The AI Code Review Agent supports two deployment options:

• Bito Cloud (fully managed)

• Self-hosted service (run on your own infrastructure)

Each option comes with its own set of benefits and considerations.

This guide walks you through both options to help you determine which deployment model best fits your team’s needs.

Bito Cloud

provides a managed environment for running the AI Code Review Agent, offering a seamless, hassle-free experience. This option is ideal for teams looking for quick deployment and minimal operational overhead.

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

AI Code Review Agent offers a higher degree of control and customization, suited for organizations with specific requirements or those who prefer to manage their own infrastructure.

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.

Coming soon...

Bito is an AI-powered code review tool that helps you catch bugs, security vulnerabilities, code smells, and other issues in your pull requests and code editors. By understanding your entire codebase, Bito provides context-aware, actionable suggestions that improve code quality and security.

It includes real-time recommendations from dev tools you already use such as static code analysis, open source vulnerability scanners, linters, and secrets scanning tools (e.g., passwords, API keys, sensitive information, etc.).

Supported platforms

AI Code Reviews in Git: GitHub, GitLab, Bitbucket

AI Code Reviews in IDE: VS Code, Cursor, Windsurf, JetBrains

See AI Code Review Agent in action

Quickstart guide

Helpful resources

Feature guides

Video library

Need help?

If you have any questions, feel free to email us at

This document explains some of Bito's privacy and security practices. Our outlines our various accreditations (SOC 2 Type II) and our various security policies. You can read our full Privacy Policy at .

Bito AI

Security is top of mind at Bito, especially when it comes to your code. A fundamental approach we have taken is we do not store any code, code snippets, indexes or embedding vectors on Bito’s servers unless you expressly allow it. You decide where you want to store your code, either locally on your machine, in your cloud, or on Bito’s cloud. Importantly, our AI partners do not store any of this information.

All requests are transmitted over HTTPS and are fully encrypted.

Coming soon....

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.

For more details, visit the page.

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:

1. : Ideal for developers seeking a simple, interactive way to conduct code reviews from the command line.

2. : Perfect for teams looking to automate code reviews through external events, enhancing their CI/CD workflow.

3. : A great option for GitHub users to seamlessly integrate automated code reviews into their GitHub Actions workflows.

offers a single-click solution for using the , eliminating the need for any downloads on your machine. You can create multiple instances of the Agent, allowing each to be used with a different repository on a Git provider such as GitHub, GitLab, or Bitbucket.

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

lets you integrate the into your CI/CD pipeline for automated code reviews. This document provides a step-by-step guide to help you configure and run the script successfully.

Installation and Configuration Steps

1. based on your Git provider, and follow the step-by-step instructions to install the AI Code Review Agent

You can try asking any question you may have in mind regarding your codebase. In most cases, Bito will give you an accurate answer. Bito uses AI to determine if you are asking about something in your codebase.

However, if you want to ask a question about your code no matter what, then you can use our pre-defined keywords such as "my code", "my repo", "my project", "my workspace", etc., in your question.

The complete list of these keywords is given on our page.

Here are some popular use cases (with example questions):

Keeping your Bito plugin up to date ensures you have access to the latest features and improvements. In this article, we will guide you through the steps to update the Bito plugin on both VS Code and JetBrains IDEs. Let's dive in!

Updating Bito Plugin on VS Code

1. Open your VS Code IDE

2. Navigate to the Extensions view by clicking on the square icon in the left sidebar

Here is the list of keywords in different languages to ask questions regarding your entire codebase. Use any of these keywords in your prompts inside Bito chatbox.

English:

• my code

When you open a project in Visual Studio Code or JetBrains IDEs, Bito lets you enable the of code files from that project’s folder. Basically, this indexing mechanism leverages our new that enables Bito to understand your entire codebase and answer any questions regarding it.

The index is stored locally on your system to provide better performance while maintaining the security/privacy of your private code.

Enabling unicode For Windows 10 and below

Unicode characters (using other languages) might not be readily supported on command prompt if you are on Windows 10 or below. You can run command chcp 936 in cmd prior to using bito to support unicode characters in Windows 10 or below.

Bito automatically saves the chat session History. The session history is stored locally on your computer. You can return to any chat session and continue the AI conversation from where you left off. Bito will automatically maintain and restore the memory of the loaded chat session.

You can "Delete" any saved chat session or share a permalink to the session with your coworkers.

Here is the video overview of accessing and managing the session history.

Parameters are the individual elements of a Large Language Model that are learned from the training data. Think of them as the synapses in a human brain—tiny connections that store learned information.

