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

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.

Install/run using Bito Cloud

Self-hosted service

Self-hosted 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.

Install/run as a self-hosted service

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

• my repo

• my project

• my workspace

Chinese:

• 我的代码

• 我的仓库

• 我的代码库

• 我的项目

• 我的文件夹

Chinese Traditional:

• 我的程式碼

• 我的倉庫

• 我的項目

• 我的工作區

Spanish:

• Mi código

• Mi repo

• Mi proyecto

• Mi espacio de trabajo

Japanese:

• 私のコード

• 私のリポ

• 私のプロジェクト

• 私のワークスペース

Portuguese:

• Meu código

• Meu repo

• Meu projeto

• Meu espaço de trabalho

Polish

• Mój obszar roboczy

• moje miejsce pracy

• mój obszar roboczy

• moj kod

• mój kod

• moim kodzie

• moje repo

• moje repozytorium

• moim repo

• moj projekt

• mój projekt

• moim projekcie

Bito Cloud offers a single-click solution for using the AI Code Review Agent, 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) 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.

The user-friendly Code Review Analytics dashboards help you track key metrics such as pull requests reviewed, issues found, lines of code reviewed, and understand individual contributions.

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.

Any Shortcut such as "Performance Check" or "Improve Readability" that proposes changes to your existing code automatically opens a "Diff View" between the proposed and actual code. This allows you to review the changes before accepting them into your code. The diff view opens automatically when Bito AI returns the proposed changes. You can also view the diff at any point through the "Diff" action.

Video showing side-by-side diff view

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.

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

3. In the search bar, type "Bito" to locate the Bito plugin

4. Once you locate the Bito plugin, click on the update button to initiate the update

Pro Tip 💡: Enable Auto-update for Bito Plugin on VS Code (as shown in the video)

Updating Bito Plugin on JetBrains IDEs

1. Open your JetBrains IDE (e.g., IntelliJ IDEA, PyCharm, etc.)

2. Go to Settings by clicking on "File" in the menu bar (Windows/Linux) or by clicking on "IntelliJ IDEA" in the menu bar (macOS).

3. In the Settings window, navigate to the "Plugins" section

4. Switch to the "Installed" tab to view the list of installed plugins

5. Locate the Bito plugin in the list and click on the update button to initiate the update

Bito deploys a locally on the user’s machine, bundled as part of the Bito IDE plug-in. This database uses (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 .

Bito’s "AI Code Completions" capabilities offer real-time, personalized code suggestions as you type. Powered by the latest best-in-class Large Language Models (such as GPT-4o mini and Google PaLM 2 – 540B parameters compare to Copilot’s 12B parameter model), Bito understands your codebase and provides contextually accurate code suggestions right from within your IDE. Bito’s model is also trained on data until 2 months back, many other models are trained on 12-18 months old data.

Speed up your development workflow with AI-assisted code completion. Watch as lines of code, full functions, or even entire code blocks are generated for you on the fly.

Supporting a wide range of over 35 programming languages—from Python to SQL, from C++ to Go and JavaScript—this feature is designed to make coding faster, easier, and more efficient for developers like you.

Let's dive in to see .

Bito Cloud lets you integrate the AI Code Review Agent 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. Select the appropriate Git provider guide from this link based on your Git provider, and follow the step-by-step instructions to install the AI Code Review Agent using Bito Cloud. Be sure to review the prerequisites and the installation/configuration steps provided in the documentation.

2. Download the bito-action-script folder from GitHub, which includes a shell script (bito-actions.sh) and a configuration file (bito_action.properties).

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.

Bito can suggest code for these programming languages:

• C

• C++

• C#

• CSS

• Clojure

• Dart

• Elixir

• Erlang

• Fortran

• Go

• GoogleSQL

• Groovy

• Haskell

• HTML

• Java

• JavaScript

• JavaServer Pages

• Kotlin

• Lean (proof assistant)

• Lua

• Objective-C

• OCaml

• Perl

• PHP

• Python

• R

• Ruby

• Rust

• Scala

• Shell script

• Solidity

• SQL

• Swift

• TypeScript

• XML

• Verilog

• YAML

Advanced AI planning and reasoning capabilities

Wingman excels at understanding high-level instructions and breaking them into actionable steps. Unlike traditional assistants, it plans and executes tasks, making it an invaluable partner for complex projects.

The more detailed and specific your instructions, the better the results Wingman can deliver. You can also iterate with Wingman as the project evolves, refining its output step by step to meet your exact requirements.

Example use case: Provide a detailed prompt like:

Create an API for user authentication and integrate it into my backend. Please review my code thoroughly to suggest the key interfaces that should be created. Besides normal user registration and authentication, also include token management capabilities and risk-based scoring mechanisms to help alert us if a user might be trying to breach the system.

Contextual/focused research capabilities

When tasked with challenges requiring additional information, Wingman conducts targeted research to gather relevant details and context for your project.

Example use case: If you're building a feature but need to confirm industry-standard practices, Wingman will gather up-to-date information and incorporate it into the solution.

Seamless web browsing and information gathering

Wingman can browse the web autonomously to find and retrieve useful data. This feature ensures that your projects benefit from the latest tools, libraries, or guidelines available.

Example use case: If you ask Wingman to implement a feature using a cutting-edge library, it will search for the library, understand its documentation, and integrate it into your code.

Code writing in multiple programming languages

Wingman can generate high-quality code across a variety of programming languages. It understands your project’s requirements and provides context-aware solutions tailored to your tech stack.

Example use case: Ask Wingman to write a function in Python, JavaScript, or another language—it will deliver optimized and functional code.

Integration with popular tools

Wingman integrates seamlessly with tools you already use, including:

• Version control: Support for Git operations like git push, git commit, and git clone for GitHub and GitLab workflows.

• Project management: Jira, Linear

• Documentation: Confluence

• File operations: Manage and update files directly.

This integration ensures that Wingman fits naturally into your existing workflow.

Example use case: Assign a Jira ticket to Wingman, and it will complete the associated coding task, update the ticket, and link it to the appropriate pull request.

Extensible architecture

Wingman is designed with flexibility in mind, allowing you to easily integrate it with tools that fit your workflow. Its adaptable architecture ensures it evolves with your development needs.

Example use case: If your team starts using a new project management tool, Wingman’s flexibility lets you integrate it into your process effortlessly.

Have a specific tool in mind? Drop us a note at [email protected] to request adding support for it.

Task completion made easy

Wingman is designed to assist you in completing tasks efficiently, working alongside you and checking in as needed. Once provided with clear instructions, it handles everything from planning to execution while keeping you in the loop.

Example use case: Wingman can help implement a feature, document it in Confluence, and create a pull request—keeping you informed every step of the way.

Natural language interaction

Communicate with Wingman in any language through a chat interface. Describe what you need, and Wingman will take care of the rest. Additionally, you can set your preferred AI output language on the Advanced settings page. For example, if you set Spanish as your preferred language, Wingman will respond to you in Spanish.

Example use case: Type "Generate a REST API for user management and write tests for it" in the chat, and Wingman will handle the implementation and testing.

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.

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): View Guide

• GitLab Personal Access Token: View Guide

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 [email protected] for any assistance.

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.

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 Use cases and examples 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.

Bito makes it super easy to use the answer generated by AI, and take a number of actions.

Copy Answer

Copy the answer to the clipboard.

Regenerate Answer

AI may not give the best answer on the first attempt every time. You can ask Bito AI to regenerate the answer by clicking "Regenerate" button next to the answer.

Copy or Insert Code

If the AI answer includes a code snippet, Bito automatically identifies and displays code in a separate block. This makes it easy to copy the code to the clipboard or insert it in the code editor.

Rate Response

Vote response "Up" or "Down". This feedback Bito improve the prompt handling.

