Enhance your code reviews with deeper codebase intelligence by connecting Bito's AI Code Review Agent to AI Architect. This integration enables significantly more accurate and context-aware reviews by leveraging the comprehensive knowledge graph that AI Architect builds from your codebase.

What this integration provides

When AI Code Review Agent accesses AI Architect's knowledge graph, it gains a complete understanding of your codebase architecture — including microservices, modules, APIs, dependencies, and design patterns.

This enhanced context allows the AI Code Review Agent to:

• Provide system-aware code reviews - Understand how changes in one service or module impact other parts of your system

• Catch architectural inconsistencies - Identify when new code doesn't align with your established patterns and conventions

• Detect cross-repository issues - Spot problems that span multiple repositories or services

• Deliver more accurate suggestions - Generate fixes that are grounded in your actual codebase structure and usage patterns

• Reduce false positives - Better understand context to avoid flagging valid code as problematic

Follow these steps to connect AI Architect with AI Code Review Agent:

1. Log in to

2. Open the dashboard.

3. In the Server URL field, enter your Bito MCP URL

4. In the Auth token field, enter your Bito MCP Access Token

Contact our team at to request a trial. We'll help you configure the integration and get your team up and running quickly.

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.

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

1. and select a workspace to get started.

2. From the left sidebar, select .

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

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

The supports two deployment options:

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.

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.

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

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

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

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

Copy the answer to the clipboard.

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.

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.

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

Launch Windsurf IDE on your computer.

From the top menu, click View -> Extensions to open the Extensions marketplace.

1. In the Extensions search bar at the top, type "Bito"

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

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

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.

To ensure the operates smoothly with your GitHub (Self-Managed), GitLab (Self-Managed), or Bitbucket (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

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

Launch Cursor IDE on your computer.

From the top menu, click View -> Extensions to open the Extensions marketplace.

1. In the Extensions search bar at the top, type "Bito"

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

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

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

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

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

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

Follow the steps below to get started:

1. and select a workspace to get started.

2. From the left sidebar, select .

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.

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.

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

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 adapted from “Noctis Lux”:

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.

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.

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.

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.

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.

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.

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

• 我的代码

• 我的仓库

• 我的代码库

• 我的项目

• 我的程式碼

• 我的倉庫

• 我的項目

• 我的工作區

• Mi código

• Mi repo

• Mi proyecto

• Mi espacio de trabajo

• 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

• Meu código

• Meu repo

• Meu projeto

• Meu espaço de trabalho

• Mój obszar roboczy

• moje miejsce pracy

• mój obszar roboczy

• moj kod

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

Video guide

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

Step-by-step instructions

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

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

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

This guide will help you set up and start using Bito's AI-powered tools. Whether you're looking to enhance feedback of your coding agents with AI Architect or automate code reviews with our AI Code Review Agent, you'll find everything you need below.

AI Architect

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 Available Keywords 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

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

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

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

• Find logical errors in scraper.py in my code

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

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

• 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 in my code

• 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.

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

The AI Code Review Agent becomes significantly more powerful when paired with AI Architect.

Below is a clear explanation of how the agent behaves in each setup and why AI Architect unlocks much deeper, system-level insights.

AI Code Review Agent without AI Architect

The standard AI Code Review Agent analyzes code at the repository level.

It creates a within-repo knowledge graph by building:

• Abstract Syntax Trees (ASTs)

• Symbol indexes

• Local dependency relationships

This allows it to perform strong, context-aware code reviews within a single repository, including:

• Identifying issues in the diff

• Understanding dependencies inside the repo

• Checking for consistency and correctness within that project

• Suggesting improvements based on local patterns

However, the agent’s visibility stops at the repository boundary. It cannot detect effects on other services or codebases.

When AI Architect is enabled, the AI Code Review Agent gains a complete view of your entire engineering ecosystem.

AI Architect builds a cross-repository knowledge graph that maps:

• All services

• Shared libraries

• Modules and components

• Inter-service dependencies

With this system-level understanding, the agent can perform much deeper analysis.

1. Cross-repository awareness

The agent understands how code in one repo interacts with code in others — crucial for microservices and distributed systems.