How Parameters Work in LLMs

Each parameter in an LLM holds a tiny piece of information about the language patterns the model has seen during training. They are the fundamental elements that determine the behavior of the model when it generates text.

For example, imagine teaching a child what a cat is by showing them pictures of different cats. Each picture tweaks the child's understanding and definition of a cat. In LLMs, each training example tweaks the parameters to better understand and generate language.

The Role of Parameters in Understanding and Generating Language

Parameters are crucial because they allow the model to perform tasks such as translation, write articles, and even generate source code. When you ask an AI a question, the parameters work together to sift through the learned patterns and generate a response that makes sense based on the training it received.

For instance, if you ask an AI to write a poem, the parameters will determine how to structure the poem, what words to use, and how to create rhyme or rhythm, all based on the data it was trained on.

The Scale of LLM Parameters: Just How Large Are We Talking?

When we say "Large" in LLM, we're not kidding. The size of a language model is directly related to the number of parameters it has.

Take GPT-4, for example, with its 1.76 trillion parameters. That's like 1.76 trillion different dials the model can tweak to get language just right. Each parameter holds a piece of information that can contribute to understanding a sentence's structure, the meaning of a word, or even the tone of a text.

Earlier models had significantly fewer parameters. GPT-1, for instance, had only 117 million parameters. With each new generation, the number of parameters has grown exponentially, leading to more sophisticated and nuanced language generation.

Training LLMs: How Parameters Learn

Training an LLM involves a process called "backpropagation" where the model makes predictions, checks how far off it is, and adjusts the parameters accordingly.

Let's say we're training an LLM to recognize the sentiment of a sentence. We show it the sentence "I love sunny days!" tagged as positive sentiment. The LLM predicts positive but isn't very confident. During backpropagation, it adjusts the parameters to increase the confidence for future similar sentences.

This process is repeated millions of times with millions of examples, gradually fine-tuning the parameters so that the model's predictions become more accurate over time.

Parameter’s Impact on AI Performance and Limitations

The number of parameters is one of the key factors influencing an AI model's performance. However, more parameters can mean a model requires more computational power and data to train effectively, which can lead to increased costs and longer training times.

With great power comes great responsibility—and greater chances of making mistakes. More parameters can sometimes mean that the model starts seeing patterns where there aren't any, a phenomenon known as "overfitting" where the model performs well on training data but poorly on new, unseen data.

The Future of Parameters in LLMs

The future of LLMs might not just be about adding more parameters, but also about making better use of them. Innovations in how parameters are structured and how they learn are ongoing.

AI researchers are exploring ways to make LLMs more parameter-efficient, meaning they can achieve the same or better performance with fewer parameters. Techniques like "parameter sharing" and "sparse activation" are part of this cutting-edge research.

Conclusion

Parameters in LLMs are the core elements that allow these models to understand and generate human-like text. While the sheer number of parameters can be overwhelming, it's their intricate training and fine-tuning that empower AI to interact with us in increasingly complex ways.

As AI continues to evolve, the focus is shifting from simply ramping up parameters to refining how they're used, ensuring that the future of AI is not just smarter but also more efficient and accessible.

Bito’s AI Code Review Agent is the first agent built with Bito’s AI Agent framework and engine. It is an automated AI assistant (powered by Anthropic’s Claude Sonnet 3.7) that will review your team’s code; it spots bugs, issues, code smells, and security vulnerabilities in Pull/Merge Requests (PR/MR) and provides high-quality suggestions to fix them.

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.

It ensures a secure and confidential experience without compromising on reliability. Bito neither reads nor stores your code, and none of your code is used for AI model training. Learn more about our .

By accessing Bito's feature, the AI Code Review Agent can analyze relevant context from your entire repository, providing better context-aware analysis and suggestions. This tailored approach ensures a more personalized and contextually relevant code review experience.

To comprehend your code and its dependencies, we use Symbol Indexing, Abstract Syntax Trees (AST), and Embeddings. Each step feeds into the next, starting from locating specific code snippets with Symbol Indexing, getting their structural context with AST parsing, and then leveraging embedding vectors for broader semantic insights. This approach ensures a detailed understanding of the code's functionality and its dependencies. For more information, see

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

The Free Plan offers AI-generated pull request summaries to provide a quick overview of changes. For advanced features like line-level code suggestions, consider upgrading to the Team Plan. For detailed pricing information, visit our page.

Learn more

bito config [flags]

• run bito config -l or bito config --list to list all config variables and values.

• run bito config -e or bito config --edit to open the config file in default editor.