Modify Last Prompt

Many of these commands can be executed with keyboard shortcuts documented here: Keyboard shortcuts

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.

5. Share on X (Twitter):

   1. Click on X (Twitter) from the menu, and a dialogue window will appear, asking whether you want to open the external site.

   2. Simply click "Open" to proceed.

   3. You will be redirected to the X (Twitter) website, with a pre-written tweet containing a link to your Chat Session ready to be published.

   4. Click the "Post" button to send the tweet.

6. Share Through Email:

   1. Click on Email from the menu, and you will be redirected to your email application.

   2. Select your email account if needed.

   3. The email will be pre-filled with all the necessary information, including the link to your Chat Session.

   4. Add the receiver(s) of this email using the "To" input field.

   5. Click the "Send" button to send the email.

7. Share the Link:

   1. Click on Link from the menu.

   2. A confirmation popup will appear. Click Share session to generate a unique URL for your chat session, which will automatically be copied to your clipboard for easy sharing.

   3. Feel free to share this link with anyone you'd like to.

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 analyzes the file you are currently editing and your codebase to understand the context. It offers two types of AI Code Completions:

1- Autocompletions (complete the line as you type)

In this method, as you are writing a line of code, Bito will automatically predict what you will write next and generate relevant suggestions based on your codebase.

2- Comment to code

In this method, you can write any kind of requirements you have in natural language comments, and Bito will suggest the best code tailored to your codebase to fulfill those requirements – often writing the entire function.

How Good are AI Code Completions?

Since Bito is familiar with your entire codebase, it can provide more accurate code suggestions than other AI Coding Assistants available today.

For example:

1. Bito can see your imports and predict what task you are trying to complete.

2. Bito can read the function you're inside and predict what you'll do next.

3. Bito can spot the APIs you've integrated and suggest possible endpoints to call.

After gathering the context, Bito uses different Large Language Models (LLMs) to come up with some options that you will most likely want to write next. So, if one solution doesn’t work, there are more you can try.

Bito provides high-quality code completions that align with the code you are working on. However, if the suggested completions are not as accurate in your specific case, you can write additional code or provide explicit instructions in comments to help Bito better understand the context and generate more precise solutions.

Bito's AI Code Completions doesn't interfere with your coding process. It offers code suggestions only after you have paused typing for 250 milliseconds (you can adjust this in settings), or if you explicitly ask for AI Code Completions by typing Alt + Shift + K on Windows or Option + Shift + K on macOS, and the suggested code is merely displayed as a placeholder.

To accept the entire code suggestion, simply press the "Tab" key on your keyboard. Alternatively, you can accept the code completion incrementally, word by word, by pressing "" (coming soon...). To accept one line at a time, click the three dots button in the code completion UI toolbar and then select "Accept Line" (coming soon...).

If you don’t like the suggestion, Bito does not force you to use it. You can simply dismiss it by pressing the “Esc” key on your keyboard or continue typing as normal.

Bito also provides alternative suggestions, which you can navigate using the arrow keys in the code completion UI toolbar or by using the shortcut keys mentioned below.

Prerequisites

Terminal

• Bash (for Mac and Linux)

• CMD (for Windows)

Using Bito CLI

Before you can use Bito CLI, you need to install and configure 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.

• Exit Bito CLI: To quit/exit from Bito CLI, type quit and press Ctrl+D .

• Terminate: Press Ctrl+C to terminate Bito CLI.

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:

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?

To start a conversation, type your question directly as a reply to the Agent’s code review comment.

The AI Code Review Agent will analyze your comment and determine if it’s a valid and relevant question.

• If the agent decides it’s a valid question, it will respond with helpful insights.

• If the agent determines it’s unclear, off-topic, or not related to its feedback, it will not respond.

To help the agent recognize your question faster, you can also tag your comment with @bitoagent or @askbito. Tagging informs the Agent that your message is intended as a question. However, tagging does not guarantee a reply. The agent will still analyze your comment and decide whether it is a valid question worth responding to.

Bito usually responds within about 10 seconds.

• On GitHub and Bitbucket, you may need to manually refresh the page to see the response.

• On GitLab, updates happen automatically.

What you can ask about

When chatting with the AI Code Review Agent, you can ask questions to better understand or improve the code feedback it provided. Here are examples of what you can ask:

• Clarifications about a highlighted issue Ask the AI to explain why it flagged a certain line of code or why something might cause a problem.

• Request for alternative solutions Request different ways to fix or improve the code beyond what was originally suggested.

• Deeper explanations If you want to understand the technical reasoning behind a suggestion (e.g., security concerns, performance impacts, best practices), you can ask for more detailed explanations.

• Request for examples Ask the AI to provide an example snippet showing the corrected or improved code.

• Trade-off discussions Ask the AI about pros and cons of different approaches it may have suggested (e.g., performance vs. readability).

• Best practices guidance Request advice on best practices related to the specific code snippet — such as naming conventions, error handling, optimization tips, or design patterns.

• Language-specific advice If you’re working in a particular language (e.g., JavaScript, Python, Java), you can ask for language-specific guidance related to the comment.

• Request for more context If the suggestion feels too "short" or "surface level," you can ask the AI to explain more about the broader coding or architectural concept behind its feedback.

• Security and safety questions If a suggestion touches on security (like input validation, authentication, or encryption), you can ask for further security-related advice.

• Testing and validation Ask the AI if it recommends writing any tests based on its code suggestions and what those tests might look like.

What you cannot ask about

The AI can only answer questions related to its own code review comments.

• You cannot ask general questions about the repository or unrelated topics.

• You cannot start a new thread independently — your question must be a reply to a comment made by Bito’s AI Code Review Agent.

If your comment is not linked to a Bito review comment, the AI will not respond.

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

1. Enable comment resolution rules in your Git provider

GitHub:

• Go to your repository → Settings → Branches

• Create or edit a branch protection rule (e.g., for main)

• Enable:

  • ✅ Require a pull request before merging

  • ✅ Require conversation resolution before merging

GitLab:

• Go to your project → Settings → Merge requests

• Under Merge checks, enable:

  • ✅ All threads must be resolved

• Click Save changes button.

Bitbucket:

• Go to your repository → Repository settings → Branch restrictions

• Click Add a branch restriction button.

• Switch to Merge settings tab.

• Under Merge checks, enable:

  • ✅ No changes are requested

• Click Save button.

2. Turn on “Request changes comments” in Bito

• Go to Repositories in the Bito dashboard.

• Click on Settings for your desired AI Code Review Agent instance.

• Enable the toggle: “Request changes comments”

• Save changes

When this is on, Bito will flag actionable AI feedback as formal review comments requiring resolution. Informational or minor suggestions will remain as regular comments.

3. What happens in a pull request

• Bito runs an AI code review on your pull request or merge request.

• Actionable issues are posted as change requests.

• Your Git provider treats these comments according to your configured merge rules.

• If comment resolution is required, the merge is blocked until the flagged issues are resolved.

Example workflow

1. Developer opens a pull request or merge request.

2. Bito reviews the code and posts a “request change” comment on a problematic line.

3. The Git provider blocks the merge due to unresolved comments or threads.

4. Developer fixes the issue and marks the thread as resolved.

5. Merge becomes possible once all conditions are met.

Why use this feature?

• Enforces follow-up on critical AI-detected issues.

• Works natively with GitHub, GitLab, and Bitbucket workflows.

• Ensures only reviewed and clean code gets merged.

• Helps maintain consistent code quality at scale.

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 coding assistance 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 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

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.

For now, this feature is only available for our Team Plan which costs $15 per user per month. We have plans to release it for our Free Plan soon. But it will be limited to repos of 10MB indexable size.

Recent breakthroughs in and have helped make many AI Coding Assistant tools available, including Bito, to help you develop software faster.