2. Cross-repo impact analysis

During a pull request review, the agent can identify:

• What breaks downstream if you change an interface

• Which services call the function you updated

• Which teams or repos depend on your changes

• Whether the update introduces architecture-wide risks

3. Architecture-level checks

The agent evaluates your changes not just for correctness, but for their alignment with the overall system design.

4. Early problem detection across the entire codebase

Ripple effects, breaking changes, or dependency violations that traditionally appear only in staging or after deployment can now be flagged directly during review.

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

• • 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>

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.

Security and privacy are fundamental to how Bito's AI Architect operates. Bito doesn't store your code and we don't use your code for AI model training.

This document explains how AI Architect handles your code and data across different deployment modes.

Code storage: Bito-hosted vs. Self-hosted

The way AI Architect handles your code depends on your deployment choice:

Bito-hosted AI Architect

Your code is not stored on Bito's servers.

When you use Bito-hosted AI Architect, Bito stores only summaries and indexes of your code — not the code itself. These indexes help AI Architect understand your repository structure, key functionalities, service calls, design patterns, variable naming conventions, and architectural relationships across microservices.

How it works:

• AI Architect analyzes your code to build a knowledge graph

• Only metadata and summaries are stored on Bito's servers (e.g., "this repo contains authentication services," "this module calls these other services")

• The actual code is never persisted in Bito's cloud

When code access is needed:

• If you ask a question that requires viewing actual code, AI Architect authenticates with your Git provider using your credentials

• It fetches the specific file on-demand

• Extracts the necessary information

• Passes it back to you through the MCP server

For performance optimization, Bito temporarily caches individual code files for up to 10 minutes during analysis. You have full control over this behavior — you can configure the cache duration or disable caching entirely through your settings.

Your code remains entirely in your data center.

When you deploy AI Architect on-premises in your own infrastructure, all code and indexes are stored locally. Bito receives no code or code-related information. We only receive anonymous metadata about usage to confirm the product is working properly — no details about what your code does or contains.

Bito-hosted AI Architect adds a few seconds to requests that require actual code access because the code must be fetched on-demand from your Git provider. This slight latency is the trade-off for keeping your code off Bito's servers. In practice, this difference is not material to user experience.

• All requests to and from AI Architect are transmitted over HTTPS and fully encrypted

• Your Git credentials are used only to authenticate and fetch code when needed

• Code accessed during analysis is handled securely and not logged or stored beyond the temporary cache window

Your code is never used for AI model training. Bito doesn't train on, learn from, or retain any of your code snippets or queries for model improvement purposes.

Bito uses leading AI providers (such as Anthropic, OpenAI, etc.) via their APIs to power AI Architect features. None of your code or AI requests are stored by these partners. All AI providers we work with maintain commitments to not use API data for model training or retain user data beyond the immediate request processing.

To ensure AI Architect is working correctly and to improve the product, Bito collects:

• Anonymous usage metrics

• Feature usage statistics

• Error logs (without code content)

This information helps us understand how AI Architect is being used and identify areas for improvement.

Bito maintains SOC 2 Type II compliance and follows industry-leading security practices. For detailed information about our security posture and certifications, visit our .

For our full privacy practices, see our .

If you have questions about how AI Architect handles your code or data, contact the Bito team at . We're committed to transparency about security and privacy practices.

can be self-hosted in two ways depending on your use case. Pick the one that matches how you plan to use it:

A lightweight, single-machine install for individual developers who want to evaluate AI Architect or use it for personal development work. It runs entirely in Docker on your local machine (e.g., laptop) and automatically registers itself with your installed coding agents — no shared infrastructure, no DevOps coordination required.

👉

A full-featured, multi-user deployment of Bito's AI Architect designed for teams who want to share a centralized knowledge graph across the organization. It supports both Docker Compose and Kubernetes, can be hosted on any on-premise or cloud infrastructure you control, and includes SSO authentication.

👉

Use Bito's with ChatGPT (Web & Desktop) to enhance your AI-powered coding experience.

Once connected via MCP (Model Context Protocol), ChatGPT can leverage AI Architect's deep contextual understanding of your project, enabling more accurate code suggestions, explanations, and code insights.

1. Follow the . Upon successful setup, you will receive a Bito MCP URL and Bito MCP Access Token that you need to enter in your coding agent.

