });

    // Bad practice: Not returning anything from async operation
  };

  return (
    <div style={{ padding: "1rem" }}>
      <h1>Ask a Question</h1>{" "}
      <input
        value={question}
        onChange={(e) => setQuestion(e.target.value)}
        placeholder="Type your question..."
      />
      <button onClick={handleAsk}>Ask</button>
      <button onClick={fetchDataBadly}>Bad Fetch</button>
      {answer && (
        <div style={{ marginTop: "1rem" }}>
          <h2>Answer: {answer.answer}</h2>
          <img
            src={answer.image}
            alt={answer.answer}
            style={{ maxWidth: "200px" }}
          />
        </div>
      )}
    </div>
  );
}

Let’s start with Entelligence first.

Entelligence AI:

With Entelligence AI, you don’t need to raise a PR or even commit anything. It starts reviewing your code directly in the editor as you make changes.

That makes it especially helpful when you’re working on rough drafts or fixing logic before things ever reach GitHub or Bitbucket.

On the same Ask.jsx file, Entelligence flagged a bunch of real issues early:

• UI-blocking loop
  
  It pointed out that the code let i = 0; i < 100000; i++) { Math.random(); } was unnecessarily CPU-intensive and could freeze the UI.

• Unbatched fetch calls
  
  It flagged that calling fetch() multiple times in a row without handling them properly was a bad pattern, wasteful, and prone to API limits.

• Missing response status checks
  
  It caught that await badRes.json() was being called without verifying badRes.ok, which could crash the app.

• No cleanup on intervals
  
  Warned about the setInterval() running continuously without any cleanup, which could lead to memory leaks.

• LocalStorage risks
  
  It noted that the localStorage logic lacked error handling, and flagged that the question was saved without sanitization, something that could lead to XSS if used later.

• Multiple concerns in one function
  
  It didn’t just stop at bugs. Entelligence also flagged architectural concerns, like how handleAsk() was doing too many unrelated things (fetching data, updating localStorage, looping, etc.).

• Unsafe rendering from the API
  
  Finally, it warned that rendering answer.answer and answer.image directly could be risky if the external API ever got compromised.

Each issue was highlighted inline, along with a suggested fix that you could accept with one click.

CodeRabbit:

CodeRabbit also begins reviewing your code when you make any changes. You don't need to commit the changes. You can choose to review committed changes, uncommitted ones, or both. Then, by clicking on Review, the code review will start.

In our Ask.jsx file, CodeRabbit caught several issues right away:

• No error handling around fetch
  
  It rewrote the entire logic using a try-catch block and added proper checks for .ok status.

• Unprotected localStorage operations
  
  It pointed out that using localStorage.getItem() and setItem() without a try-catch could fail in certain environments or when invalid JSON is stored.

• Uncleaned setInterval
  
  It warned us about a memory leak due to the missing clearInterval() in the cleanup phase.

• Unhelpful catch block and deep callback nesting
  
  It spotted an empty catch block in fetchDataBadly and suggested better error handling.

• Incorrect use of var and directly mutating React state
  
  Flagged usage of var instead of let/const, and the unsafe direct mutation of the answer state.

All of these issues appeared right after running a review, even before raising a PR. You could click a checkmark next to each suggestion, and the fix would be applied directly to the file.

It’s a pretty fast way to clean things up, especially if you like reviewing changes in batches.

Both are good, but Entelligence catches more suggestions. Besides the number of suggestions, the difference is also in the depth of reasoning behind them. Entelligence explains why something matters and what could happen if it’s ignored. All of this happens directly in the editor, without switching tools or breaking your flow.

Post-PR Code Review

Once a pull request is raised, both CodeRabbit and Entelligence AI jump in to review your changes. But they approach it a bit differently.

CodeRabbit

After raising a PR, CodeRabbit leaves a series of comments throughout the code.

In our test, it posted around 6 suggestions across the file. These included everything from removing redundant fetch calls to rewriting deeply nested functions using modern async/await patterns.