The major issue with these AI assistants, though, is that they have no idea about your entire codebase. Some tools take context from currently opened files in your IDE, while others enable you to manually enter code snippets in a chat-like interface and then ask questions about them.

But with Bito’s AI that understands your entire repository, this is a whole new capability. For example, what if you could ask questions like:

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

• In my code list all the files and code changes needed to add column desc in table raw_data in dailyReport DB.

• In my code suggest code refactoring for api.py and mention all other files that needs to be updated accordingly

• Please write the frontend and backend code to take a user’s credentials, and authenticate the user. Use the authentication service in my code

This will definitely improve the way you build software.

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.

How to Ask Questions?

Once indexing is complete, you can ask any question in the Bito chatbox. Bito uses AI to determine if you are asking about something in your codebase. If Bito is 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.

In case you ask a general question (not related to your codebase), then Bito will directly send your request to our LLM without first looking for the appropriate local context.

However, if you want to ask a question about your code no matter what, then you can use specific 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.

Once Bito sees any input containing these keywords, it will use the index to identify relevant portions of code or content in your folder and use it for processing your question, query, or task.

Security of your code

As usual, security is top of mind at Bito, especially when it comes to your code. A fundamental approach we have taken is to keep all code on your machine, and not store any code, code snippets, indexes, or on Bito’s servers or our API partners. All code remains on your machine, Bito does not store it. In addition, none of your code is used for AI model training.

Learn more about .

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):

Code Explanation

• What a particular code file does

  • In my code what does code in sendgrid/sendemail.sh do?

• What a particular function in my code does

  • In my repo explain what function message_tokens do

Code Translation

• In my project rewrite the code of signup.php file in nodejs

Code Refactoring

• In my workspace suggest code refactoring for api.py and mention all other files that need to be updated accordingly

Fix Bugs

• In my code find runtime error possibilities in script.js

• Find logical errors in scraper.py in my code

Detect Code Smells

• In my code detect code smells in /app/cart.php and give solution

Generate Documentation

• Generate documentation for search.ts in my workspace in markdown format

Generate Unit tests

• In my code write unit tests for index.php

• In my code generate test code for code coverage of cache.c

Summarize Recent Code Changes

• summarize recent code changes in my code

Code Search using natural language

• Any function to compute tokens in my project?

• Any code or script to send emails in my workspace?

• In my repo list all the line numbers where $alexa array is used in index.php.

Give details of making modifications

• In my code list all the files and code changes needed to add column desc in table raw_data in dailyReport DB.

Bito's Team Plan users can either start a conversation with Basic AI Models (e.g. GPT-4o mini, Claude Haiku, Nova Lite 1.0, and similar models) or more Advanced AI Models (e.g. o3-mini, DeepSeek-V3 (served from the US and Europe), GPT-4o, Claude Sonnet 3.5, and best in class AI models). In contrast, the Free Plan users are limited to using only Basic AI Models.

This guide will help you understand when to use Basic and when to use Advanced AI models. You will also learn how to select and chat with these models in the Bito chatbox.

When to use Basic vs Advanced AI Models?

Basic AI Models

These models are designed to provide essential AI capabilities for most everyday coding tasks. They offer a solid starting point for generating boilerplate code, writing documentation, explaining code snippets, and solving simple coding problems.

While using Basic AI models, your prompts and the memory of the chat are limited to 40,000 characters (about 18 single-spaced pages).

They are also less expensive in terms of API costs. So, if you are frequently asking less-important questions in the Bito chatbox, then these Basic AI models will definitely help you save costs.

Advanced AI Models

These models are more suitable for high-complexity tasks that require long/complex prompts and advanced reasoning.

They provide more accurate and relevant code snippets, comments, or solutions to complex coding problems.

Additionally, when using Advanced AI models, your prompts and the chat memory can extend up to 240,000 characters (about 110 single-spaced pages). This means that these models can process your entire code files, leading to more accurate answers.

So, if you are looking for the best results for complex tasks, then go with Advanced AI models.

How to Chat with Basic/Advanced AI Models?

When you open the Bito plugin in VS Code or JetBrains IDEs, the "AI Chat" tab is displayed by default. This tab includes a drop-down menu at the bottom-right corner that allows you to select the AI model you want to chat with.

The available AI models are categorized under two sections "BASIC" and "ADVANCED". You can either let Bito auto-select an AI model or manually pick one that best suits your needs.

Once you select an AI model and start a chat with it, the drop-down menu will disappear, and your chosen model will handle the entire chat session.

If you want to change the AI model, click the New Chat icon located in the bottom-left corner of the Bito plugin pane. In the new chat session, select a different model from the drop-down menu.

seamlessly integrates with various tools such as Jira, Linear, Confluence, and more.

Below is a list of developer tools available to Wingman. Each tool comes with unique parameters and capabilities, and Wingman is ready to assist you in configuring and using them effectively.

is an innovative tool that harnesses the power of 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.

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.

The Training Regime

Data, Data, and More Data: LLMs are the heavyweight champions of the data world. They are trained on diverse datasets comprising encyclopedias, books, articles, and websites to learn a wide range of language patterns and concepts.

Supervised and Unsupervised Learning: Some LLMs learn through supervised learning, meaning they learn from datasets that have been labeled or corrected by humans. Others use unsupervised learning, meaning they infer patterns and rules from raw data without human annotation.

Fine-Tuning: After the initial training, LLMs can be fine-tuned for specific tasks, like legal document analysis or medical diagnosis, by training them further on specialized data.

Applications of LLMs

Writing Assistance: Grammarly or the autocomplete in your email are powered by LLMs. They predict what you’re trying to say and help you say it better.

Chatbots: If you've ever chatted with and noticed that it sounds almost like a real person, that's because it is powered by several state-of-the-art Large Language Models.

Translation Services: Services like Google Translate use LLMs to convert text from one language to another, learning from vast amounts of bilingual text to improve their accuracy.

The Magic Behind the Scenes

Neural Networks: The core technology behind LLMs is artificial neural networks, particularly a type called Transformer models. These mimic some aspects of human brain function and are particularly good at handling sequential data like text.

Training Hardware: Training LLMs requires significant computational power, often involving hundreds of GPUs or specialized TPUs that work in tandem for weeks or months.

Continuous Learning: LLMs don’t stop learning after their initial training. They can continue to learn from new data, improving their performance over time.

Examples of Large Language Models

GPT Series by OpenAI

The GPT series by OpenAI has been a trailblazer in the field of LLMs. Each version of the Generative Pre-trained Transformer has been more powerful than the last, with GPT-4o as a staggering leap forward. Boasting over 200 billion parameters, this model is not just about size; it’s about the nuanced understanding and generation of human-like text. GPT-4o can craft essays that are indistinguishable from those written by humans, compose complex poetry, and even generate functional computer code across several languages, which is a testament to its versatility.

GPT-4o's influence extends across industries. For instance, it can simulate conversations, create educational content, and even assist programmers by converting natural language descriptions into code snippets. Its advanced capabilities are being integrated into various software applications and tools, enhancing productivity and sparking creative new approaches to problem-solving.

BERT by Google

BERT stands for Bidirectional Encoder Representations from Transformers. It's a complicated name, but really, it's just Google's method for making search engines smarter. Unlike earlier models, BERT examines the context of a word in both directions (left and right of the word) within a sentence, leading to a far more nuanced interpretation of the query. This ability means that BERT can grasp the full intent behind your searches, so the results you get are closer to what you actually need.

Since its integration into Google's search engine, BERT has significantly improved the relevance of results for millions of queries every day. For users, this often translates to finding answers more quickly and accurately, sometimes in subtle ways that may go unnoticed but are nonetheless powerful. Beyond search, BERT is also revolutionizing natural language processing tasks such as language translation, question answering, and text summarization.

