Bito's AI helps developers dramatically accelerate their impact. It's a Swiss Army knife of capabilities that can 10x your developer productivity and save you an hour a day, using the same models as ChatGPT!

Bito AI makes it easy to write code, understand syntax, write test cases, explain code, comment on code, check security, and even explain high-level concepts. Trained on billions of lines of code and millions of documents, it's pretty incredible what we can help you do without having to search the web or waste time on tedious stuff.

What can you do with Bito?

Bito AI is a general-purpose AI assistance in that developers can ask any technical question, generate code from the natural language prompts, and get feedback on the existing code. Here are some things you can do with Bito AI Knowledge Assistance.

1. Generate Code: Ask Bito to generate code in any language with the natural language prompt. (e.g., write a Java function to convert a number from one base to another)

2. Command Syntax: Ask for the syntax of any technical command. (e.g., How to set a global variable for git?")

3. Test Cases: Generate test cases for the code.

4. Explain Code: Explain the selected code. Ask how this code works or what it does.

5. Comment Method: Generate a comment for the function or method to add to your code.

6. Improve Performance: Ask how you can improve the performance of a given code.

7. Check Security: Ask if the selected code has any known security issues.

8. Learn Technical Concepts: Ask a question about any technical concept (e.g., Explains B+ trees, explain Banker's algorithm)

Through extensions, Bito meets you where you work, your IDEs, like Visual Studio Code or JetBrains family of IDE.

Next, learn how to install Bito extensions.

Video guide

Watch the video below to learn how to download the Bito extension on VS Code.

Step-by-step instructions

1. In Visual Studio Code, go to the extension tab and search for Bito.

1. Install the extension. We recommend you restart the IDE after the installation is complete.

1. After a successful install, the Bito logo appears in the Visual Studio Code pane.

1. Click the Bito logo to launch the extension and complete the setup process. You will either need to create a new workspace if you are the first in your company to install Bito or join an existing workspace created by a co-worker. See Managing Workspace Members

Setup Bito extension in VS Code running through SSH

SSH (Secure Shell) is a network protocol that securely enables remote access, system management, and file transfer between computers over unsecured networks.

Visual Studio Code IDE allows developers to access and collaborate on projects from any connected machine remotely. The corresponding extension [Remote -SSH] must be installed on the host machine's Visual Studio Code IDE to utilize this feature.

The Bito VS Code extension seamlessly integrates with Remote development via SSH, allowing developers to utilize Bito features and capabilities on their remote machines.

Remote SSH connection and setup

Please follow the instructions given in the links below:

Video Guide:

Setup Bito extension in VS Code running through WSL

Running VS Code on WSL allows developers to work in a Linux-like environment directly from Windows. This kind of setup is to take advantage of development experience on both operating systems.

WSL provides access to Linux command-line tools, utilities, and applications, to enhance productivity and streamline the development process.

This setup ensures a consistent development environment across different systems, making it easier to develop, test, and deploy applications that will run on Linux servers.

WSL connection and setup

Please follow the instructions given in the links below:

Video Guide:

Video guide

Watch the video below to learn how to download the Bito extension on JetBrains IDEs.

Step-by-step instructions

1. In JetBrains IDEs such as IntelliJ, go to File -> Settings to open the Settings dialog, and click Plugins -> Marketplace tab in the settings dialog. Search for Bito.

2. Click "Install" to install the Bito extension. We recommend you restart the IDE after the installation is complete.

3. Bito panel will appear on the right-hand sidebar. Click it to complete the setup process. You will either need to create a new workspace if you are the first in your company to install Bito or join an existing workspace created by a co-worker. See Managing Workspace Members

Welcome to Bito, a developer's personal assistance that can boost productivity by more than 30%. In this chapter, we will cover the essentials of what you need to know to kickstart your journey with Bito.

Step-by-Step Instructions

2. Now click on the “Add to Chrome” button.
3. A popup will appear. Click on “Add extension” to install Bito in Google Chrome. The installation process will only take a few seconds.
4. Once Bito is installed, a new popup will appear, informing you that the Bito extension has been added to Chrome. Simply close this popup.
5. Now click on the “Extensions icon” given on the top-right side of your browser window. A popup box will appear. Here you will see something like “Bito” in the list. Select the small pin icon in front of it to always show the “Bito icon” in the browser ribbon.

   For example:
6. Now, click the “Bito icon” from the browser ribbon. You will be asked to create a new Bito account or sign in to your existing account. You can also try Bito as a Guest user. But you will be limited to only asking 10 questions as a Guest. So, it is better to sign up to get unlimited access for free.
7. Type your email address and click on “Continue”. A six-digit confirmation code will be sent to your email.
8. Enter the confirmation code in the given input field and click on “Submit”.
9. If the confirmation code you entered is correct, you will be logged in to Bito Chrome Extension. Here is how the user interface (UI) will look like.
10. You can try the prompts already given in the UI by simply clicking on them. Additionally, you can type your own technical questions in the given input box and click on the “Send” button.
11. Bito will quickly start writing the answer. Once the answer is generated, you can select the “Copy Answer” button to copy it to your clipboard and paste it wherever you want to.

Bito AI Assistance can help with generating new content or help with your existing code.

Click on each use case for an example instruction and response.

Generate Code

Instruction

Response

package main
import ( 
"encoding/json" "fmt" "net/http" )
type user struct { Name string json:"name" Age int json:"age" }
func main() 
{http.HandleFunc("/", func(w http.ResponseWriter, r *http.Request) { 
	// Create user 
	u := user{ Name: "John Doe", Age: 25, }
	// Encode user
	uj, err := json.Marshal(u)
	if err != nil {
		fmt.Fprintf(w, err.Error())
	}
	// Return user
	w.Header().Set("Content-Type", "application/json")
	w.WriteHeader(http.StatusOK)
	fmt.Fprintf(w, "%s\n", uj)
})
http.ListenAndServe(":3000", nil)
}

Command Syntax

Instruction

Response

Learn Technical Concepts

Instruction

Response

class Node:
    def __init__(self, order):
        self.order = order
        self.keys = []
        self.children = [None] * (order + 1)

class BPlusTree:
    def __init__(self, order):
        self.root = Node(order)
 
    def insert(self, key):
        if not self.root.keys:
            self.root.keys.append(key)
        else:
            pass # traverse the tree and insert the key
 
    def delete(self, key):
        pass # traverse the tree and delete the key
 
    def search(self, key):
        pass # traverse the tree and search for the key
 
# Create a B+ tree of order 5
bplus_tree = BPlusTree(5)
 
# Insert a key
bplus_tree.insert(10)
 
# Delete a key
bplus_tree.delete(10)

# Search for a key
bplus_tree.search(10)

Test Case

Instruction

genrateLabelDiffArray(newLabelArray,oldLabelArray=[])
  {
    let new_ =[],old_=[],merged_=[];
    newLabelArray.forEach((lbl)=>{
      if(new_[lbl] === undefined){ new_[lbl] =0;merged_[lbl]=0; }
      ++new_[lbl];
      ++merged_[lbl];
    });
    
    oldLabelArray.forEach((lbl)=>{
      if(old_[lbl] === undefined){ old_[lbl] =0; }
      if(merged_[lbl] === undefined){ merged_[lbl] =0; }