Sample Configuration

What is an Access Key and How to Get it?

is an alternate authentication mechanism to Email & OTP based authentication. You can use an Access Key in Bito CLI to access various functionalities such as Bito AI Chat. Here’s a guide on . Basically, after creating the Access Key, you have to use it in the config file mentioned above. For example, access_key: “YOUR_ACCESS_KEY_HERE”

Access Key can be persisted in Bito CLI by adding it in the config file using bito config -e. Such persisted Access Key can be over-ridden by running bito -k <access-key> or bito --key <access-key> for the transient session (sessions that last only for a short time).

Preferred AI Model Type

By default AI Model Type is set to ADVANCED and it can be overridden by running bito -m <BASIC/ADVANCED>. Model type is used for AI query in the current session. Model type can be set to BASIC or ADVANCED, which is case insensitive.

"ADVANCED" refers to AI models like GPT-4o, Claude Sonnet 3.5, and best in class AI models, while "BASIC" refers to AI models like GPT-4o mini and similar models.

When using Basic AI models, your prompts and the chat's memory are limited to 40,000 characters (about 18 single-spaced pages). However, with Advanced AI models, your prompts and the chat memory can go up to 240,000 characters (about 110 single-spaced pages). This means that Advanced models can process your entire code files, leading to more accurate answers.

If you are seeking the best results for complex tasks, then choose Advanced AI models.

To see how many Advanced AI requests you have left, please visit the page. On this page, you can also set to control usage of Advanced AI model requests for your workspace and avoid unexpected expenses.

Also note that even if you have set preferred_ai_model: ADVANCED in Bito CLI config but your Advanced AI model requests quota is finished (or your self-imposed is reached) then Bito CLI will start using Basic AI models instead of Advanced AI models.

Prerequisites

Terminal

• Bash (for Mac and Linux)

• CMD (for Windows)

Using Bito CLI

Before you can use Bito CLI, you need to and it. Once the setup is done, follow the steps below:

• Execute Chat: Run bito command on command prompt to get started. Ask anything you want help with such as awk command to print first and last column.

• Note: Bito CLI supports long prompts through multiline input. To complete and submit the prompt, press Ctrl+D. Enter/Return key adds a new line to the input.

Getting Started

Check out the video below to get started with Bito CLI.

Examples

Here are two examples for you to see My Prompt in action:

1. How to Create Git Commit Messages and Markdown Documentation with Ease using Bito CLI My Prompt:

1. How to generate test data using Bito CLI My Prompt:

Bito CLI (Command Line Interface) is an innovative tool that harnesses the power of Bito AI chat functionality to automate software development workflows. It can automate repetitive tasks like software documentation, test case generation, pull request review, release notes generation, writing commit message or pull request description, and much more.

For example, you can run a command like bito –p writedocprompt.txt -f mycode.js for non-interactive mode in Bito CLI (where writedocprompt.txt will contain your prompt text such as Explain the code below in brief and mycode.js will contain the actual code on which the action is to be performed).

Download Bito CLI from GitHub:

With support for 50+ programming languages (Python, JavaScript, SQL, etc.) and 50+ spoken languages (English, German, Chinese, etc.), Bito CLI is versatile and adaptable to different project needs. Furthermore, it's designed to be compatible across multiple operating systems, including Windows, Mac, and Linux, ensuring a wide range of usability.

You can either use "ADVANCED" AI models like GPT-4o, Claude Sonnet 3.5, and best in class AI models, or "BASIC" AI models like GPT-4o mini and similar models inside Bito CLI.

When using Basic AI models, your prompts and the chat's memory are limited to 40,000 characters (about 18 single-spaced pages). However, with Advanced AI models, your prompts and the chat memory can go up to 240,000 characters (about 110 single-spaced pages). This means that Advanced models can process your entire code files, leading to more accurate answers.

If you are seeking the best results for complex tasks, then choose Advanced AI models.

Bito CLI is an invaluable asset for developers looking to increase efficiency and productivity in their workflows. It allows developers to save time and focus on more complex and creative aspects of their work. Additionally, Bito CLI plays a crucial role in supporting continuous integration and deployment (CI/CD) processes. Explore some we've created using Bito CLI, which you can implement in your projects right now. These automations showcase the powerful capabilities of Bito CLI.

To get started, check out our guide on , ensuring you make the most out of it.

Easily share insights from any AI Chat session by creating a unique shareable link directly from the Bito extension in VS Code or JetBrains IDEs.

Whether you need to share AI-generated code suggestions, explanations, or any other chat insights, this feature allows you to create a public link that others can access. The link will remain active for 15 days and can be viewed by anyone with access to the URL, making collaboration and knowledge sharing seamless.

Additionally, you can quickly share your AI Chat session through a pre-written Tweet or an Email.