In summary, both the GPT series and BERT are not just steps but giant leaps forward in our ability to interface with machines in a more natural, intuitive way. They are redefining what's possible in the realm of AI and continuing to pave the way for smarter, more responsive technology.

Ethical Considerations and Challenges

Bias in AI: Since LLMs learn from existing data, they can perpetuate and amplify biases present in that data. It’s an ongoing challenge to ensure that LLMs are fair and unbiased.

Privacy: Training LLMs on personal data raises privacy concerns. Ensuring data used is anonymized and secure is paramount.

Environmental Impact: The energy consumption of training and running LLMs is significant. Researchers are working on more efficient models to mitigate this.

The Future of LLMs

Evolving Intelligence: LLMs are getting more sophisticated, with future models expected to handle more complex tasks and exhibit greater understanding of human language.

Interdisciplinary Integration: LLMs are beginning to intersect with other fields, such as bioinformatics and climatology, providing unique insights and accelerating research.

Democratization of AI: As LLMs become more user-friendly, their use is expanding beyond tech companies to schools, small businesses, and individual creators.

Conclusion

Large Language Models are transforming how we interact with machines, making them more human-like than ever. They're a blend of colossal data, computing power, and intelligent algorithms, pushing the boundaries of what machines can understand and accomplish. As they evolve, LLMs will continue to shape our digital landscape in unpredictable and exciting ways. Just remember, the next time you type out a sentence and your phone suggests the end of it, there’s a little bit of LLM magic at work.

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

Make sure you have Bito CLI installed on your system. If you haven't installed it, you can find detailed instructions in the Bito CLI repository at https://github.com/gitbito/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.

Prerequisites

To get started with Bito Wingman, ensure the following requirements are met:

1. Bito Team Plan:

   • Subscribe to the Bito Team Plan to unlock Bito Wingman along with all other premium features. For detailed pricing information, visit our Pricing page.

2. Install or update the VS Code extension:

   • Ensure you have Bito v1.4.7 or later installed in your VS Code editor.

   • The Bito Wingman will download automatically after you install or update the Bito IDE extension.

   • Once the download is complete, Wingman will prepare itself, and you'll be ready to use it.

How to use Bito Wingman?

Bito Wingman can be used in the following ways:

• Using Bito Wingman via IDE

• Using Bito Wingman via CLI

Using Bito Wingman via IDE

1. Open Bito Wingman:

   • Open Bito in your IDE and click on "Launch Bito Wingman" button from the Bito panel.

2. Start a session:

   • In the Wingman window, type your instructions in the chatbox and submit them.

3. Set or change the working directory:

   • Default Behavior:

     • If no project is open in the IDE, Wingman defaults to your home directory.

   • Set working directory:

     • Click "Select specific directory" to choose a working directory for a new session.

   • Change working directory:

     • For existing sessions, click the edit icon next to the folder path at the top of the Wingman screen.

     • Enter the complete path to your desired directory.

1. Manage sessions:

   • All active Wingman sessions are listed in the left sidebar.

   • You can run multiple sessions simultaneously, and Wingman will manage them in the background.

Using Bito Wingman via CLI

This section explains how to run Bito Wingman from the command line. The prerequisites are the same as above, and the CLI binary is installed automatically with the Bito IDE extension.

1. Locating the executable:

   • After installation, the Bito Wingman binary is located at:

     • <User home directory>/.bitowingman/bin

   • The executable file is named with the version number and target platform. For example:

     • macOS: bitowingman-1.0.9-darwin-arm64

     • Windows: bitowingman-1.0.9-win32-x64.exe

1. CLI usage modes:

   Bito Wingman supports two modes for interacting via the CLI:

   • Interactive mode (recommended): Interactive mode provides a chat-like interface for real-time command execution.

     • On macOS: ~/.bitowingman/bin/bitowingman-1.0.9-darwin-arm64 -i

     • On Windows (PowerShell): & "$env:USERPROFILE\.bitowingman\bin\bitowingman-1.0.9-win32-x64.exe" -i

     Note: After launching interactive mode, type help and press Enter to view the list of supported commands.

   • Non-interactive mode: Non-interactive mode allows you to execute a command directly and receive the results without entering a full session.

     • On macOS: ~/.bitowingman/bin/bitowingman-1.0.9-darwin-arm64 "run git diff and summarize the changes"

     • On Windows (PowerShell): & "$env:USERPROFILE\.bitowingman\bin\bitowingman-1.0.9-win32-x64.exe" "run git diff and summarize the changes"

Configuring tools in Wingman

Bito Wingman seamlessly integrates with various tools such as Jira, Linear, Confluence, and more. Click the "Tools" button in the top-right corner of the Wingman screen to view all supported tools.

1. To configure a tool, simply ask Wingman, "How do I configure [Tool Name]?"

2. Wingman will provide detailed step-by-step instructions. Follow the instructions to complete the configuration process.

3. If a tool requires an API token, Wingman will guide you through the process of obtaining it. Once you provide the token, Wingman will handle the configuration automatically.

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 Bito web UI.

To customize an existing agent, open the Code Review > Repositories 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 – Exclude specific files, folders, or branches from review to focus on relevant code.

• Tools – Enable additional checks, such as secret scanning and static analysis.

• Chat – Configure how the agent responds to follow-up questions in pull request comments and manage automatic replies.

These settings tailor the agent’s behavior to match team workflows and project needs. For detailed guidance, see Create or customize an Agent instance.

Self-hosted agent configuration

In self-hosted deployments, configuration is managed by editing the bito-cra.properties file. 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.

• Authentication

  • bito_cli.bito.access_key: Required for authenticating the agent with the Bito platform.

  • git.provider, git.access_token, etc.: Required for connecting to the appropriate Git provider (e.g., GitHub, GitLab, Bitbucket).

• General feedback settings

  • code_feedback: Enables or disables general feedback comments in reviews.

• Analysis tools

  • static_analysis: Enables static code analysis.

  • dependency_check: Enables open-source dependency scanning.

  • dependency_check.snyk_auth_token: Required when using Snyk for vulnerability detection.

• Review format and scope

  • review_comments: Defines output style (e.g., single post or inline comments).

  • review_scope: Limits the review focus to specific concerns such as security, performance, or style.

• Filters

  • include_source_branches and include_target_branches: Restrict reviews to pull requests that match specified source and target branch patterns.

  • exclude_files: Skips selected files based on glob patterns.

  • exclude_draft_pr: Skips draft pull requests when enabled (default: True).

Each property is documented in detail on the bito-cra.properties file documentation page.

Get up and running with Bito in just a few steps! Bito seamlessly integrates with Visual Studio Code, providing powerful AI-driven coding assistance 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, AI Code Completions, and more.

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:

• https://code.visualstudio.com/docs/remote/ssh

• https://learn.microsoft.com/en-us/windows-server/administration/openssh/openssh_install_firstuse

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:

• https://code.visualstudio.com/docs/remote/wsl-tutorial

• https://learn.microsoft.com/en-us/windows/wsl/install

Video Guide:

AI Code Completions are disabled by default. To enable them follow the steps below.

VS Code

1. Click the gear icon at the bottom left of the VS Code window. Then select “Settings” to open the main settings page.

1. From the settings sidebar, click on "Text Editor" and then select "Suggestions". Now, on the right-side, tick the checkbox given in front of "Inline Suggest: Enabled" option. Please note that if this option is disabled then Bito's AI Code Completions functionality will not work.

1. In the search bar, type "bito" and then from the sidebar click on "Bito" under "Extensions" to access the Bito extension settings.

1. Here you will see three options that can be configured. These are:

   1. Enable Auto Completion: Tick this checkbox to enable inline code suggestion in the editor. Uncheck it to disable this feature.

   2. Enable Comment to Code: Tick this checkbox to enable generating code from comment in the editor. Uncheck it to disable this feature.