      ++old_[lbl];
      --merged_[lbl]
    });
    let final = [];

    merged_.forEach((value,key)=>{
      if(value!=0){ 
      //  final[key.toString()] = value;
      final.push(key+":"+value);
       }
    })
    
      return final;
  }

Response

Explain this Code

Instruction

genrateLabelDiffArray(newLabelArray,oldLabelArray=[])
  {
    let new_ =[],old_=[],merged_=[];
    newLabelArray.forEach((lbl)=>{
      if(new_[lbl] === undefined){ new_[lbl] =0;merged_[lbl]=0; }
      ++new_[lbl];
      ++merged_[lbl];
    });
    
    oldLabelArray.forEach((lbl)=>{
      if(old_[lbl] === undefined){ old_[lbl] =0; }
      if(merged_[lbl] === undefined){ merged_[lbl] =0; }

      ++old_[lbl];
      --merged_[lbl]
    });
    let final = [];

    merged_.forEach((value,key)=>{
      if(value!=0){ 
      //  final[key.toString()] = value;
      final.push(key+":"+value);
       }
    })
    
      return final;
  }

Response

Comment Method

Instruction

genrateLabelDiffArray(newLabelArray,oldLabelArray=[])
  {
    let new_ =[],old_=[],merged_=[];
    newLabelArray.forEach((lbl)=>{
      if(new_[lbl] === undefined){ new_[lbl] =0;merged_[lbl]=0; }
      ++new_[lbl];
      ++merged_[lbl];
    });
    
    oldLabelArray.forEach((lbl)=>{
      if(old_[lbl] === undefined){ old_[lbl] =0; }
      if(merged_[lbl] === undefined){ merged_[lbl] =0; }

      ++old_[lbl];
      --merged_[lbl]
    });
    let final = [];

    merged_.forEach((value,key)=>{
      if(value!=0){ 
      //  final[key.toString()] = value;
      final.push(key+":"+value);
       }
    })
    
      return final;
  }

Response

Improve Performance

Instruction

genrateLabelDiffArray(newLabelArray,oldLabelArray=[])
  {
    let new_ =[],old_=[],merged_=[];
    newLabelArray.forEach((lbl)=>{
      if(new_[lbl] === undefined){ new_[lbl] =0;merged_[lbl]=0; }
      ++new_[lbl];
      ++merged_[lbl];
    });
    
    oldLabelArray.forEach((lbl)=>{
      if(old_[lbl] === undefined){ old_[lbl] =0; }
      if(merged_[lbl] === undefined){ merged_[lbl] =0; }

      ++old_[lbl];
      --merged_[lbl]
    });
    let final = [];

    merged_.forEach((value,key)=>{
      if(value!=0){ 
      //  final[key.toString()] = value;
      final.push(key+":"+value);
       }
    })
    
      return final;
  }

Response

Instruction

genrateLabelDiffArray(newLabelArray,oldLabelArray=[])
  {
    let new_ =[],old_=[],merged_=[];
    newLabelArray.forEach((lbl)=>{
      if(new_[lbl] === undefined){ new_[lbl] =0;merged_[lbl]=0; }
      ++new_[lbl];
      ++merged_[lbl];
    });
    
    oldLabelArray.forEach((lbl)=>{
      if(old_[lbl] === undefined){ old_[lbl] =0; }
      if(merged_[lbl] === undefined){ merged_[lbl] =0; }

      ++old_[lbl];
      --merged_[lbl]
    });
    let final = [];

    merged_.forEach((value,key)=>{
      if(value!=0){ 
      //  final[key.toString()] = value;
      final.push(key+":"+value);
       }
    })
    
      return final;
  }

Response

// Rewritten Code
function genrateLabelDiffArray(newLabelArray, oldLabelArray = []) {
  let new_ = {},
    old_ = {},
    merged_ = {},
    final = [];

  newLabelArray.forEach((lbl) => {
    new_[lbl] = new_[lbl] ? new_[lbl] + 1 : 1;
    merged_[lbl] = merged_[lbl] ? merged_[lbl] + 1 : 1;
  });

  oldLabelArray.forEach((lbl) => {
    old_[lbl] = old_[lbl] ? old_[lbl] + 1 : 1;
    merged_[lbl] = merged_[lbl] ? merged_[lbl] - 1 : -1;
  });

  Object.entries(merged_).forEach(([key, value]) => {
    if (value !== 0) {
      final.push(key + ":" + value);
    }
  });
  
  return final;
}

Check for Security

Instruction

import database
search_term = input("Enter a search term: ")
query = "SELECT * FROM users WHERE name = '" + search_term + "'"
results = database.query(query)
print(results)

Response

Instruction

import database
search_term = input("Enter a search term: ")
query = "SELECT * FROM users WHERE name = '" + search_term + "'"
results = database.query(query)
print(results)

Response

import database

# Get the user-supplied search term
search_term = input("Enter a search term: ")

# Use the user-supplied search term to query the database
query = "SELECT * FROM users WHERE name = %s"
results = database.query(query, (search_term,))

# Print the results
print(results)

Find Style Issues

Instruction

genrateLabelDiffArray(newLabelArray,oldLabelArray=[])
  {
    let new_ =[],old_=[],merged_=[];
    newLabelArray.forEach((lbl)=>{
      if(new_[lbl] === undefined){ new_[lbl] =0;merged_[lbl]=0; }
      ++new_[lbl];
      ++merged_[lbl];
    });
    
    oldLabelArray.forEach((lbl)=>{
      if(old_[lbl] === undefined){ old_[lbl] =0; }
      if(merged_[lbl] === undefined){ merged_[lbl] =0; }

      ++old_[lbl];
      --merged_[lbl]
    });
    let final = [];

    merged_.forEach((value,key)=>{
      if(value!=0){ 
      //  final[key.toString()] = value;
      final.push(key+":"+value);
       }
    })
    
      return final;
  }

Response

It seamlessly integrates with Git providers such as GitHub, GitLab, and Bitbucket, automatically posting recommendations directly as comments within the corresponding Pull Request. It includes real-time recommendations from Static Code Analysis and OSS vulnerability tools such as fbinfer, Dependency-Check, etc. and can include high severity suggestions from other 3rd party tools you use such as Snyk.

The AI Code Review Agent acts as a set of specialized engineers each analyzing different aspects of your PR. They analyze aspects such as Performance, Code Structure, Security, Optimization, and Scalability. By combining and filtering the results, the Agent can provide you with much more detailed and insightful code reviews, bringing you a better quality code review and helping you save time.

The AI Code Review Agent helps engineering teams merge code faster while also keeping the code clean and up to standard, making sure it runs smoothly and follows best practices.