Let's see how it is done:

1. Open Bito in Visual Studio Code or any JetBrains IDE.

2. Start a conversation in Bito’s AI Chat user interface.

3. Locate the share button on the top right of the Bito extension side-panel.

4. Click the share button to open a menu with options, including X (Twitter), Email, and Link.

The IDE customization settings are accessible through the new toolbar dropdown menu titled "Extension Settings".

Light and Dark Themes

In Visual Studio Code and JetBrains IDEs, you can choose between a light or dark theme for the Bito panel to match your coding environment preference. For VS Code users, Bito also offers an adaptive theme mode in which the Bito panel and font colors automatically adjust based on your selected VS Code theme, creating a seamless visual experience.

You can set the desired theme through the Theme dropdown.

Theme Screenshots

“Always Light” Theme

“Always Dark” Theme

“Light” or “Dark” Theme - Matching IDE

“Adaptive” Theme

Theme adapted from “Noctis Lux”:

Theme adapted from “Solarized Light”:

Theme adapted from “Tomorrow Night Blue”:

Theme adapted from “barn-cat”:

Font Size Control

Take control of your code readability! Within the Bito extension settings, you can now adjust the font size for a comfortable viewing experience.

You can set the desired font size through the Font Size text field. However, if you check the Font Size (Match with IDE Font) checkbox, it will override the set font size with the Editor font size.

Bito deploys a Vector Database locally on the user’s machine, bundled as part of the Bito IDE plug-in. This database uses Embeddings (a vector with over 1,000 dimensions) to retrieve text, function names, objects, etc. from the codebase and then transform them into multi-dimensional vector space.

Then when you give it a function name or ask it a question, that query is converted into a vector and is compared to other vectors nearby. This returns the relevant search results. So, it's a way to perform search not on keywords, but on meaning. Vector Databases are able to do this kind of search very quickly.

Bito also uses an Agent Selection Framework that acts like an autonomous entity capable of perceiving its environment, making decisions, and taking actions to achieve certain goals. It figures out if it’s necessary to do an embeddings comparison on your codebase, do we need to perform an action against Jira, or do we do something else.

Finally, Bito utilizes from Open AI, Anthropic, and others that actually provide the answer to the question by leveraging the context provided by the Agent Selection Framework and the embeddings.

This is what makes us stand out from other AI tools like ChatGPT, GitHub Copilot, etc. that do not understand your entire codebase.

We’re making significant innovations in our to simplify coding for everyone. To learn more about this head over to .

If you no longer need an AI Code Review Agent instance, you can delete it to keep your workspace organized. Follow the steps below to quickly remove any unused Agents.

1. Log in to Bito Cloud and select a workspace to get started.

2. From the left sidebar, select Code Review Agents.

   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.

Save time and effort by quickly creating a new AI Code Review Agent instance using the configuration settings of an existing one. It’s a fast and simple way to set up multiple Agent instances without having to reconfigure each one.

Follow the steps below to get started:

1. Log in to Bito Cloud and select a workspace to get started.

2. From the left sidebar, select Code Review Agents.

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.

1. When you save the configuration, your new Agent instance will be added and available on the page.

You would need to create an account with your email to use Bito. You can sign up for Bito directly from the IDE extension or the Bito web interface at https://alpha.bito.ai/.

1. After you install the Bito extension, click the "Sign up or Sign-in" button on the Bito sign-up flow screen.

1. In the next screen, enter your work email address, and verify through a six-digit code sent to your email address.

1. Once your email is verified, you will get an option to create your profile. Enter your full name and set the language for the AI setup. Bito uses this setting to generate the output regardless of prompt language.

Now, let's learn to start using Bito.

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.

The Bito free trial gives you access to premium features for 14 days, allowing you to experience the full capabilities of Bito's AI-powered coding assistant.

You can start your free trial directly from the Bito IDE extension using any of the three methods below.

How to start your free trial

Bito’s Request changes comments feature helps enforce code quality by blocking merges until all AI-generated review comments are resolved—fully supported in GitHub, GitLab, and Bitbucket.

When enabled, Bito identifies actionable issues in pull requests and posts them as formal “Request changes” review comments. If your repository uses branch protection rules that require all review conversations to be resolved before merging, Bito’s flagged comments will automatically block the pull request until addressed.

This ensures developers don’t accidentally merge incomplete or unreviewed code.

How it works

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.

How to chat?

Large Language Models (LLMs) are advanced AI algorithms trained to understand, generate, and sometimes translate human language. They are called “large” for a good reason: they consist of millions or even billions of parameters, which are the fundamental data points the model uses to make predictions and decisions.