The suggestions are clear and directly tied to the lines of code they refer to. It also includes committable diffs, so you can apply fixes with a single click. The comments are categorized (e.g., "⚠️ Potential issue", "🛠️ Refactor suggestion") and easy to understand.

However, the review process took about 1–3 minutes to finish. After that, you can track your PRs in CodeRabbit’s dashboard.

Entelligence AI

Entelligence reviewed the same PR much faster, taking about 10 to 30 seconds. But more than speed, the structure of its feedback stood out.

Rather than just adding line-by-line comments, Entelligence started with a PR summary that explained the purpose of the changes. It also broke down the logic step-by-step and even included a sequence diagram to show how different parts of the code interacted.

You could react to suggestions with a 👍 or 👎 to fine-tune future reviews. It also showed the current review settings, like what types of issues it checks for, which can be updated right from the dashboard.

The dashboard view itself goes beyond just listing PRs. You can track review activity across repos, see the number of comments per PR, and adjust organization-wide settings. It’s designed for teams who want visibility and consistency without doing any extra work.

Context Awareness

To test context awareness, I intentionally included an import mismatch in one of the test PRs. I used a function called cleanInput in the main component, but the actual exported function from helpers.js was named sanitizeInput. Let’s see how both perform.

CodeRabbit:

CodeRabbit caught this and flagged it with a suggestion to fix the import. It also recommended input validation and consistent usage of sanitization logic.

That was a good sign, it understood how the function was used and what it was supposed to do based on the file's context. However, when I accepted the fix, it did not rename the import but removed all imports and even eliminated the usages of those functions from the file entirely. That was a bit strange.

But here’s where Entelligence AI went a step further.

Entelligence AI

While CodeRabbit focused on the diff, Entelligence looked beyond it. In another file that wasn’t included in the pull request diff but was still using the same incorrect cleanInput function, Entelligence flagged that as well. It suggested aligning the function usage across the codebase, even though that file wasn’t modified in the current PR.

It identified the mismatch and updated the import name from clean to sanitizeInput, preserving the structure of the file and only changing what was necessary.

This is the difference where Entelligence takes the lead. Entelligence looks at more than just the current changes, it sees how these changes impact the whole codebase. It understands patterns, connections between files, and past team decisions.

While both tools help find immediate problems, Entelligence is special because it has a wider view. It considers the entire project to make sure nothing else breaks quietly.

Code Fixes & Suggestions

To test how both tools handle real-world code improvements, I added a few intentional issues in the Ask.jsx file:

• Mutated the answer object directly: answer && (answer.extra = "bad mutation");

• Added an empty catch block

• Used alert() to display validation errors

• Removed the type from the <button> element

These are small, but realistic, examples of what a junior dev might miss, or what slips into quick prototypes. Here’s how both tools responded:

Entelligence AI

Entelligence pointed out the issues:

• Flagged the React state mutation and clearly explained why it’s a problem—modifying state directly leads to unpredictable UI behavior.

• Identified the missing type="button" on the <button>

• Highlighted the empty catch block, suggesting that it makes debugging harder and hurts resilience.

Each suggestion came with optional inline diffs, and in some cases, Entelligence explained the downstream risks, like potential form issues due to the missing button type. That extra context made it more than just a linter-like fix.

CodeRabbit

CodeRabbit also quickly flagged each of the changes with clear, actionable suggestions:

• State Mutation: It pointed out that directly modifying the state object (answer.extra = ...) violates React’s immutability principle, and recommended removing it entirely.

• Empty Catch Block: It advised against suppressing all errors and suggested proper error handling for better debugging and visibility.

• Blocking alert(): It recognized alert() as a poor UX choice and recommended using inline feedback or toast messages instead.

Each of these suggestions was tied to specific lines and could be applied with one click using CodeRabbit's interface.

While both tools surfaced the right issues, Entelligence added a bit more reasoning behind each suggestion, which can be helpful when teaching juniors or trying to avoid similar bugs later.