The AI Code Review Agent is built using Bito Dev Agents, an open framework and engine to build custom AI Agents for software developers that understands code, can connect to your organization’s data and tools, and can be discovered and shared via a global registry.

Why use an AI Agent for code review?

In many organizations, senior developers spend approximately half of their time reviewing code changes in PRs to find potential issues. The AI Code Review Agent can help save this valuable time.

AI Code Review Agent speeds up PR merges by 89%, reduces regressions by 34%, and delivers 87% human-grade feedback.

However, it's important to remember that the AI Code Review Agent is designed to assist, not replace, senior software engineers. It takes care of many of the more mundane issues involved in code review, so senior engineers can focus on the business logic and how new development is aligned with your organization’s business goals.

Pricing details

Learn more

Features overview

AI that understands your code

To comprehend your code and its dependencies, it uses Symbol Indexing, Abstract Syntax Trees (AST), and Embeddings.

One-click setup for GitHub, GitLab, and Bitbucket

Bito supports integration with the following Git providers:

Automated and manually-triggered AI code reviews

By default, the AI Code Review Agent automatically reviews all new pull requests and provides detailed feedback. To initiate a manual review, simply type /review in the comment box on the pull request and submit it. This action will start the code review process.

Pull request summary

Get a concise overview of your pull request (PR) directly in the description section, making it easier to understand the code changes at a glance. This includes a summary of the PR, the type of code changes, whether unit tests were added, and the estimated effort required for review.

Changelist

A tabular view that displays key changes in a pull request, making it easy to spot important updates at a glance without reviewing every detail. Changelist categorizes modifications and highlights impacted files, giving you a quick, comprehensive summary of what has changed.

One-click to accept suggestions

The AI-generated code review feedback is posted as comments directly within your pull request, making it seamless to view and address suggestions right where they matter most.

You can accept the suggestions with a single click, and the changes will be added as a new commit to the pull request.

Chat with AI Code Review Agent

Ask questions directly to the AI Code Review Agent regarding its code review feedback. You can inquire about highlighted issues, request alternative solutions, or seek clarifications on suggested fixes.

Real-time collaboration with the AI Code Review Agent accelerates your development cycle. By delivering immediate, actionable insights, it eliminates the delays typically experienced with human reviews. Developers can engage directly with the Agent to clarify recommendations on the spot, ensuring that any issues are addressed swiftly and accurately.

Bito supports over 20 languages—including English, Hindi, Chinese, and Spanish—so you can interact with the AI in the language you’re most comfortable with.

Incremental code reviews

AI Code Review Agent automatically reviews only the recent changes each time you push new commits to a pull request. This saves time and reduces costs by avoiding unnecessary re-reviews of all files.

Code review analytics

Custom code review rules and guidelines

The AI Code Review Agent offers a flexible solution for teams looking to enforce custom code review rules, standards, and guidelines tailored to their unique development practices. Whether your team follows specific coding conventions or industry best practices, you can customize the Agent to suite your needs.

We support two ways to customize AI Code Review Agent’s suggestions:

Multiple specialized engineers for targeted code analysis

The AI Code Review Agent acts as a team of specialized engineers, each analyzing different aspects of your pull request. You'll get specific advice for improving your code, right down to the exact line in each file.

The areas of analysis include:

• Security

• Performance

• Scalability

• Optimization

• Will this change break anything? Based on the diff can we include anything?

• Code structure and formatting (e.g., tab, spaces)

• Basic coding standards including variable names (e.g., ijk)

This multifaceted analysis results in more detailed and accurate code reviews, saving you time and improving code quality.

Integrated feedback from dev tools you use

Elevate your code reviews by harnessing the power of the development tools you already trust. Bito's AI Code Review Agent seamlessly integrates feedback from essential tools including:

• Static code analysis

• Open source security vulnerabilities check

• Linter integrations

• Secrets scanning (e.g., passwords, API keys, sensitive information)

Static code analysis

Using tools like Facebook’s open-source fbinfer (available out of the box), the Agent dives deep into your code—tailored to each language—and suggests actionable fixes. You can also configure additional tools you use for a more customized analysis experience.

Open source security vulnerabilities check

Linter integrations

Our integrated linter support reviews your code for consistency and adherence to best practices. By catching common errors early, it ensures your code stays clean, maintainable, and aligned with modern development standards.

Secrets scanning

Safeguard your sensitive data effortlessly. With built-in scanning capabilities, the Agent checks your code for exposed passwords, API keys, and other confidential information—helping to secure your codebase throughout the development lifecycle.

Supports all major programming languages

No matter if you're coding in Python, JavaScript, Java, C++, or beyond, our AI Code Review Agent has you covered. It understands the unique syntax and best practices of every popular language, delivering tailored insights that help you write cleaner, more efficient code—every time.

Enterprise-grade security

Bito and third-party LLM providers never store or use your code, prompts, or any other data for model training or any other purpose.

Bito is SOC 2 Type II compliant. This certification reinforces our commitment to safeguarding user data by adhering to strict security, availability, and confidentiality standards. SOC 2 Type II compliance is an independent, rigorous audit that evaluates how well an organization implements and follows these security practices over time.

Supported Programming Languages

AI Code Review

Basic Code Understanding is providing the surrounding code for the diff to help AI better understand the context of the diff.

Static Code Analysis and Open Source Vulnerabilities Check

Supported Tools and Platforms

Bito Cloud

Pros:

• Simplicity: Enjoy a straightforward setup with a single-click installation process, making it easy to get started without technical hurdles.

• Maintenance-Free: Bito Cloud takes care of all necessary updates and maintenance, ensuring your Agent always operates on the latest software version without any effort on your part.

• Scalability: The platform is designed to easily scale, accommodating project growth effortlessly and ensuring reliable performance under varying loads.

Cons:

• Handling of Pull Request Diffs: For analysis purposes, diffs from pull requests are temporarily stored on our servers.

Self-hosted service

Pros:

• Full Control: Self-hosting provides complete control over the deployment environment, allowing for extensive customization and the ability to integrate with existing systems as needed.

• Privacy and Security: Keeping the AI Code Review Agent within your own infrastructure can enhance data security and privacy, as all information remains under your direct control.

Cons:

• Setup Complexity: Establishing a self-hosted environment requires technical know-how and can be more complex than using a managed service, potentially leading to longer setup times.

• Maintenance Responsibility: The responsibility of maintaining and updating the software falls entirely on your team, which includes ensuring the system is scaled appropriately to handle demand.

We also support GitHub (Self-Managed) and GitLab (Self-Managed).

Connect Bito to your Git provider

Select your Git provider from the options below and follow the step-by-step installation guide to seamlessly set up your AI Code Review Agent.

We are excited to announce that one of our users has developed a dedicated Vim and Neovim plugin for Bito, integrating it seamlessly with your favorite code editor. This plugin enhances your coding experience by leveraging the power of Bito's AI capabilities directly within Vim and Neovim.