How Do LLM Work?

Imagine teaching a child language by reading every book you can find. That’s essentially what LLMs go through. They are fed vast amounts of text data and use statistical methods to find patterns and learn from context. Through a process known as machine learning, these models become adept at predicting the next word in a sentence, answering questions, summarizing texts, and more.

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.

A prompt, in the simplest terms, is the initial input or instruction given to an AI model to elicit a response or generate content. It's the human touchpoint for machine intelligence, a cue that sets the AI's gears in motion.

Prompts are more than mere commands; they are the seeds from which vast trees of potential conversations and content grow. Think of them as the opening line of a story, the question in a quiz, or the problem statement in a mathematical conundrum – the prompt is the genesis of the AI's creative or analytical output.

For example, when you ask GPT-4o "What's the best way to learn a new language?" you've given it a prompt. The AI then processes this and generates advice based on its training data.

The Art of Prompt Engineering

Bito has created the ability for our AI to understand your codebase, which produces dramatically better results that are personalized to you. This can help you write code, refactor code, explain code, debug, and generate test cases – all with the benefits of AI knowing your entire code base.

Bito AI automatically figures out if you're asking about something in your code. If it's confident, it grabs the relevant parts of your code from our and feeds them to the for accurate answers. But if it's unsure, Bito will ask you to confirm before proceeding.

Bito users come from all over the world, and it makes it super easy to set the AI output language. Bito will automatically generate the text output in the language in your user profile setting, regardless of the prompt input language.

Bito allows setting this language when creating an account, as described in .

You can also set or change this setting anytime by going to in Bito Cloud. Here is a quick video walkthrough.

Supported Languages:

Bito offers 20+ languages for you to choose from. Here is the list of currently supported languages:

1. English (Default Language)

Bito AI chat is the most versatile and flexible way to use AI assistance. You can type any technical question to generate the best possible response. Check out these to understand all you can do with Bito.

To use AI Chat, type the question in the chat box, and press 'Enter' to send. You can add a new line in the question with 'SHIFT+ ENTER'.

Bito starts streaming answers within a few seconds, depending on the size and complexity of the prompt.

The AI Code Review Agent offers a suite of commands tailored to developers' needs. You can manually trigger a code review by entering any of these commands in the comment box below a pull/merge request on GitHub, GitLab, or Bitbucket and submitting the comment. Alternatively, if you are using the self-hosted version, you can configure these commands in the bito-cra.properties file for automated code reviews.

/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

Combining general feedback with specialized review scopes

If you'd like to receive general code quality feedback alongside specialized analyses, include the general keyword in your review command.

For example, to receive feedback on general code quality, performance, and security, use:

• Example: /review general,performance,security

This ensures a holistic review encompassing both general code quality and specific areas of concern.

/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.

Control code review workflow

These commands allow you to manage the AI Code Review Agent's behavior directly within your pull requests across GitHub, GitLab, and Bitbucket.

/pause

Pauses automatic AI reviews on the current pull request.

Use case: Useful when significant changes are underway, and you want to prevent the AI from reviewing incomplete code.

Example: Add a comment with /pause to the pull request.

/resume

Resumes the automatic AI reviews that were previously paused on the pull request.

Use case: Once your code changes are ready for review, use this command to re-enable the AI's automatic analysis.

Example: Add a comment with /resume to the pull request.

/resolve

Marks all Bito-posted review comments as resolved.

Use case: After addressing the issues highlighted by the AI, use this command to clean up the comment threads.

Example: Add a comment with /resolve to the pull request.

/abort

Cancels all in-progress AI code reviews on the current pull request.

Use case: If an AI review is no longer needed or was initiated by mistake, this command stops the process.

Example: Add a comment with /abort to the pull request.

Display Code Review in a Single Post

By default, the /review command generates inline comments, placing code suggestions directly beneath the corresponding lines in each file for clearer guidance on improvements. If you prefer a single consolidated code review instead of separate inline comments, use the #inline_comment parameter and set its value to False.

Example: /review #inline_comment=False

Example: /review scalability #inline_comment=False

Templates help you improve your code quality instantly with AI-powered analysis. Get automated suggestions for performance optimization, security fixes, style improvements, and code cleanup without leaving your editor. Each template provides actionable feedback and ready-to-use code improvements that you can review and apply with a single click.

Available templates

1. Performance Check: Optimize code performance and efficiency

2. Security Check: Identify and fix security vulnerabilities

3. Style Check: Apply coding style and formatting standards

4. Improve Readability: Enhance code clarity and organization

5. Clean Code: Remove debugging and logging statements

How to use templates

Prerequisites

Select the code you want to analyze in your editor before using any template.