AI Architect is a full engineering workflow platform built around a knowledge graph of your codebase, business context, tribal knowledge, docs, tickets, and observability data. It operates across three solution areas — technical design & scoping, grounded coding, and code review — surfacing system-level intelligence at every stage of development.

This page covers the core features that power those workflows.

The knowledge graph is the context engine at the core of AI Architect. It ingests your codebase, Git history, issue tracker data (Jira, Linear), documentation (Confluence), observability signals, and Slack conversations into a unified, continuously updated graph.

Rather than treating your codebase as searchable text, the knowledge graph models the relationships between services, APIs, dependencies, past decisions, and recurring incident patterns — capturing how your system actually fits together.

This shared context powers every capability in AI Architect. When an agent runs a feasibility analysis, generates a technical design, or reviews a pull request, it draws from the same knowledge graph — so output is grounded in your real architecture, not generic patterns.

The knowledge graph also enables cross-repo reasoning: it understands blast radius, tracks instability histories, and connects a Jira incident from six months ago to the service a developer is about to change today.

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.

• Go to your repository → Settings → Branches

Bito's Governor is a gateway that sits between your AI coding tools (Claude Code, Cursor, Codex, etc.) and the LLM providers (Anthropic, OpenAI, Groq, etc.). It reduces the tokens each task consumes and the price you pay for every token.

Governor combines with a smart model router:

• Bito's AI Architect cuts the token count. It serves system context from a live of your entire engineering system (code, business context, and tribal knowledge), so your tools skip the discovery work that consumes most of a task's tokens.

• The model router cuts the price per token. It uses the same knowledge graph to see how complex each request is, then sends it to the cheapest LLM that can handle it, because most requests do not need a frontier model.

Bito's supports different configuration methods depending on the deployment environment:

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

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

The sections below provide configuration guidance for each setup.

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

Bito’s is the first agent built with Bito’s AI Agent framework and engine. It is an automated AI assistant (powered by best-in-class AI models) 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

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

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

The easiest way to start your trial is through natural interaction:

1. Type a message in the Bito chat box and send it.

2. Look for the popup that appears after sending your message.

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.

This page details exactly how the Bito Slack app handles, stores, and processes your data. All policies stated here apply specifically to data flowing through the Bito Slack integration.

Data Retention Policy

• Conversational data (messages sent to @Bito, slash command inputs, and AI responses) is not stored by Bito. Only logs are retained for 15 days and are automatically deleted afterward.

• Workspace metadata (workspace ID, installer identity, app configuration): retained for the duration of the active installation.

• Account-level data: see the .

Upon uninstallation, all data is permanently deleted except for logs. Logs are retained for 15 days and then automatically deleted, except where retention is required by law.

We do not archive messages sent to @Bito, slash command inputs, or AI responses. Only logs are retained in AWS S3 for 15 days and then automatically deleted.

• Primary storage: AWS, US-East region (default). EU residency available for Enterprise customers on request.

• Encryption at rest: AES-256

• Encryption in transit: TLS 1.2 or higher

• Access controls: RBAC with mandatory SSO + MFA for employees

1. Email with subject "Slack Data Deletion Request"

2. Include workspace name, workspace ID, and admin verification

3. Bito verifies within 2 business days

4. Deletion is completed within 2 weeks of verification

Deletion covers all conversational data and workspace metadata in production systems. Backup purging follows the next rotation cycle.

Bito for Slack uses third-party LLMs selected by task and plan:

• OpenAI

• Anthropic

• Your message content to @Bito

• Relevant thread context (only when @Bito is mentioned in a thread)

• System instructions

Never sent: Slack user emails/names beyond attribution, messages from channels where @Bito is not invited, private DMs not involving @Bito.

Your data is never used to train LLM models. Bito's contracts with LLM providers explicitly prohibit use of customer prompts/outputs for training. Provider-side retention is limited to abuse-monitoring (typically ≤30 days) per their enterprise policies.

LLM API calls are routed to US endpoints by default. Enterprise customers may request EU-only processing via .

• Visible AI disclaimer on responses

• Rate limiting and abuse detection

• LLM-vendor content moderation

• Feedback / human escalation via

Bito for Slack is powered by large language models (LLMs). AI-generated responses may be inaccurate, incomplete, biased, or outdated. Treat outputs as informational suggestions, not as professional, legal, financial, medical, or compliance advice. Always verify critical information before acting on it.