Installation

To get started with "vim-bitoai," follow these steps:

Step 1: Install Bito CLI

Step 2: Install the Plugin

Open your terminal and navigate to your Vim or Neovim plugin directory. Then, clone the "vim-bitoai" repository using the following command:

git clone https://github.com/zhenyangze/vim-bitoai.git

Step 3: Configure the Plugin

Open your Vim or Neovim configuration file and add the following lines:

" Vim Plug
Plug 'zhenyangze/vim-bitoai'

" NeoBundle
NewBundle 'zhenyangze/vim-bitoai'

" Vundle
Plugin 'zhenyangze/vim-bitoai'

Save the configuration file and restart your editor or run :source ~/.vimrc (for Vim) or :source ~/.config/nvim/init.vim (for Neovim) to load the changes.

Step 4: Verify the Installation

Open Vim or Neovim, and you should now have the "vim-bitoai" plugin installed and ready to use.

Usage

You can use its powerful features once you have installed the "vim-bitoai" plugin. Here are some of the available commands:

• BitoAiGenerate: Generates code based on a given prompt.

• BitoAiGenerateUnit: Generates unit test code for the selected code block.

• BitoAiGenerateComment: Generates comments for methods, explaining parameters and output.

• BitoAiCheck: Performs a check for potential issues in the code and suggests improvements.

• BitoAiCheckSecurity: Checks the code for security issues and provides recommendations.

• BitoAiCheckStyle: Checks the code for style issues and suggests style improvements.

• BitoAiCheckPerformance: Analyzes the code for performance issues and suggests optimizations.

• BitoAiReadable: Organizes the code to enhance readability and maintainability.

• BitoAiExplain: Generates an explanation for the selected code.

To execute a command, follow these steps:

1. Open a file in Vim or Neovim that you want to work on.

2. Select the code block you want to act on. You can use visual mode or manually specify the range using line numbers.

3. Execute the desired command by running the corresponding command in command mode. For example, to generate code based on a prompt, use the : BitoAiGenerate command. Note: Some commands may prompt you for additional information or options.

4. The plugin will communicate with the Bito CLI and execute the command, providing the output directly within your editor.

By leveraging the "vim-bitoai" plugin, you can directly harness the power of Bito's AI capabilities within your favorite Vim or Neovim editor. This integration lets you streamline your software development process, saving time and effort in repetitive tasks and promoting efficient coding practices.

Customization

The "vim-bitoai" plugin also offers customization options tailored to your specific needs. Here are a few variables you can configure in your Vim or Neovim configuration file:

• g:bito_buffer_name_prefix: Sets the prefix for the buffer name in the Bito history. By default, it is set to 'bito_history_'.

• g:vim_bito_path: Specifies the path to the Bito CLI executable. If the Bito CLI is not in your system's command path, you can provide the full path to the executable.

• g:vim_bito_prompt_{command}: Allows you to customize the prompt for a specific command. Replace {command} with the desired command.

To define a custom prompt, add the following line to your Vim or Neovim configuration file and replace your prompt with the desired prompt text:

if !exists("g:vim_bito_prompt_{command}")
    let g:vim_bito_prompt_{command}="your prompt"
endif

Remember to restart your editor or run the appropriate command to load the changes.

We encourage you to explore the "vim-bitoai" plugin and experience the benefits of seamless integration between Bito and your Vim or Neovim editor. Feel free to contribute to the repository or provide feedback to help us further improve this plugin and enhance your coding experience.

Video tutorial

Coming soon...

Installation and configuration steps

Follow the step-by-step instructions below to install the AI Code Review Agent using Bito Cloud:

Step 1: Log in to Bito

Step 2: Open the Code Review Agents setup

Step 3: Select your Git provider

Bito supports integration with the following Git providers:

• GitHub

• GitHub (Self-Managed)

• GitLab

• GitLab (Self-Managed)

• Bitbucket

Since we are setting up the Agent for GitHub, select GitHub to proceed.

Step 4: Install the Bito app for GitHub

To enable pull request reviews, you need to install and authorize the Bito’s AI Code Review Agent app.

Click the Install Bito App for GitHub button. This will redirect you to GitHub.

On GitHub, select where you want to install the app.

Grant Bito access to your repositories:

• Choose All repositories to enable Bito for every repository in your account.

• Or, select Only select repositories and pick specific repositories using the dropdown menu.

Click Install & Authorize to proceed. Once completed, you will be redirected to Bito.

Step 5: Enable AI Code Review Agent on repositories

After connecting Bito to your GitHub account, you need to enable the AI Code Review Agent for your repositories.

Click the Go to repository list button to view all repositories Bito can access in your GitHub account.

Use the toggles in the Code Review Status column to enable or disable the Agent for each repository.

Step 6: Automated and manual pull request reviews

Once a repository is enabled, you can invoke the AI Code Review Agent in the following ways:

1. Automated code review: By default, the Agent automatically reviews all new pull requests and provides detailed feedback.

2. Manually trigger code review: To initiate a manual review, simply type /review in the comment box on the pull request and submit it. This action will start the code review process.

The AI-generated code review feedback will be posted as comments directly within your pull request, making it seamless to view and address suggestions right where they matter most.

Step 7: Specialized commands for code reviews

Bito also offers specialized commands that are designed to provide detailed insights into specific areas of your source code, including security, performance, scalability, code structure, and optimization.

• /review security: Analyzes code to identify security vulnerabilities and ensure secure coding practices.

• /review performance: Evaluates code for performance issues, identifying slow or resource-heavy areas.

• /review scalability: Assesses the code's ability to handle increased usage and scale effectively.

• /review codeorg: Scans for readability and maintainability, promoting clear and efficient code organization.

• /review codeoptimize: Identifies optimization opportunities to enhance code efficiency and reduce resource usage.

By default, the /review command generates inline comments, meaning that code suggestions are inserted directly beneath the code diffs in each file. This approach provides a clearer view of the exact lines requiring improvement. However, if you prefer a code review in a single post rather than separate inline comments under the diffs, you can include the optional parameter: /review #inline_comment=False

Step 8: Chat with AI Code Review Agent

Ask questions directly to the AI Code Review Agent regarding its code review feedback. You can inquire about highlighted issues, request alternative solutions, or seek clarifications on suggested fixes.

To start the conversation, type your question in the comment box within the inline suggestions on your pull request, and then submit it. Typically, Bito AI responses are delivered in about 10 seconds. On GitHub and Bitbucket, you need to manually refresh the page to see the responses, while GitLab updates automatically.

Bito supports over 20 languages—including English, Hindi, Chinese, and Spanish—so you can interact with the AI in the language you’re most comfortable with.

Screenshots

Screenshot # 1

Screenshot # 2

Screenshot # 3

On your machine or in a Private Cloud, you can run the AI Code Review Agent via either CLI or webhooks service. This guide will teach you about the key differences between CLI and webhooks service and when to use each mode.