Tracking & Analyzing Pull Requests Across the Organization

When it comes to visibility across engineering work, both CodeRabbit and Entelligence AI offer dashboards and deeper insights, but they differ in depth, flexibility, and how much context they surface.

Entelligence AI

Entelligence AI is more comprehensive and better suited for scaling across teams:

• It gives you a centralized overview of all PRs across projects

• You can track who authored, reviewed, and merged PRs, along with auto-generated summaries

• Slack integration provides real-time updates for every review, PR status, and sprint summary

• Auto-updating documentation based on PR changes (or manually via the dashboard)

• Connect multiple repos and track sprint performance across them

• Deep Team Insights: performance reviews, contribution patterns, and sprint assessments

• Define custom guidelines so reviews match your team’s standards

Entelligence supports many tools that engineering teams already use:

• Communication: Slack, Discord

• Documentation: Notion, Google Docs, Confluence

• Project Management: Jira, Linear, Asana

• Observability: Sentry, Datadog, PagerDuty

These integrations help Entelligence pull in relevant context, enrich reviews, and automate workflows, like syncing sprint data from Jira, pushing updates to Slack, or linking changes to a Notion doc.

CodeRabbit

CodeRabbit offers a straightforward dashboard for tracking only PR activity. It also integrates with:

• Jira – to connect reviews with tickets

• Linear – to tie reviews to sprint planning

• CircleCI – to link CI builds with pull requests

There’s a Reports tab where you can create summaries, and a Learnings tab that tracks bot interactions across repositories, though these feel lightweight and dependent on manual use.

Which One Should You Choose?

Why Entelligence AI is a Better Fit

After using both tools across different scenarios, editing locally, raising PRs, and tracking reviews, it becomes clear that Entelligence AI does a bit more at every step:

• Less setup, more value early
  
  Entelligence starts reviewing the moment you make changes. No more context switching. It flags issues as you work, which helps prevent problems before they’re even committed.

• Reviews that explain, not just comment
  
  Instead of just saying what’s wrong, Entelligence explains why, whether it’s state mutation, architectural issues, or hidden risks like missing cleanup functions or unsafe rendering. This kind of feedback is especially helpful when you’re trying to learn or working with larger teams.

• Understands the bigger picture
  
  Where most tools focus on the lines that changed, Entelligence steps back to see how the new code fits into everything else. It notices function mismatches, duplicated logic, or cross-file inconsistencies, even when those files weren’t touched in the PR.

• One tool for everything
  
  PR summaries, team insights, documentation updates, performance reviews, Slack, and other workflow tool integrations all come from the same dashboard. This means fewer tabs, fewer integrations to manage, and a simpler workflow for teams.

• It grows with your team
  
  The tool learns from past reviews and adapts based on team preferences. So over time, feedback gets more tailored, not just to the code, but to how your team likes to build.

So while CodeRabbit is a solid helper for PRs, Entelligence AI ends up being more than a reviewer, it becomes part of how the team writes, shares, and improves code every day.

Learn more about the Entelligence AI code review extension:

Check Documentation

Conclusion

Both Entelligence AI and CodeRabbit offer valuable support for AI-assisted code review, but they support different levels of depth.

• Entelligence AI is like a smart teammate in your development process. It doesn't just look at code changes, it understands the entire codebase, follows architectural patterns, and works well with the tools your team already uses. It provides real-time code feedback, creates automatic documentation, and gives insights into sprints, making it ideal for teams focused on quality.

• CodeRabbit gives clear and useful feedback on pull requests. It's quick to set up, simple to use, and great for developers who want helpful suggestions during or after coding. Its integrations with GitHub and code editors make it a practical choice for teams or individual developers who want to automate basic reviews.

If you're looking for something that grows with your codebase, fits naturally into daily work, helps improve more than just the diff, and is a tool that has a full-context, long-term code quality with scalable insights, Entelligence AI is the better choice.

Install Entelligence AI VS Code Extension⛵