Report inaccurate responses to .

1. In Slack, go to Settings → Manage Apps → Bito → Remove

2. Upon uninstallation, all data is permanently deleted except for logs. Logs are retained for 15 days and then automatically deleted, except where retention is required by law.

3. To confirm or expedite deletion, email

Bito's AI Architect plugin brings cross-repository intelligence to Claude Code. Search code, explore dependencies, analyze architecture, and discover patterns across all your organization's repositories. Use it to build implementation plans, write requirements documents, diagnose production issues, and understand complex codebases.

It enables users to explore and understand any codebase — from high-level architecture to line-level code traces — calibrated for multiple personas (Developer, EM, PM, VP, CTO).

Key capabilities:

• Deep codebase exploration across multiple repositories

• AI-powered architecture analysis and documentation

• Production incident triage with blast radius mapping

• Feature planning grounded in real system context

Run this command in Claude Code:

Run the interactive setup:

This command guides you through configuring your workspace ID, bearer token, and email.

Alternative: manual configuration

Set these environment variables manually:

After configuration, restart Claude Code and run /mcp to verify the server is connected.

Once installed, access these slash commands in Claude Code:

Use the codebase explorer to understand any repository:

The explorer uses a 4-phase workflow to analyze architecture, dependencies, and code patterns. Choose your persona (Developer, EM, PM, VP, CTO) to get insights calibrated to your role.

Create an implementation plan with cross-repo context:

The plugin analyzes your codebase to identify affected components, dependencies, and potential conflicts.

Diagnose incidents across repositories:

Get cross-repo context and blast radius mapping to understand the full impact of production issues.

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.

By default, /review performs an incremental review. It analyzes only the changes made since the last review (such as new commits pushed after a previous review) rather than re-reviewing the entire pull/merge request. This keeps feedback focused on what's new and avoids repeating suggestions on code that was already reviewed.

/review full

The /review full command performs a manual full review of the pull/merge request. Instead of looking only at the changes since the last review, it re-analyzes the entire set of changes in the request from scratch.

Use case: Use /review full when you want a complete re-review of all changes. For example, after significant rework, or when you want the Agent to reassess the whole request rather than only the latest commits.

Example: Add a comment with /review full to the pull/merge request.

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

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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

This document explains some of Bito's privacy and security practices. Our Trust Center outlines our various accreditations (SOC 2 Type II) and our various security policies. You can read our full Privacy Policy at https://bito.ai/privacy-policy/.

Bito AI

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

All requests are transmitted over HTTPS and are fully encrypted.

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.

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

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.

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.

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 .

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.

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.

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.

The Bito Slack app has its own dedicated data handling page covering retention, deletion, LLM details, and uninstall procedures specific to Slack data.

→

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

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

Available templates

1. Performance Check: Optimize code performance and efficiency

2. Security Check: Identify and fix security vulnerabilities

3. Style Check: Apply coding style and formatting standards

4. Improve Readability: Enhance code clarity and organization

5. Clean Code: Remove debugging and logging statements

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

1. Select code in your editor

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

1. Choose the desired template from the dropdown menu

1. Select code in your editor

2. Right-click in the editor window

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

4. Select the desired template from the submenu

1. Select code in your editor

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

3. Choose the desired template from the dropdown menu

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

1. Select code in your editor

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

3. Type "bito" to see available templates

4. Select the desired template from the list

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

1. Click the Apply button to open the diff view

1. Review the changes highlighted in the diff:

   • Red lines show code to be removed

   • Green lines show code to be added

2. Choose your action:

• Select meaningful code blocks for better analysis results

• Templates work best with complete functions or logical code segments

• Review suggested changes before applying them to your codebase

• Verify that the changes don't break existing functionality

Use Bito's AI Architect with Claude Desktop to enhance your AI-powered coding experience.

Once connected via MCP (Model Context Protocol), Claude Desktop can leverage AI Architect's deep contextual understanding of your project, enabling more accurate code suggestions, explanations, and code insights.

Prerequisites

1. Follow the AI Architect installation instructions. Upon successful setup, you will receive a Bito MCP URL and Bito MCP Access Token that you need to enter in your coding agent.