   3. Set Auto Completion Trigger Logic: Decide how fast Bito makes suggestions by setting your preferred pause time. This input field allows you to set the pause time in milliseconds. Lower values make suggestions more often. Minimum and default value is 250 milliseconds.

JetBrains IDEs (e.g. IntelliJ IDEA, WebStorm, PyCharm, etc.)

1. Click the gear icon at the top right of the JetBrains IDE window, then select "Settings" to open the main settings window.
2. Now, in the sidebar click on "Tools" and then click "Bito" to access the Bito extension settings.
3. Here you will see three options that can be configured. These are:

   1. Enable Auto Completion: Tick this checkbox to enable inline code suggestion in the editor. Uncheck it to disable this feature.

   2. Enable Comment to Code: Tick this checkbox to enable generating code from comment in the editor. Uncheck it to disable this feature.

   3. Set Auto Completion Trigger Logic: Decide how fast Bito makes suggestions by setting your preferred pause time. This input field allows you to set the pause time in milliseconds. Lower values make suggestions more often. Minimum and default value is 250 milliseconds.

1. After doing all of the above steps, you must click on "Apply" and then "OK" button to save your changes. Otherwise, your modifications will be lost.

Bito Wingman is an AI coding agent designed to revolutionize the way you build software. Unlike traditional code assistants or autocomplete tools, Wingman acts as a virtual developer on your team capable of handling complex coding tasks from start to finish, with direction from you. Much of Bito Wingman was built by Bito Wingman.

Wingman understands high-level instructions, breaks them into actionable steps, researches relevant information, and executes tasks autonomously.

Wingman can handle everything from code generation to managing Jira tickets and updating files. It deeply understands your code, excels at reasoning and planning to handle complex tasks, and has access to apps such as file operations, Jira, Linear, Confluence, GitHub, GitLab, and many more.

AI in its purest form frees us up to work much more iteratively and on many things at one time. But you need tools that work that way too. Wingman is designed to be nimble to meet your work habits, from the browser to your local IDE. Run as many tasks as you have browser tabs open. Work in your IDE too. Switch back and forth. It’s all possible with your Wingman.

Example instructions for Wingman

Here are some real-world examples of tasks you can ask Wingman to handle, from coding and documentation to building and testing.

• “Review jira ticket AI-5623, write the code, update the necessary files, and commit it. Mark the ticket as in testing”

• “Document my repo and upload it to confluence. Please be sure to highlight the major modules and the key dependencies. Diagram out the system architecture in mermaid.”

• “Update my build script, then build and run my code”

What can Wingman do?

• Write code: Generate high-quality, context-aware code to implement features, fix bugs, or even start entire projects from scratch.

• Plan and execute: Understand your objectives, break them into smaller steps, and manage execution intelligently.

• Research on demand: Use web browsing capabilities to gather information, research APIs, or solve challenges in real time.

• Automate repetitive tasks: Handle the grunt work, from generating boilerplate code to managing Jira tickets and updating files.

• Integrate seamlessly: Work across popular tools like Jira, Linear, Confluence, and more to fit right into your workflow.

• Collaborate intelligently: Act as an AI pair programmer, offering proactive suggestions, reasoning about solutions, and scaling alongside your team’s needs.

How does Wingman work?

Wingman uses a combination of large language models, planning algorithms, and integrations with your favorite tools to deliver results. When you give Wingman an instruction, it:

• Understands: Processes the intent behind your high-level request.

• Plans: Breaks down the task into actionable steps with a clear roadmap.

• Researches: Fetches relevant information if needed, such as documentation or examples.

• Executes: Writes, tests, and manages code or other assets to complete the task end-to-end.

Why use Bito Wingman?

• Save time: Offload tedious and repetitive tasks, freeing you to focus on creative and strategic aspects of development.

• Increase productivity: Tackle more in less time with an AI developer that handles projects autonomously. Many developers on our own team report being 50% to 300% more productive.

• Boost quality: Generate clean, functional code with minimal errors thanks to Wingman’s intelligent reasoning.

• Seamless collaboration: Work smarter with an AI assistant that integrates with your workflow and scales with your team.

Pricing details

Bito Wingman is available to use as part of our Team Plan. For detailed pricing information, visit our Pricing page.

Learn more

• Getting started

• Supported tools

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.

Create a New Workspace

The link to create a new workspace will appear at the bottom of the sign-up flow screen. Click on "Create Workspace" to get started.

Now, enter the name of the workspace. You can also choose to make this workspace discoverable by the users with the same domain email as your email. Finally, click on the "Next" button to proceed creating a new workspace.

Once you complete the Workspace setup, Bito will be ready to use.

Join an Existing Workspace

If your email domain is allowed for the Workspace, or your coworker invited you, you will see the Workspace listed during the sign-up flow under the "Workspaces Available to Join" list.

Simply click on the "Join" button given in front of the workspace you want to join. Joining your company or team Workspace takes less than a minute.

Alternatively, you can join the Workspace through the Workspace link shared by your coworker.

Change Workspace

Follow the below steps to switch to a different workspace:

1. First log out of your Bito account.

1. Then, log back in and choose the workspace you want from the available list.

How to See Which Workspace You Are In?

In the IDE extension, place your mouse cursor over the workspace icon. The workspace name will show up as a tooltip.

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 to allow you to decide where you want to store your code, either locally on your machine, in your cloud, or on Bito’s cloud (coming soon). We do not store any code, code snippets, indexes or embedding vectors on Bito’s servers unless you expressly allow that. Importantly, our AI partners do not store any of this information.

All requests are transmitted over HTTPS and are fully encrypted.

None of your code or AI requests are used for AI model training. None of your code or AI requests are stored by our AI partners. Our AI model partners are OpenAI, Anthropic, and Google. Here are their policies where they state that they do not store or train on data related to API access (we access all AI models via APIs):

1. OpenAI:

2. Anthropic:

3. Google Cloud: (5th paragraph)

The AI requests including code snippets you send to Bito are sent to Bito servers for processing so that we can respond with an answer.

Interactions with Bito AI are auto-moderated and managed for toxicity and harmful inputs and outputs.

Any response generated by the Bito IDE AI Assistant is stored locally on your machine to show the history in Bito UI. You can clear the history anytime you want from the Bito UI.

SOC 2 Type II Compliance

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.

Our SOC 2 Type II compliance means:

• Enhanced Data Security: We consistently implement robust controls to protect your data from unauthorized access and ensure it remains secure.

• Operational Excellence: Our processes are designed to maintain high availability and reliability, ensuring uninterrupted service.

• Regular Monitoring and Testing: We conduct continuous monitoring and regular internal reviews to uphold the highest security standards.

This certification is an assurance that Bito operates with a high level of trust and transparency, providing you with a secure environment for your code and data.

For any further questions regarding our SOC 2 Type II compliance or to request a copy of the audit report, please reach out to

Code Flow through Bito’s System

AI Code Review Agent

When you use the self-hosted/docker version that you have setup in your VPC, in the docker image Bito checks out the diff and clones the repo for static analysis and also to determine relevant code context for code review. This context and the diff is passed to Bito's system. The request is then sent to a third-party LLM (e.g., OpenAI, Google Cloud, etc.). The LLM processes the prompt and return the response to Bito. No code is retained by the LLM. Bito then receives the response, processes it (such as formatting), and returns it to your self-hosted docker instance. This then posts it to your Git provider. However, the original query is not retained, nor are the results. After each code review is completed, the diff and the checked out repo are deleted.

If you use the Bito cloud to run the AI Code Review Agent, it runs similarly to the self-hosted version. Bito ephemerally checks out the diff and clones the repo for static analysis and to determine the relevant code context for code review. This context and the diff is passed to Bito's system. The request is then sent by Bito to a third-party LLM (e.g., OpenAI, Google Cloud, etc.). The LLM processes the prompt and return the response to Bito. No code is retained by the LLM. Bito then receives the response, processes it (such as formatting), and posts it to your Git provider. However, the original query is not retained, nor are the results. After each code review is completed, the diff and the checked out repo are deleted.