Difference Between CLI and webhooks service

The main difference between CLI and webhooks service lies in their operational approach and purpose. In CLI, the docker container is used for a one-time code review. This mode is ideal for isolated, single-instance analyses where a quick, direct review of the code is needed.

On the other hand, webhooks service is designed for continuous operation. When set in webhooks service mode, the AI Code Review Agent remains online and active at a specified URL. This continuous operation allows it to respond automatically whenever a pull request is opened in a repository. In this scenario, the git provider notifies the server, triggering the AI Code Review Agent to analyze the pull request and post its review as a comment directly on it.

When to Use CLI and When to Use webhooks service

Selecting the appropriate mode for code review with the AI Code Review Agent depends largely on the nature and frequency of your code review needs.

CLI: Ideal for Specific, One-Time Reviews

CLI mode is best suited for scenarios requiring immediate, one-time code reviews. It's particularly effective for:

• Conducting quick assessments of specific pull requests.

• Performing periodic, scheduled code analyses.

• Reviewing code in environments with limited or no continuous integration support.

• Integrating with batch processing scripts for ad-hoc analysis.

• Using in educational settings to demonstrate code review practices.

• Experimenting with different code review configurations.

• Reviewing code on local setups or for personal projects.

• Performing a final check before pushing code to a repository.

CLI mode stands out for its simplicity and is perfect for standalone tasks where a single, direct execution of the code review process is all that's needed.

Webhooks service: For Continuous, Automated Reviews

Webhooks service, on the other hand, is the go-to choice for continuous code review processes. It excels in:

• Continuously monitoring all pull requests in a repository.

• Providing instant feedback in collaborative projects.

• Seamlessly integrating with CI/CD pipelines for automated reviews.

• Performing automated code quality checks in team environments.

• Conducting real-time security scans on new pull requests.

• Ensuring adherence to coding standards in every pull request.

• Streamlining the code review process in large-scale projects.

• Maintaining consistency in code review across multiple projects.

• Enhancing workflows in remote or distributed development teams.

• Offering prompt feedback in agile development settings.

Webhooks service is indispensable in active development environments where consistent monitoring and immediate feedback are critical. It automates the code review process, integrating seamlessly into the workflow and eliminating the need for manual initiation of code reviews.

Video tutorial

coming soon...

Prerequisites

Before proceeding, ensure you've completed all necessary prerequisites.

1. Create a GitLab Personal Access Token:

For GitLab merge request code reviews, a token with api scope is required. Make sure that the token is created by a GitLab user who has the Maintainer access role.

2. Authorizing a GitLab Personal Access Token for use with SAML single sign-on:

If your GitLab organization enforces SAML Single Sign-On (SSO), you must authorize your Personal Access Token through your Identity Provider (IdP); otherwise, Bito's AI Code Review Agent won't function properly.

For more information, please refer to these GitLab documentation:

Installation and configuration steps

Follow the step-by-step instructions below to install the AI Code Review Agent using Bito Cloud:

Step 1: Log in to Bito

Step 2: Open the Code Review Agents setup

Step 3: Select your Git provider

Bito supports integration with the following Git providers:

• GitHub

• GitHub (Self-Managed)

• GitLab

• GitLab (Self-Managed)

• Bitbucket

Since we are setting up the Agent for GitLab (Self-Managed) server, select GitLab (Self-Managed) to proceed.

Step 4: Connect Bito to GitLab

To enable merge request reviews, you’ll need to connect your Bito workspace to your GitLab (Self-Managed) server.

You need to enter the details for the below mentioned input fields:

• Hosted GitLab URL: This is the domain portion of the URL where you GitLab Enterprise Server is hosted (e.g., https://yourcompany.gitlab.com). Please check with your GitLab administrator for the correct URL.

Click Validate to ensure the token is functioning properly.

If the token is successfully validated, you can select your GitLab Group from the dropdown menu.

Click Connect Bito to GitLab to proceed.

Step 5: Enable AI Code Review Agent on repositories

After connecting Bito to your GitLab self-managed server, you need to enable the AI Code Review Agent for your repositories.

Click the Go to repository list button to view all repositories Bito can access in your GitLab self-managed server.

Use the toggles in the Code Review Status column to enable or disable the Agent for each repository.

Step 6: Automated and manual merge request reviews

Once a repository is enabled, you can invoke the AI Code Review Agent in the following ways:

1. Automated code review: By default, the Agent automatically reviews all new merge requests and provides detailed feedback.

2. Manually trigger code review: To initiate a manual review, simply type /review in the comment box on the merge request and submit it. This action will start the code review process.

The AI-generated code review feedback will be posted as comments directly within your merge request, making it seamless to view and address suggestions right where they matter most.

Step 7: Specialized commands for code reviews

Bito also offers specialized commands that are designed to provide detailed insights into specific areas of your source code, including security, performance, scalability, code structure, and optimization.

• /review security: Analyzes code to identify security vulnerabilities and ensure secure coding practices.

• /review performance: Evaluates code for performance issues, identifying slow or resource-heavy areas.

• /review scalability: Assesses the code's ability to handle increased usage and scale effectively.

• /review codeorg: Scans for readability and maintainability, promoting clear and efficient code organization.

• /review codeoptimize: Identifies optimization opportunities to enhance code efficiency and reduce resource usage.

By default, the /review command generates inline comments, meaning that code suggestions are inserted directly beneath the code diffs in each file. This approach provides a clearer view of the exact lines requiring improvement. However, if you prefer a code review in a single post rather than separate inline comments under the diffs, you can include the optional parameter: /review #inline_comment=False

Step 8: Chat with AI Code Review Agent

Ask questions directly to the AI Code Review Agent regarding its code review feedback. You can inquire about highlighted issues, request alternative solutions, or seek clarifications on suggested fixes.

To start the conversation, type your question in the comment box within the inline suggestions on your merge request, and then submit it. Typically, Bito AI responses are delivered in about 10 seconds. On GitHub and Bitbucket, you need to manually refresh the page to see the responses, while GitLab updates automatically.

Bito supports over 20 languages—including English, Hindi, Chinese, and Spanish—so you can interact with the AI in the language you’re most comfortable with.

Screenshots

Screenshot # 1

Screenshot # 2

Screenshot # 3

Video tutorial

coming soon...

Prerequisites

Before proceeding, ensure you've completed all necessary prerequisites.

1. Create a GitHub Personal Access Token (classic):

For GitHub pull request code reviews, ensure you have a CLASSIC personal access token with repo scope. We do not support fine-grained tokens currently.

2. Authorizing a GitHub Personal Access Token for use with SAML single sign-on:

Installation and configuration steps

Follow the step-by-step instructions below to install the AI Code Review Agent using Bito Cloud:

Step 1: Log in to Bito

Step 2: Open the Code Review Agents setup

Step 3: Select your Git provider

Bito supports integration with the following Git providers:

• GitHub

• GitHub (Self-Managed)

• GitLab

• GitLab (Self-Managed)

• Bitbucket

Since we are setting up the Agent for self-managed GitHub Enterprise server, select GitHub (Self-Managed) to proceed.