Method 1: Click Templates button

1. Select code in your editor

2. Click the Templates button at the bottom of the Bito extension panel

1. Choose the desired template from the dropdown menu

Method 2: Open context menu

1. Select code in your editor

2. Right-click in the editor window

3. Hover over "Bito AI" in the context menu

4. Select the desired template from the submenu

Method 3: Using slash / command in Bito chat box

1. Select code in your editor

2. Type / at the start of the Bito chat box

3. Choose the desired template from the dropdown menu

1. Type some text after the slash / to filter templates by name

Method 4: Command Palette (VS Code)

1. Select code in your editor

2. Go to View → Command Palette (or press Ctrl+Shift+P / Cmd+Shift+P)

3. Type "bito" to see available templates

4. Select the desired template from the list

Applying code suggestions

When templates provide code improvements, you'll see an Apply button above the suggested code snippet.

1. Click the Apply button to open the diff view

1. Review the changes highlighted in the diff:

   • Red lines show code to be removed

   • Green lines show code to be added

2. Choose your action:

Tips

• Select meaningful code blocks for better analysis results

• Templates work best with complete functions or logical code segments

• Review suggested changes before applying them to your codebase

• Verify that the changes don't break existing functionality

Installing Bito CLI (Recommended)

We recommend you use the following methods to install Bito CLI.

Mac and Linux

sudo curl https://alpha.bito.ai/downloads/cli/install.sh -fsSL | bash

Note: curl will always download the latest version.

Archlinux

Arch and Arch based distro users can install it from

yay -S bito-cli

or

paru -S bito-cli

Note for the Mac Users: You might face issues related to verification for which you will have to manually do the steps from (we are working on fixing it as soon as possible).

Windows

• In the , open the folder that has the latest version number.

• From here, download the MSI file called Bito CLI.exe and then install Bito CLI using this installer.

• On Windows 11, you might get notification related to publisher verification. Click on "Show more" or "More info" and click on "Run anyway" (we are working on fixing this as soon as possible).

Installing with Manual Binary Download (Not Recommended)

While it's not recommended, you can download the Bito CLI binary from our repository, and install it manually. The binary is available for Windows, Linux, and Mac OS (x86 and ARM architecture).

Mac and Linux

1. In the , open the folder that has the latest version number.

2. From here, download the Bito CLI binary specific to your OS platform.

3. Start the terminal, go to the location where you downloaded the binary, move the downloaded file (in the command below use bito-* filename you have downloaded) to filename bito.

   mv bito-<os>-<arch> bito

Windows

1. In the , open the folder that has the latest version number.

2. From here, download the Bito CLI binary for Windows called bito.exe.

3. For using Bito CLI, always move to the directory containing Bito CLI prior to running it.

Uninstalling Bito CLI

Mac and Linux

sudo curl https://alpha.bito.ai/downloads/cli/uninstall.sh -fsSL | bash

Note: This will completely uninstall Bito CLI and all of its components.

Windows

For Windows, you can uninstall Bito CLI just like you uninstall any other software from the control panel. You can follow these steps:

1. Click on the Windows Start button and type "control panel" in the search box, and then open the Control Panel app.

2. Under the "Programs" option, click on "Uninstall a program".

3. Find "Bito CLI" in the list of installed programs and click on it.

4. Click on the "Uninstall" button (given at the top) to start the uninstallation process.

After completing these steps, Bito CLI should be completely removed from your Windows machine.

Bito's AI Code Review Agent supports different configuration methods depending on the deployment environment:

1. Bito-hosted – The agent runs on Bito's infrastructure and is configured through the Bito web UI.

2. Self-hosted – The agent runs on user-managed infrastructure and is configured by editing the bito-cra.properties file.

The sections below provide configuration guidance for each setup.

Bito-hosted agent configuration

In Bito-hosted AI Code Review Agent, you can configure the agent through the .

To customize an existing agent, open the page and click the Settings button next to the Agent instance to be modified.

The agent settings page allows configuration of options such as:

• Agent name – Define a unique name for easy identification.

• Review options – Choose the review mode (Essential or Comprehensive), set feedback language, and enable features like auto-review, incremental review, summaries, and change walkthroughs.

• Custom guidelines – Create and apply custom code review rules tailored to your team’s standards directly from the dashboard.

• Filters

These settings tailor the agent’s behavior to match team workflows and project needs. For detailed guidance, see .

Self-hosted agent configuration

In self-hosted deployments, configuration is managed by editing the . This file defines how the agent operates and connects to required services.

Key configuration options include:

• Mode

  • mode = cli: Processes a single pull request using a manual URL input.