AI Chat and Code Completions

When we receive an AI request from a user, it is processed by Bito's system (such as adding relevant context and determining the Large Language Model (LLM) to use). However, the original query is not retained. The request is then sent to a third-party LLM (e.g., OpenAI, Google Cloud, etc.). The LLM processes the prompt and return the response to Bito. Bito then receives the response, processes it (such as formatting), and returns it to the user’s machine.

For enterprises, we have the ability to connect to your own private LLM accounts, including but not limited to OpenAI, Google Cloud, Anthropic, or third-party services such as AWS Bedrock, Azure OpenAI. This way all data goes through your own accounts or Virtual Private Cloud (VPC), ensuring enhanced control and security.

Data and Business Privacy Policy

In line with Bito's commitment to transparency and adherence to data privacy standards, our comprehensive data and business privacy policy is integrated into our practices. Our complete Terms of Use, including the Privacy Policy, are available at , with our principal licensing information detailed at .

Data Retention Policy

Our data retention policy is carefully designed to comply with legal standards and to respect our customers' privacy concerns. The policy is categorized into four levels of data:

1. Relationship and Usage Meta Data: This includes all data related to the customer's interaction with Bito, such as address, billing amounts, user account data (name and email), and usage metrics (number of queries made, time of day, length of query, etc.). This category of data is retained indefinitely for ongoing service improvement and customer support.

2. Bito Business Data: Includes customer-created templates and settings. This data is terminated 90 days after the end of the business relationship with Bito.

3. Confidential Customer Business Data: This includes code, code artifacts, and other organization-owned data such as Jira, Confluence, etc. This data is either stored on-prem/locally on the customer’s machines, or, if in the cloud, is terminated at the end of the business relationship with Bito.

4. AI Requests: Data in an AI request to Bito’s AI system. AI requests are neither retained nor viewed by Bito. We ensure the confidentiality of your AI queries; Bito and our LLM partners do not store your code, and none of your data is used for model training. All requests are transmitted via HTTPS and are fully encrypted.

Sub-processor

Bito uses the following third-party services: Amazon AWS, Anthropic, Clearbit, Github, Google Analytics, Google Cloud, HelpScout, Hubspot, Microsoft Azure, Mixpanel, OpenAI, SendGrid, SiteGround, and Slack for infrastructure, support, and functional capabilities.

Personal Data

Bito follows industry standard practices for protecting your e-mail and other personal details. Our password-less login process - which requires one-time passcode sent to your e-mail for every login - ensures the complete security of your account.

If you have any questions about our security and privacy, please email

Bito’s 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

Generating the best possible response is as much science as art. The are built on the same technology as ChatGPT handles the science part. Crafting a good is the art part. The Templates in Bito takes the burden off your shoulder in being crafty. You can select a piece of code and use one of the eight prompts, whether you want to check the code for performance or add error handling. Behind-the-scene actor "Bito Prompt Manager" crafts a well-versed prompt that squeezes the best response out of the machine. You can also save your favorite prompts for quick access anytime. Check out .

Templates are accessible:

1- In the Bito panel

Click on the Templates button to expand or collapse Templates menu.

2- Using Slash / Command in Bito Chat Box

Type a forward slash / right at the start in the Bito chat box. Once you do, the template menu will open from where you can quickly select and use the template you want.

Want to narrow down your choices? Simply start typing after the / slash, and it'll only show you templates that match your words. And hey, you can also use the arrow keys, or Tab and Shift + Tab, to navigate the templates menu.

3- From the Code Editor's Context Menu

Select code, right click, and click Bito AI to access shortcuts

4- Through the Command Palette in Visual Studio Code

Go to View -> Command Palette -> Type "Bito" to access the templates

The following Loom demonstrates Standard Templates in Bito:

Bito includes the following standard templates out of the box.

🤯 Sick of typing out long prompts every time? 😩 Bito's got your back! Now, create custom prompt templates for all your frequently used prompts and save yourself some stress.

With "Create Prompt Template," you can create and save custom prompt templates for use in your IDE. By defining a custom template with a template name and prompt, Bito can execute the prompt as is on the selected code. With this feature, you can save time and streamline your workflow by quickly executing frequently used prompts without inputting them manually each time.

The custom prompt templates feature and standard prompt templates are located below the chatbox.

Here is a quick overview of the Custom Prompt Templates in Bito

Creating a Custom Prompt Template

1. Open Bito Plugin in your IDE

2. Below the chatbox, click on "New Template".

1. Enter the "Template Name" and "Prompt" for your custom template. You can use {{%code%}} as a macro to insert the selected code in your prompt. If this macro is not used, Bito will insert the selected code at the end of your prompt. Next, select the "Output Format". You currently have two options:

   1. Display in Bito panel (Default)

   2. Output in diff view

1. Click on "Create Template" to save your new custom template. All custom templates will appear below the chatbox alongside standard templates. You can create up to four custom templates.

Editing or Deleting a Custom Template

You can edit or remove the templates anytime by clicking on the three dots over the template that you want to edit or remove. Note that you can edit or remove the standard templates provided by Bito.

Using a Custom Template

1. Select any code that you want to execute the prompt.

2. Run the Custom Template by clicking it in the Bito Templates panel or from the IDE context menu.

3. Bito starts generating output

Here’s an example of how to execute prompts using your custom templates.

Let’s say you want to create a custom template to add a comment describing the logic behind the code. Here's how you can do it:

1. Below the chatbox, click on "New template".

2. Enter a name for your custom template, e.g. "Add Comment"

3. In the "Prompt" field, enter the following: "Please add a comment describing the logic behind the code. " and then click on "Create Template" to save your new custom template

1. Now, select the code to which you want to comment and click on the "Add Comment" template.

1. Bito adds the selected code at the end of the prompt and executes it via Bito AI.

How many repositories can Bito index?

Bito can index unlimited repositories for workspaces that have subscribed to our Team Plan. This feature is also coming soon for our Free Plan. But it will be limited to 10MB maximum indexable size of repository.

Why is the index creation taking a long time?

Bito takes time to thoroughly read your entire repository and understand it. This is completely normal. If your repository is a bit large, then it can take several hours to get .

Bito usually takes around 12 minutes per each 10MB of code to understand your repo.

Why is the answer not complete?

There is a limit on the amount of memory/context that can be used at a time to answer the question, so the answers sometimes may not cover all the code. To solve for this, restrict the questions by providing additional criteria like:

• In my code explain message_tokens in ai/request.js

Where can I see the status of my Index?

Open your project in VS Code or JetBrains IDEs. From the Bito plugin pane, click the laptop icon located in the top-right corner.

On this tab, you will see the status of your current project as well as the status of any other project that you indexed previously.

What happens if my IDE got closed while indexing is in progress?

In case you close the Visual Studio Code or JetBrains IDE (e.g., PyCharm) while the is in progress then don’t worry. The indexing will be paused and will automatically continue from where it left off when you reopen the IDE. Currently, the indexing will resume 5-10 minutes after reopening the IDE.

How to delete project index from IDE?

1. To delete an index, navigate to the "Manage repos" tab.
2. Next, click on the three dots button located in front of your project’s name, and then select the "Delete" option.
3. A warning popup box will appear at the bottom of Bito's plugin pane. You can choose to click the "Delete" button to remove the project's index from your system, or click the "Cancel" button to go back.

How to fix indexing issues in Visual Studio Code and JetBrains IDEs (e.g., IntelliJ IDEA, PyCharm, etc.)?

If for some reason you are struggling to index your project’s folder while using Visual Studio Code or JetBrains IDEs, then follow the below steps to delete the folder that contains all the indexes and try to re-index your project.