Step 4: Register & install the Bito App for GitHub

To enable pull request reviews, you need to register and install the Bito’s AI Code Review Agent app on your self-managed GitHub Enterprise server.

You need to enter the details for the below mentioned input fields:

• Hosted GitHub URL: This is the domain portion of the URL where you GitHub Enterprise Server is hosted (e.g., https://yourcompany.github.com). Please check with your GitHub administrator for the correct URL.

Click Validate to ensure the login credentials are working correctly. If the credentials are successfully validated, click the Install Bito App for GitHub button. This will redirect you to your GitHub (Self-Managed) server.

Now select where you want to install the app:

• Choose All repositories to enable Bito for every repository in your account.

• Or, select Only select repositories and pick specific repositories using the dropdown menu.

Click Install & Authorize to proceed. Once completed, you will be redirected to Bito.

Step 5: Enable AI Code Review Agent on repositories

After connecting Bito to your self-managed GitHub Enterprise server, you need to enable the AI Code Review Agent for your repositories.

Click the Go to repository list button to view all repositories Bito can access in your self-managed GitHub Enterprise server.

Use the toggles in the Code Review Status column to enable or disable the Agent for each repository.

Step 6: Automated and manual pull request reviews

Once a repository is enabled, you can invoke the AI Code Review Agent in the following ways:

1. Automated code review: By default, the Agent automatically reviews all new pull requests and provides detailed feedback.

2. Manually trigger code review: To initiate a manual review, simply type /review in the comment box on the pull request and submit it. This action will start the code review process.

The AI-generated code review feedback will be posted as comments directly within your pull request, making it seamless to view and address suggestions right where they matter most.

Step 7: Specialized commands for code reviews

Bito also offers specialized commands that are designed to provide detailed insights into specific areas of your source code, including security, performance, scalability, code structure, and optimization.

• /review security: Analyzes code to identify security vulnerabilities and ensure secure coding practices.

• /review performance: Evaluates code for performance issues, identifying slow or resource-heavy areas.

• /review scalability: Assesses the code's ability to handle increased usage and scale effectively.

• /review codeorg: Scans for readability and maintainability, promoting clear and efficient code organization.

• /review codeoptimize: Identifies optimization opportunities to enhance code efficiency and reduce resource usage.

By default, the /review command generates inline comments, meaning that code suggestions are inserted directly beneath the code diffs in each file. This approach provides a clearer view of the exact lines requiring improvement. However, if you prefer a code review in a single post rather than separate inline comments under the diffs, you can include the optional parameter: /review #inline_comment=False

Step 8: Chat with AI Code Review Agent

Ask questions directly to the AI Code Review Agent regarding its code review feedback. You can inquire about highlighted issues, request alternative solutions, or seek clarifications on suggested fixes.

To start the conversation, type your question in the comment box within the inline suggestions on your pull request, and then submit it. Typically, Bito AI responses are delivered in about 10 seconds. On GitHub and Bitbucket, you need to manually refresh the page to see the responses, while GitLab updates automatically.

Bito supports over 20 languages—including English, Hindi, Chinese, and Spanish—so you can interact with the AI in the language you’re most comfortable with.

Screenshots

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Installation and Configuration Steps

3. You can integrate the AI Code Review Agent into your CI/CD pipeline in two ways, depending on your preference:

• Option 1: Using the bito_action.properties File

  • Configure the following properties in the bito_action.properties file located in the downloaded bito-action-script folder.

• Run the following command:

  • ./bito_actions.sh bito_action.properties

  • Note: When using the properties file, make sure to provide all the three parameters in .properties file

• Option 2: Using Runtime Values

  • Provide all necessary values directly on the command line:

    • ./bito_actions.sh agent_instance_url=<agent_instance_url> agent_instance_secret=<secret> pr_url=<pr_url>

    • Replace <agent_instance_url>, <secret>, and <pr_url> with your specific values.

  • Note: You can also override the values given in the .properties file or provide values that are not included in the file. For example, you can configure agent_instance_url and agent_instance_secret in the bito_action.properties file, and only pass pr_url on the command line during runtime.

    • ./bito_actions.sh bito_action.properties pr_url=<pr_url>

    • Replace <pr_url> with your specific values.

1. Incorporate the AI Code Review Agent into your CI/CD pipeline by adding the appropriate commands to your build or deployment scripts. This integration will automatically trigger code reviews as part of the pipeline, enhancing your development workflow by enforcing code quality checks with every change.

Video tutorial

Coming soon...

Prerequisites

Before proceeding, ensure you've completed all necessary prerequisites.

1. Create a Bitbucket App Password:

For Bitbucket pull request code reviews, you’ll need to connect your Bito workspace to your Bitbucket account.

Ensure the required permissions are checked:

• Under Account, select Read.

• Under Pull requests, select Write.

• Under Webhooks, select Read and write.

Installation and configuration steps

Follow the step-by-step instructions below to install the AI Code Review Agent using Bito Cloud:

Step 1: Log in to Bito

Step 2: Open the Code Review Agents setup

Step 3: Select your Git provider

Bito supports integration with the following Git providers:

• GitHub

• GitHub (Self-Managed)

• GitLab

• GitLab (Self-Managed)

• Bitbucket

Since we are setting up the Agent for Bitbucket, select Bitbucket to proceed.

Step 4: Connect Bito to Bitbucket

To enable pull request reviews, you’ll need to connect your Bito workspace to your Bitbucket account.

Ensure the required permissions are checked:

• Under Account, select Read.

• Under Pull requests, select Write.

• Under Webhooks, select Read and write.

Once generated, enter your Bitbucket username and App password into the input fields in Bito.

Click Authorize to ensure the login credentials are working correctly.

If the credentials are successfully authorized, you can select your Bitbucket workspace from the dropdown menu.

Click Connect Bito to Bitbucket to proceed.

Step 5: Enable AI Code Review Agent on repositories

After connecting Bito to your Bitbucket account, you need to enable the AI Code Review Agent for your repositories.

Click the Go to repository list button to view all repositories Bito can access in your Bitbucket account.

Use the toggles in the Code Review Status column to enable or disable the Agent for each repository.

Step 6: Automated and manual pull request reviews

Once a repository is enabled, you can invoke the AI Code Review Agent in the following ways:

1. Automated code review: By default, the Agent automatically reviews all new pull requests and provides detailed feedback.

2. Manually trigger code review: To initiate a manual review, simply type /review in the comment box on the pull request and click Add comment now to submit it. This action will start the code review process.

The AI-generated code review feedback will be posted as comments directly within your pull request, making it seamless to view and address suggestions right where they matter most.

Step 7: Specialized commands for code reviews

Bito also offers specialized commands that are designed to provide detailed insights into specific areas of your source code, including security, performance, scalability, code structure, and optimization.

• /review security: Analyzes code to identify security vulnerabilities and ensure secure coding practices.