  • mode = server: Runs as a webhook service and listens for incoming events from Git platforms.

Each property is documented in detail on the page.

Get up and running with Bito in just a few steps! Bito seamlessly integrates with popular JetBrains IDEs such as IntelliJ IDEA, PyCharm, and WebStorm, providing powerful AI-driven code reviews directly within your editor. Click the button below to quickly install the Bito extension and start optimizing your development workflow with context-aware AI Chat, and more.

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

How do I whitelist Bito's gateway IP address for my on-premise Git platform?

To ensure the AI Code Review Agent operates smoothly with your GitHub (Self-Managed) or GitLab (Self-Managed), please whitelist all of Bito's gateway IP addresses in your firewall to allow incoming traffic from Bito. This will enable Bito to access your self-hosted repository.

List of IP addresses to whitelist:

• 18.188.201.104

• 3.23.173.30

• 18.216.64.170

The agent response can come from any of these IPs.

How can I prevent the AI Code Review Agent from stopping due to token expiry?

You should set a longer expiration period for your GitHub Personal Access Token (Classic) or GitLab Personal Access Token. We recommend setting the expiration to at least one year. This prevents the token from expiring early and avoids disruptions in the AI Code Review Agent's functionality.

Additionally, we highly recommend updating the token before expiry to maintain seamless integration and code review processes.

For more details on how to create tokens, follow these guides:

• GitHub Personal Access Token (Classic):

• GitLab Personal Access Token:

What is "Estimated effort to review" in code review output?

This is an estimate, on a scale of 1-5 (inclusive), of the time and effort required to review this Pull Request (PR) by an experienced and knowledgeable developer. A score of 1 means a short and easy review, while a score of 5 means a long and hard review. It takes into account the size, complexity, quality, and the needed changes of the PR code diff. The score is produced by AI.

Why does Bito need access to my Git account?

Bito requires certain permissions to analyze pull requests and provide AI-powered code reviews. It never stores your code and only accesses the necessary data to deliver review insights.

What permissions does Bito need?

Bito requires:

1. Read access to code and metadata: To analyze PRs and suggest improvements

2. Read and write access to issues and pull requests: To post AI-generated review comments

3. Read access to organization members: To provide better review context

I don’t have admin permissions. Can I still use Bito?

If you don’t have admin access, you’ll need your administrator to install Bito on your organization’s Git account. Once installed, you can use it for PR reviews on allowed repositories. GitHub also sends a notification to the organization owner to request the organization owner to install the app.

Does Bito store my code?

No, Bito does not store or train models on your code. It only analyzes pull request data in real-time and provides suggestions directly within the PR.

Can I choose which repositories Bito has access to?

Yes, after installation, you can select specific repositories instead of granting access to all. You can also manage repository access later through our web dashboard.

What happens after I install the Bito App?

Once installed, you’ll be redirected to Bito, where you can:

1. Select repositories for AI-powered reviews

2. Customize review settings to fit your workflow

3. Open a pull request to start receiving AI-driven suggestions

Where can I get help if I have issues installing Bito?

Contact for any assistance.

Help

Run any one of the below commands.

bito --help

or

bito config –help

Retrieval Augmented Generation (RAG) is a paradigm-shifting methodology within natural language processing that bridges the divide between information retrieval and language synthesis. By enabling AI systems to draw from an external corpus of data in real-time, RAG models promise a leap towards a more informed and contextually aware generation of text.

RAG fuses in-depth data retrieval with creative language synthesis in AI. It's like having an incredibly knowledgeable friend who can not only recall factual information but also weave it into a story seamlessly, in real-time.

The Mechanics of RAG

To understand RAG, let's break it down:

Indexing involves breaking down a source code file into smaller chunks and converting these chunks into that can be stored in a . Bito indexes your entire codebase locally (on your machine) to understand it and provide answers tailored to your code.

Prompt Engineering is the art and science of crafting inputs (prompts) that guide AI to produce the desired outputs. It's about understanding how to communicate with an AI in a way that leverages its capabilities to the fullest. Think of it as giving directions to a supremely intelligent genie without any misunderstandings.

Get up and running with Bito in just a few steps! Bito seamlessly integrates with Visual Studio Code, providing powerful AI-driven code reviews directly within your editor. Click the button below to quickly install the Bito extension and start optimizing your development workflow with context-aware , and more.

Video guide

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

A Bito Workspace represents your organization. It is the highest level of organization in Bito.

In a Bito Workspace, different user types play distinct roles in managing and collaborating within the workspace. Here is an overview of the three user types: Owner, Admin, and User. Understanding these roles will help you effectively manage your workspace and optimize team collaboration.