1. Close all JetBrains IDEs and VS Code instances where Bito is installed.

2. Go to your users directory. For example, on Windows it will be something like C:\Users\<your username>

3. Now, find .bito folder and delete it. (Note: All configuration settings and project indexes created by Bito will be deleted. You will also be logged out from Bito IDE plugin)

1. Once you have deleted the .bito folder, open your project in the IDE again.

2. After restarting the IDE, you will need to enter your email ID and a 6-digit code to log in. Once you're logged in, select the workspace that has an active paid subscription.

3. After that, when Bito asks if you wish to index the folder, you can select "Maybe later".

4. Then, navigate to the "Manage repos" tab in the Bito plugin pane, where you should see the folder name listed under the "Current project" along with its size, indicating that it is not indexed. Since you have deleted the .bito folder, the "Other projects" section will no longer display any entries.

5. Finally, click on "Start Indexing" and it should begin indexing the folder.

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

4. Make the file executable using following command chmod +x ./bito

5. Copy the binary to /usr/local/bin using following command sudo cp ./bito /usr/local/bin

6. Set PATH variable so that Bito CLI is always accessible. PATH=$PATH:/usr/local/bin

7. Run Bito CLI with bito command. If PATH variable is not set, you will need to run command with the complete or relative path to the Bito executable binary.

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.

4. Set PATH variable so that Bito CLI is always accessible.

   1. Follow the instructions as per this

   2. Edit the "Path" variable and add a new path of the location where Bito CLI is installed on your machine.

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.

5. Follow the instructions provided by the uninstall wizard to complete the uninstallation process.

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

Step-by-Step Instructions

1. to open the Bito Chrome Extension page.

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.

In Bito, collaboration happens within a Workspace, where team members are assigned roles and access based on their responsibilities. In most cases, every organization would create one Workspace. Anyone can , create a workspace for their team, and invite their coworkers to join the Workspace.

The dashboard introduces a clear, flexible interface for managing user access and feature seats across your team.

Seat management overview

At the top of the Members dashboard, you’ll see a summary of your seat usage and assignment status:

• Seats purchased: Displays the total number of seats your workspace has purchased and the total billing amount.

• Seats assigned: Shows how many of those seats are assigned for:

  • Code Review Agent

  • AI Chat/Wingman

• Seat assignment mode:

  • Auto: In this mode, available seats are automatically assigned to developers when they join the workspace or when they submit their first pull request reviewed by Bito. If all seats are assigned, new seat is purchased and assigned automatically.

    • Note: This mode is useful for dynamic teams where new contributors are added frequently.

  • Manual: In this mode, workspace admins need to manually purchase seats and assign them to developers as needed. Bito will review pull requests only for submitters who have an assigned seat.

    • Note: This mode is ideal for teams that want tighter control over who gets access and when billing occurs.

You can switch between Auto and Manual modes based on your team's seat allocation preferences.

Managing members by feature

Below the seat overview, you'll find three tabs to manage different types of access:

1. Code Review Agent tab

Assign or unassign seats to members specifically for the Code Review Agent feature. Each member listed here can be toggled on or off depending on whether you want to allocate a seat for this feature.

2. AI Chat/Wingman tab

Similar to the Code Review Agent tab, this tab lets you assign or remove access to Bito's AI Chat and Wingman tools. You can also to join the workspace.

3. Admin tab

This tab is dedicated to managing administrative roles within the workspace. Only members with elevated permissions are shown here.

This tab displays a table with the following information:

• Name: Displays the full name and email address of the member.

• Role: A dropdown that allows you to set or update the user’s administrative role:

  • Owner: Full control over the workspace.

  • Admin: Access to most workspace management functions.

  • + Billing contact: (Checkbox) Receives billing-related communications.

  • Billing only: (Button) Limits the member to billing management tasks.

Additional options:

Each admin row has a three-dot menu offering:

• Remove from Admin members: Revoke administrative privileges.

• Remove from workspace: Completely remove the user from the workspace.

Inviting coworkers to the Workspace

You can use Bito in a single-player mode for all the use cases. However, it works best when your coworkers join the Workspace to collaborate with Bito. There are three ways you can invite your coworkers.

Option 1 - Allow your work e-mail domain for the Workspace. This setting is turned on by default, and all users with the same e-mail domain as yours will automatically see the Workspace under "Pending Invitations" when signing up in Bito. You can manage this setting after you create the Workspace through the "Settings" page in your Bito account.

Option 2 - Invite your coworkers via e-mail when you create your Workspace or later from your workspace setting.

Option 3- Share a web link specific to your Workspace via the channel of your choice: e-mail, Slack, or Teams. The link is automatically created and shown when creating a workspace or on the workspace settings page.

Adding Admin members

To add a new admin:

1. Click the “Add members” button at the top of the Admin tab.

2. In the popup:

   • Select an existing user from your workspace, or

   • Invite a new member by entering their email address.

3. Assign the appropriate role and permissions as needed.

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.

Why is it Important?

Generative AI, like OpenAI’s GPT models, are revolutionizing industries from content creation to coding. But their utility hinges on the quality of the prompts they receive. A well-engineered prompt can yield rich, accurate, and nuanced responses, while a poor one can lead to irrelevant or even nonsensical answers.

The Anatomy of a Good Prompt

Clarity and Specificity

AI models are literal. If you ask for an article, you'll get an article. If you ask for a poem about dogs in space, you’ll get exactly that. The specificity of your request can significantly alter the output.

Example:

• Vague Prompt: Write about health.

• Engineered Prompt: Write a comprehensive guide on adopting a Mediterranean diet for improving heart health, tailored for beginners.

Contextual Information

Providing context helps the AI understand the nuance of the request. This could include tone, purpose, or background information.

Example:

• Without Context: Explain quantum computing.

• With Context: Explain quantum computing in simple terms for a blog aimed at high school students interested in physics.

Closed vs. Open Prompts

Closed prompts lead to specific answers, while open prompts allow for more creativity. Depending on your goal, you may need one over the other.

Example:

• Closed Prompt: What is the capital of France?

• Open Prompt: Describe a day in the life of a Parisian.

The Practice of Prompt Engineering

Prompt engineering is not a "get it right the first time" kind of task. It involves iterating prompts based on the responses received. Tweaking, refining, and even overhauling prompts based on output can lead to more accurate and relevant results.

A significant part of prompt engineering is experimentation. By testing different prompts and studying the outputs, engineers learn the nuances of the AI's language understanding and generation capabilities.

Keywords are the bread and butter of prompt engineering. Identifying the right keywords can steer the AI in the desired direction.

Example:

• Without Keyword Emphasis: Write about the internet.

• With Keyword Emphasis: Write an article focused on the evolution of internet privacy policies.

Advanced Techniques

Chain of Thought Prompts

These prompts mimic a human thought process, providing a step-by-step explanation that leads to an answer or conclusion. This can be especially useful for complex problem-solving.

Example:

• Chain of Thought Prompt: To calculate the gravitational force on an apple on Earth, first, we determine the mass of the apple and the distance from the center of the Earth...

Zero-Shot and Few-Shot Learning

In zero-shot learning, the AI is given a task without previous examples. In few-shot learning, it’s provided with a few examples to guide the response. Both techniques can be leveraged in prompt engineering for better results.

Example:

• Zero-Shot Prompt: What are five innovative ways to use drones in agriculture?

• Few-Shot Prompt: Here are two ways to use drones in agriculture: 1) Crop monitoring, 2) Automated planting. List three more innovative ways.

Ethical Considerations and Limitations

• Bias and Sensitivity: Prompt engineers must be mindful of inherent biases and ethical considerations. This includes avoiding prompts that could lead to harmful outputs or perpetuate stereotypes.

• Realistic Expectations: LLMs and Generative AI are powerful but not omnipotent. Understanding their limitations is crucial in setting realistic expectations for what prompt engineering can achieve.