• /review performance: Evaluates code for performance issues, identifying slow or resource-heavy areas.

• /review scalability: Assesses the code's ability to handle increased usage and scale effectively.

• /review codeorg: Scans for readability and maintainability, promoting clear and efficient code organization.

• /review codeoptimize: Identifies optimization opportunities to enhance code efficiency and reduce resource usage.

By default, the /review command generates inline comments, meaning that code suggestions are inserted directly beneath the code diffs in each file. This approach provides a clearer view of the exact lines requiring improvement. However, if you prefer a code review in a single post rather than separate inline comments under the diffs, you can include the optional parameter: /review #inline_comment=False

Step 8: Chat with AI Code Review Agent

Ask questions directly to the AI Code Review Agent regarding its code review feedback. You can inquire about highlighted issues, request alternative solutions, or seek clarifications on suggested fixes.

To start the conversation, type your question in the comment box within the inline suggestions on your pull request, and then submit it. Typically, Bito AI responses are delivered in about 10 seconds. On GitHub and Bitbucket, you need to manually refresh the page to see the responses, while GitLab updates automatically.

Bito supports over 20 languages—including English, Hindi, Chinese, and Spanish—so you can interact with the AI in the language you’re most comfortable with.

Screenshots

Screenshot # 1

Screenshot # 2

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While the Default Agent is ready for use right away, Bito also gives you the option to create new Agent instances or customize existing ones to suit your specific requirements. This flexibility ensures that the Agent can adapt to a range of workflows and project needs.

For example, you might configure one Agent to disable automatic code reviews for certain repositories, another to exclude specific Git branches from review, and yet another to filter out particular files or folders.

This guide will walk you through how to create or customize an Agent instance, unlocking its full potential to streamline your code reviews.

Creating or customizing AI Code Review Agents

Once Bito is connected to your GitHub/GitLab/Bitbucket account, you can easily create a new Agent or customize an existing one to suit your workflow.

Once you have selected an Agent to customize, you can modify its settings in the following areas:

1. General settings

Agent name

Assign a unique alphanumeric name to your Agent. This name acts as an identifier and allows you to invoke the Agent in supported clients using the @<agent_name> command.

Agent description

Provide a concise description of the Agent's purpose, such as the use case or project it will support. This makes it easier to manage multiple Agents.

2. Customization options

Bito provides three tabs for in-depth Agent customization:

a. Review

In this tab, you can configure how and when the Agent performs reviews:

• Automatic review: Toggle to enable or disable automatic reviews when a pull request is created and ready for review.

• Automatic incremental review: Toggle to enable or disable reviews for new commits added to a pull request. Only changes since the last review are assessed.

• Batch time (hours): Set the wait time (0 to 24 hours) for batching new commits before triggering a review. Lower values result in more frequent incremental reviews.

• Draft pull requests: By default, the Agent excludes draft pull requests from automated reviews. Disable this toggle to include drafts.

• Automatic summary: Toggle to enable automatic generation of AI summaries for changes, which are appended to the pull request description.

• Change Walkthrough: Enable this option to generate a table of changes and associated files, posted as a comment on the pull request.

b. Filters

Use filters to exclude specific pull requests or files from automated workflows:

• Files and folders: A list of files/folders that the AI Code Review Agent will not review if they are present in the diff. You can specify the files/folders to exclude from the review by name or glob/regex pattern. The Agent will automatically skip any files or folders that match the exclusion list. This filter applies to both manual reviews initiated through the /review command and automated reviews.

• Source or Target branch: This filter allows users to skip automated reviews for pull requests based on the source or target branch. It is useful in scenarios where automated reviews are unnecessary or could potentially disrupt the workflow. This filter applies only to automatically triggered reviews. Users should still be able to trigger reviews manually via the /review command.

c. Tools

Enhance the Agent’s reviews by enabling additional tools for static analysis, security checks, and secret detection:

• Secret Scanner: Enable this tool to detect and report secrets left in code changes.

3. Select repositories for code review

1. Click Select repositories to choose Git repositories for the Agent.

1. To enable code review for a specific repository, simply select its corresponding checkbox. You can also enable repositories later, after the Agent has been created. Once done, click Save and continue to save the new Agent configuration.

Video tutorial

Coming soon...

Prerequisites

Before proceeding, ensure you've completed all necessary prerequisites.

1. Create a GitLab Personal Access Token:

For GitLab merge request code reviews, a token with api scope is required. Make sure that the token is created by a GitLab user who has the Maintainer access role.

2. Authorizing a GitLab Personal Access Token for use with SAML single sign-on:

If your GitLab organization enforces SAML Single Sign-On (SSO), you must authorize your Personal Access Token through your Identity Provider (IdP); otherwise, Bito's AI Code Review Agent won't function properly.

For more information, please refer to these GitLab documentation:

Installation and configuration steps

Follow the step-by-step instructions below to install the AI Code Review Agent using Bito Cloud:

Step 1: Log in to Bito

Step 2: Open the Code Review Agents setup

Step 3: Select your Git provider

Bito supports integration with the following Git providers:

• GitHub

• GitHub (Self-Managed)

• GitLab

• GitLab (Self-Managed)

• Bitbucket

Since we are setting up the Agent for GitLab, select GitLab to proceed.

Step 4: Connect Bito to GitLab

To enable merge request reviews, you’ll need to connect your Bito workspace to your GitLab account.

You can either connect using OAuth (recommended) for a seamless, one-click setup or manually enter your Personal Access Token.

To connect via OAuth, simply click the Connect with OAuth (Recommended) button. This will redirect you to the GitLab website, where you'll need to log in. Once authenticated, you'll be redirected back to Bito, confirming a successful connection.

If you prefer not to use OAuth, you can connect manually using a Personal Access Token.

Once generated, click the Alternatively, use Personal or Group Access Token button.

Now, enter the token into the Personal Access Token input field in Bito.

Click Validate to ensure the token is functioning properly.

If you've successfully connected via OAuth or manually validated your token, you can select your GitLab Group from the dropdown menu.

Click Connect Bito to GitLab to proceed.

Step 5: Enable AI Code Review Agent on repositories

After connecting Bito to your GitLab account, you need to enable the AI Code Review Agent for your repositories.

Click the Go to repository list button to view all repositories Bito can access in your GitLab account.

Use the toggles in the Code Review Status column to enable or disable the Agent for each repository.

Step 6: Automated and manual merge request reviews

Once a repository is enabled, you can invoke the AI Code Review Agent in the following ways:

1. Automated code review: By default, the Agent automatically reviews all new merge requests and provides detailed feedback.

2. Manually trigger code review: To initiate a manual review, simply type /review in the comment box on the merge request and submit it. This action will start the code review process.

The AI-generated code review feedback will be posted as comments directly within your merge request, making it seamless to view and address suggestions right where they matter most.

Step 7: Specialized commands for code reviews

Bito also offers specialized commands that are designed to provide detailed insights into specific areas of your source code, including security, performance, scalability, code structure, and optimization.