2. Download BitoAIArchitectGuidelines.md file. You will need to copy/paste the content from this file later when configuring AI Architect.

   • Note: This file contains best practices, usage instructions, and prompting guidelines for the Bito AI Architect MCP server. The setup will work without this file, but including it helps AI tools interact more effectively with the Bito AI Architect MCP server.

3. Claude Desktop installed - Download and install Claude Desktop from if you haven't already.

4. Node.js 20.18.1 or higher - Required for the mcp-remote proxy

   • macOS:

   • Windows: Download from (download 20.x LTS)

Claude Desktop uses claude_desktop_config.json with the mcp-remote proxy for OAuth-enabled remote servers.

Claude Desktop supports both local MCP servers and remote HTTP servers. For Bito AI Architect (a remote OAuth server), we use the mcp-remote proxy which handles the OAuth flow automatically.

1. Verify JSON syntax in config file

2. Ensure Node.js 20+ is installed: node --version

3. Check that npx is available: npx --version

1. Ensure your browser is set as default

2. Allow pop-ups for the OAuth flow

3. Check firewall settings

• Ensure Node.js is in your PATH

• Try running npx -y mcp-remote --help in terminal to verify it works

• OAuth tokens may have expired - restart Claude Desktop to re-authorize

• Check your internet connection

• Verify the Workspace ID is correct

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:

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.

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.

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:

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.

The Architect MCP Analytics dashboard in Bito Cloud provides visibility into how Bito's AI Architect is being used across your workspace through the Model Context Protocol (MCP). Use these metrics to track tool usage, monitor engagement, and understand what tasks the AI Architect is being applied to.

All charts update on a rolling basis and are accessible directly from the Bito UI.

Overview

Each time the AI Architect invokes a structured capability — such as code search, file reading, or architecture analysis — it is recorded as a tool call. The analytics page aggregates these tool calls to help you answer questions like:

• How much is the AI Architect being used?

• Who is using it, and how many people are actively engaged?

• Which types of tasks is it being used for most?

• Is usage growing or declining over time?

Displays the total number of MCP tool calls made by all users in the last 4 weeks.

Use this as a high-level indicator of overall AI Architect activity. A rising count suggests growing adoption or deeper integration into developer workflows. A sudden drop may point to access issues, a change in team workflow, or a degraded experience worth investigating.

Shows the number of unique workspace members who triggered at least one MCP tool call within the last 4 weeks.

This metric reflects the breadth of adoption rather than total volume. Compare it alongside tool call volume to understand usage distribution:

• High volume + low active users → a few power users driving most activity

• Growing active users + stable volume → broader but lighter adoption across the team

Breaks down tool call volume week by week over the last 8 weeks.

The longer time horizon makes it easier to identify growth trajectories, plateau periods, or regressions following a product change or team event. Use this chart to compare performance before and after AI Architect rollouts, onboarding pushes, or feature updates.

Segments tool calls by their functional category, showing which types of tasks the AI Architect is most frequently being used for. Volume is displayed over time so you can track how the purpose distribution shifts within the 4-week window.

Use this chart to identify which Architect capabilities are most valued by your team and where adoption of specific features may need support or enablement.

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

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.

Please follow the instructions given in the links below:

Video Guide:

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.

Please follow the instructions given in the links below:

Video Guide:

Bito integrates with Confluence to enhance functional validation by enriching pull request requirements with deeper context from linked documentation.

It fetches relevant details from Confluence pages linked in Jira issues or directly in pull request – such as edge cases, design decisions, or test scenarios – and incorporates them into structured validation results.

This improves accuracy when validating code changes and ensures that every pull request is aligned with the requirements.

When a pull request is opened, Bito automatically:

1. Detects Confluence references. Bito looks for Confluence page links in the pull request description or in any linked Jira issues.

Bito's Knowledge graph is the context engine behind that captures your entire engineering system. It ingests your codebase, business context, and tribal knowledge into a unified graph, giving AI and dev tools a shared understanding of the system.

This always-updated graph powers smarter planning, grounded code generation, and faster problem-solving across a variety of tools, including:

• Issue trackers (Jira, Linear)

• Coding agents (Claude Code, Cursor, etc.)