• Data Privacy and Security: As prompts often contain information that may be sensitive, engineers must consider data privacy and security in their designs.

Conclusion

Prompt engineering is more than a technical skill—it’s a new form of linguistic artistry. As we continue to integrate AI into our daily lives, becoming adept at communicating with these systems will become as essential as coding is today.

Whether you’re a writer, a developer, or just an AI enthusiast, mastering the craft of prompt engineering will place you at the forefront of this exciting conversational frontier. So go ahead, start crafting those prompts, and unlock the full potential of your AI companions.

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:

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:

• Retrieval: Before generating any new text, the RAG model retrieves information from a large dataset or database. This could be anything from a simple database of facts to an extensive library of books and articles.

• Augmented: The retrieved information is then fed into a generative model to "augment" its knowledge. This means the generative model doesn't have to rely solely on what it has been trained on; it can access external data for a more informative output.

• Generation: Finally, the model generates text using both its pre-trained knowledge and the newly retrieved information, leading to more accurate, detailed, and relevant responses.

The Components of a RAG Model

A RAG model typically involves two major components:

1. Document Retriever: This is a neural network or an algorithm designed to sift through the database and retrieve the most relevant documents based on the query it receives.

2. Sequence-to-Sequence Model: After retrieval, a Seq2Seq model, often a transformer-based model like BERT or GPT, takes the retrieved documents and the initial query to generate a coherent and relevant piece of text.

How to Build a RAG

Let's imagine we want to build a RAG model that, when given a prompt about a historical figure or event, can generate a detailed and accurate paragraph.

Step 1: Choose Your Data Source

First, you need a database from which the model can retrieve information. For historical facts, this could be a curated dataset like Wikipedia articles, historical texts, or a database of historical records.

Step 2: Index Your Data Source

Before you can retrieve information, you need to index your data source to make it searchable. You can use software like Elasticsearch for efficient indexing and searching of text documents.

Step 3: Set Up the Retriever

You then need a retrieval model that can take a query and find the most relevant documents in your database. This could be a simple TF-IDF (Term Frequency-Inverse Document Frequency) retriever or a more sophisticated neural network-based approach like a Dense Retriever that maps text to embeddings.

Step 4: Integrate with a Generative AI Model

The retrieved documents are then fed into a generative AI model, like GPT-4o or BERT. This model is responsible for synthesizing the information from the documents with the original query to generate coherent text.

Step 5: Training Your RAG Model

If you're training a RAG model from scratch, you'd need to fine-tune your generative AI model on a task-specific dataset. You’d need to:

• Provide pairs of queries and the correct responses.

• Allow the model to retrieve documents during training and learn which documents help it generate the best responses.

Step 6: Iterative Refinement

After initial training, you can refine your model through further iterations, improving the retriever or the generator based on the quality of outputs and user feedback.

Building such a RAG system would be a significant engineering effort, requiring expertise in machine learning, NLP, and software engineering.

Why RAG is a Game-Changer

RAG significantly enhances the relevance and factual accuracy of text generated by AI systems. This is due to its ability to access current databases, allowing the AI to provide information that is not only accurate but also reflects the latest updates.

Moreover, RAG reduces the amount of training data needed for language models. By leveraging external databases for knowledge, these models do not need to be fed as much initial data to become functional.

RAG also offers the capability to tailor responses more specifically, as the source of the retrieved data can be customized to suit the particular information requirement. This functionality signifies a leap forward in making AI interactions more precise and valuable for users seeking information.

Practical Applications of RAG

The applications of RAG are vast and varied. Here are a few examples:

• Customer Support: RAG can pull up customer data or FAQs to provide personalized and accurate support.

• Content Creation: Journalists and writers can use RAG to automatically gather information on a topic and generate a draft article.

• Educational Tools: RAG can be used to create tutoring systems that provide students with detailed explanations and up-to-date knowledge.

Challenges and Considerations

Despite its advantages, RAG also comes with its set of challenges:

• Quality of Data: The retrieved information is only as good as the database it comes from. Inaccurate or biased data sources can lead to flawed outputs.

• Latency: Retrieval from large databases can be time-consuming, leading to slower response times.

• Complexity: Combining retrieval and generation systems requires sophisticated machinery and expertise, making it complex to implement.

Conclusion

Retrieval Augmented Generation is a significant step forward in the NLP field. By allowing machines to access a vast array of information and create something meaningful from it, RAG opens up a world of possibilities for AI applications.

Whether you're a developer looking to build smarter AI systems, a business aiming to improve customer experience, or just an AI enthusiast, understanding RAG is crucial for advancing in the dynamic field of artificial intelligence.

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

5. /review codeoptimize

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

Set Your Own Keyboard Shortcuts in VS Code

In VS Code settings, you can customize the keyboard shortcuts for AI Code Completions feature according to your preferences. To do so, follow the below steps:

1. Click the gear icon at the bottom left of the VS Code window. Then, select “Keyboard Shortcuts” to view all the keyboard shortcuts used by VS Code and its extensions.

1. In the search bar, type "bito" to view all the keyboard shortcuts used by the Bito extension.

1. Find the command for which you want to change the keyboard shortcut. Then, click on the edit icon in front of it.

1. A popup modal will appear. Enter your new key combination and press the Enter button to save it.

Reset Keyboard Shortcut to Default Value in VS Code

If you change a keyboard shortcut and want to revert to the original, just right-click on the specific command. A menu will pop up. Choose "Reset Keybinding" from this menu.

Set Your Own Keyboard Shortcuts in JetBrains

In JetBrains IDE settings, you can customize the keyboard shortcuts for the AI Code Completions feature according to your preferences. To do so, follow the steps below:

1. Click the gear icon at the top right of the JetBrains IDE window, then select "Settings" to open the settings window.
2. In the settings window, click on the "Keymap" button given in the left sidebar. Then, in the search bar, type "bito" to view all the keyboard shortcuts used by the Bito extension.
3. Find the command for which you want to change the keyboard shortcut and right-click on it. Then select "Add Keyboard Shortcut".
4. A popup modal will appear. Enter your new key combination and click the "OK" button to save it.
5. Now you will have more than one keyboard shortcuts assigned to a command. To remove the previously set keyboard shortcut, right-click on the command again. From here, you can remove the desired keyboard shortcut by clicking on the "Remove [keyboard_shortcut_here]" button.
6. After doing all of the above steps, you must click on "Apply" and then "OK" button to save your changes. Otherwise, your modifications will be lost.

Reset Keyboard Shortcut to Default Value in JetBrains

If you change a keyboard shortcut and want to revert to the original, just right-click on the specific command. A menu will pop up. Choose "Reset Shortcuts" from this menu.

After resetting the shortcut, you must click on "Apply" and then "OK" button to save your changes. Otherwise, your modifications will be lost.

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

JetBrains Document:

1. File > settings > keymaps > configure keymaps

1. Bito extension shortcuts can be overwritten by going into the File > Settings > Keymaps > configure keymaps > to the action you want to assign. It will also overwrite the Bito shortcut if there are conflicts.

2. Bito extension keyboard shortcuts can be changed from the IntelliJ settings. File > Settings > Keymaps > configure keymaps > plugins > Bito > action you want to change by right click.

3. Bito extension Keyboard shortcuts can be deleted from the IntelliJ settings. File > Settings > Keymaps > configure keymaps > plugins > Bito > action you want to delete by right click.

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.
• From here you can start the by clicking on the “Start Indexing” button. Here, you will also see the total indexable size of the repository. Read more about

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:

1. Open your project’s folder using a JetBrains IDE. For this guide, we are using PyCharm.

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.
• From here you can start the by clicking on the “Start Indexing” button given in front of your current project. Here, you will also see the total indexable size of the repository. Read more about