Owner: The Owner holds the highest level of authority within the workspace

Admin: Admins have a significant role in managing the workspace alongside the Owner

User: Users have access to the workspace with limited administrative privileges

Here's a table summarizing the roles of the different user types in a Bito Workspace:

Generative AI has been making waves across various sectors, from art to technology, leaving many people scratching their heads and wondering: WTF is Generative AI? In this guide, we'll unpack the buzzword and provide you with a clear understanding of what Generative AI is, how it works, and why it's becoming increasingly important in our digital world.

What is Generative AI?

At its core, Generative AI refers to the subset of artificial intelligence where the systems are designed to generate new content. It’s like giving an artist a canvas, but the artist is an algorithm that can create images, compose music, write text, generate programming source code, and much more.

Generative AI systems are typically powered by machine learning models that have been trained on vast datasets. They learn patterns, structures, and features from this data and use this understanding to generate new, original creations that are often indistinguishable from content created by humans.

A workspace is a dedicated environment or space where teams can collaborate and use Bito services. After logging into your Bito account, you can either create a new workspace or join an existing one you've been invited to.

1. Open your project’s folder using Visual Studio Code.

2. Bito uses AI to create an of your project’s codebase. It enables Bito to understand the code and provide relevant answers. There are three ways to start the indexing process:

   • When you open a new project, a popup box will appear through which Bito asks you whether you want to enable indexing of this project or not. Click on the “Enable” button to start the indexing process. You can also skip this step by clicking the “Maybe later” button. You can always index the project later if you want.

• In the bottom-left of Bito plug-in pane, hover your mouse cursor over this icon. You can also enable indexing from here by clicking on the “Click to enable it” text.
• Another option is to open the "Manage Repos" tab by clicking the laptop icon in the top-right corner of the Bito plugin pane.

1. Let’s start the indexing process by using any of the above-mentioned methods.

2. The status will now be updated to “Indexing in progress...” instead of “Not Indexed”. You will also see the real-time indexing progress for the current folder, based on the number of files indexed.

1. Once the indexing is complete, the status will be updated from “Indexing in progress...” to “Indexed”, and will look like this.
2. Now you can ask any question regarding your codebase by adding the keyword "my code" to your AI requests in the Bito chatbox. Bito is ready to answer them!

1. In case you ever want to delete an index then you can do that by clicking on this three dots button and then clicking the “Delete” button.

1. A warning popup box will open in the bottom of Bito’s plugin pane. You can either click on the “Delete” button to delete the project’s index from your system or click on the “Cancel” button to go back.

A Quick Example from a Real Project

For the sake of this tutorial, we’ve created a simple “Music Player using JavaScript”.

Here’s how it looks:

We have added a bunch of songs to this project. The song details like name, artist, image, and the music file name are stored in a file called music-list.js

Question # 1

Let’s ask Bito to list names of all song artists used in my code

As you can see, Bito gave the correct answer by utilizing its understanding of our repository.

Similarly, we can ask any coding-related question like find bugs, improve code, add new features, etc.

Question # 2

Our music player is working fine, but we don’t have any option to mute/unmute the song.

Let’s ask Bito to add this feature.

Here’s the question I used:

In my code how can i add a button to mute and unmute the song? By default, set this button to unmute. Also, use the same design as existing buttons in UI.

After adding the code suggested by Bito, here’s how the music player looks when it starts (unmuted).

And when muted:

Bito UI in Visual Studio Code and JetBrains IDEs is entirely keyboard accessible. You can navigate Bito UI with standard keyboard actions such as TAB, SHIFT+TAB, ENTER, and ESC keys. Additionally, you can use the following shortcuts for quick operations.

The following video demonstrates important keyboard shortcuts.

General

Question & Answers

The following keyboard shortcuts work after the Q/A block is selected.

Change Default Keyboard Shortcuts

Bito has carefully selected the keyboard shortcuts after thorough testing. However, it's possible that Bito selected key combination may conflict with IDE or other extensions shortcut. You can change the Bito default shortcut keys to avoid such conflicts.

Visual Studio Code Editor

1. To Open the Keyboards Shortcuts editor in VS Code, navigate to the menu under File > Preferences > Keyboard Shortcuts. (Code > Preferences > Keyboard Shortcuts on macOS)

1. Search for default available commands, keybindings, or Bito extension-specific commands in VSCode keyboard shortcut editor.

1. Finding a conflict in Key binding → Search for the key and take necessary action, e.g., Remove or Reset.

1. Add a new key binding or map the existing Bito extension command. Provide the necessary information (Command ID) to add the new key binding.

JetBrains