• /review security: Analyzes code to identify security vulnerabilities and ensure secure coding practices.

• /review performance: Evaluates code for performance issues, identifying slow or resource-heavy areas.

• /review scalability: Assesses the code's ability to handle increased usage and scale effectively.

• /review codeorg: Scans for readability and maintainability, promoting clear and efficient code organization.

• /review codeoptimize: Identifies optimization opportunities to enhance code efficiency and reduce resource usage.

By default, the /review command generates inline comments, meaning that code suggestions are inserted directly beneath the code diffs in each file. This approach provides a clearer view of the exact lines requiring improvement. However, if you prefer a code review in a single post rather than separate inline comments under the diffs, you can include the optional parameter: /review #inline_comment=False

Step 8: Chat with AI Code Review Agent

Ask questions directly to the AI Code Review Agent regarding its code review feedback. You can inquire about highlighted issues, request alternative solutions, or seek clarifications on suggested fixes.

To start the conversation, type your question in the comment box within the inline suggestions on your merge request, and then submit it. Typically, Bito AI responses are delivered in about 10 seconds. On GitHub and Bitbucket, you need to manually refresh the page to see the responses, while GitLab updates automatically.

Bito supports over 20 languages—including English, Hindi, Chinese, and Spanish—so you can interact with the AI in the language you’re most comfortable with.

Screenshots

Screenshot # 1

Screenshot # 2

Screenshot # 3

Follow the steps below to get started:

1. If your Bito workspace is connected to your GitHub/GitLab/Bitbucket account, a list of AI Code Review Agent instances configured in your workspace will appear. Locate the instance you wish to duplicate and click the Clone button given in front of it.

1. An Agent configuration form will open, pre-populated with the input field values. You can edit these values as needed.

1. Click Select repositories to choose Git repositories for the new Agent.

1. To enable code review for a specific repository, simply select its corresponding checkbox. You can also enable repositories later, after the Agent has been created. Once done, click Save and continue to save the new Agent configuration.

The self-hosted AI Code Review Agent offers a more private and customizable option for teams looking to enhance their code review processes within their own infrastructure, while maintaining complete control over their data. This approach is ideal for organizations with specific compliance, security, or customization requirements.

Understanding CLI vs webhooks service

When setting up the AI Code Review Agent, you have the flexibility to choose between two primary modes of operation: CLI and webhooks service.

• CLI allows developers to manually initiate code reviews directly from terminal. This mode is ideal for quick, on-demand code reviews without the need for continuous monitoring or integration.

• Webhooks service transforms the Agent into a persistent service that automatically triggers code reviews based on specific events, such as pull requests or comments on pull requests. This mode is suitable for teams looking to automate their code review processes.

Deployment Options

Based on your needs and the desired integration level with your development workflow, choose one of the following options to install and run the AI Code Review Agent:

2. Start Docker: Ensure Docker is running on your machine.

4. Extract and Navigate:

• Extract the downloaded .zip file to a preferred location.

• Navigate to the extracted folder and then to the “cra-scripts” subfolder.

• Note the full path to the “cra-scripts” folder for later use.

1. Open Command Line:

   • Use Bash for Linux and macOS.

   • Use PowerShell for Windows.

2. Set Directory:

   • Change the current directory in Bash/PowerShell to the “cra-scripts” folder.

   • Example command: cd [Path to cra-scripts folder]

   • Adjust the path based on your extraction location.

1. Configure Properties:

   • Set mandatory properties:

     • mode = cli

     • pr_url

     • bito_cli.bito.access_key

     • git.provider

     • git.access_token

• Optional properties (can be skipped or set as needed):

  • git.domain

  • code_feedback

  • static_analysis

  • dependency_check

  • dependency_check.snyk_auth_token

  • review_scope

  • exclude_branches

  • exclude_files

  • exclude_draft_pr

1. Run the Agent:

   • On Linux/macOS in Bash: Run ./bito-cra.sh bito-cra.properties

   • On Windows in PowerShell: Run ./bito-cra.ps1 bito-cra.properties

1. Final Steps:

   • The script may prompt values of mandatory/optional properties if they are not preconfigured.

   • Upon completion, a code review comment is automatically posted on the Pull Request specified in the pr_url property.

Screenshots

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Minimum System Requirements

A machine with the following minimum specifications is recommended for Docker image deployment and for obtaining optimal performance of the AI Code Review Agent.

Supported Operating Systems

• Windows

• Linux

• macOS

OS Prerequisites

Required Access Tokens

2. If your Bito workspace is connected to your GitHub/GitLab/Bitbucket account, a list of AI Code Review Agent instances configured in your workspace will appear.

1. Before deleting an Agent, ensure that any repositories currently using it are reassigned to another Agent otherwise a warning popup will appear.

1. Locate the Agent you wish to delete and click the Delete button given in front of it.

/review

This command provides a broad overview of your code changes, offering suggestions for improvement across various aspects but without diving deep for secure coding or performance optimizations or scalability improvements etc. This makes it ideal for catching general code quality issues that might not necessarily be critical blockers but can enhance readability, maintainability, and overall code health.

Think of it as a first-pass review to identify potential areas for improvement before delving into more specialized analyses.

Review Scope

Five specialized commands are available to perform detailed analyses on specific aspects of your code. Details for each command are given below.

1. /review security

2. /review performance

3. /review scalability

4. /review codeorg

5. /review codeoptimize

/review security

This command performs an in-depth analysis of your code to identify vulnerabilities that could allow attackers to steal data, gain unauthorized access, or disrupt your application. This includes checking for weaknesses in input validation, output encoding, authentication, authorization, and session management. It also looks for proper encryption of sensitive data, secure coding practices, and potential misconfigurations that could expose your system.

/review performance

This command evaluates the current performance of the code by pinpointing slow or resource-intensive areas and identifying potential bottlenecks. It helps developers understand where the code may be underperforming against expected benchmarks or standards. It is particularly useful for identifying slow processes that could benefit from further investigation and refinement.

This includes checking how well your code accesses data and manages tasks like database interactions and memory usage.

/review scalability

This command analyzes your code to identify potential roadblocks to handling increased usage or data. It checks how well the codebase supports horizontal scaling and whether it is compatible with load balancing strategies. It also ensures the code can handle concurrent requests efficiently and avoids bottlenecks from single points of failure. The command further examines error handling and retry mechanisms to promote system resilience under pressure.

/review codeorg

This command scans your code for readability, maintainability, and overall clarity. This includes checking for consistent formatting, clear comments, well-defined functions, and efficient use of data structures. It also looks for opportunities to reduce code duplication, improve error handling, and ensure the code is written for future growth and maintainability.

/review codeoptimize

This command helps identify specific parts of the code that can be made more efficient through optimization techniques. It suggests refactoring opportunities, algorithmic improvements, and areas where resource usage can be minimized. This command is essential for enhancing the overall efficiency of the code, making it faster and less resource-heavy.

Display Code Review in a Single Post