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 CLI vs webhooks service 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. Install/run via CLI: Ideal for developers seeking a simple, interactive way to conduct code reviews from the command line.

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

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

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

Bito's AI Architect builds a knowledge graph of your codebase (from repos to modules to APIs) and operational history (context from issue trackers such as Jira). This gives your team a shared, always-current understanding of the system, and makes that understanding available wherever engineering decisions are made:

• In issue trackers (Jira) for feasibility analysis, technical design, and cross-repo impact assessment.

• In coding agents (Claude Code, Cursor, Windsurf, GitHub Copilot, and more) for grounded code generation, accelerated onboarding, and production issue triage.

• (GitHub, GitLab, Bitbucket) for codebase-aware AI code reviews

This fundamentally changes the game for enterprises with many microservices or large, complex codebases.

• Technical design and planning — AI Architect analyzes your Jira tickets and posts a complete implementation plan directly in comments section: feasibility assessment, story breakdown, effort estimates, risk flags, and dependency mapping.

• Grounded 1-shot production-ready code — The AI Architect learns all your services, endpoints, code usage examples, and architectural patterns. The agent automatically feeds those to your coding agent (Claude Code, Cursor, Codex, any MCP client) to provide it the necessary information to quickly and efficiently create production ready code.

The AI Architect builds the knowledge graph by analyzing all your repositories (whether you have 50 or 5,000 repos) to learn about your codebase architecture, microservices, modules, API endpoints, design patterns, and more.

AI Architect is designed to be flexible and can power multiple use cases across different AI coding tools and workflows.

Most AI coding tools struggle with accuracy in real-world codebases because they

1. Don’t fully understand the breadth and depth of your codebase. They read some of the code in your existing repository, but they don’t have a complete graph of your internal APIs, endpoints, libraries, etc. On top of that, if you are accessing a monorepo or many services not available on your machine locally, they have no context or get confused trying to access them. Bito’s AI Architect has built a knowledge graph to provide this information in a cheap and efficient way to your coding agent so it can accomplish the task with grounded and complete information.

2. They don’t fully understand how all of your services and modules interact with each other when you are trying to understand your overall system versus just one component. The AI Architect’s graph contains a mapping of all the dependencies, allowing to provide sophisticated analysis – how you would expect an Architect too.

Traditional embeddings work like a search engine — they retrieve code snippets or documents similar to a given query.

They can find related content but can’t understand how different parts of your system work together.

The AI Architect, on the other hand:

• Builds a knowledge graph that captures relationships between repositories, modules, APIs, and libraries.

• Provides precise answers and implementations, not just search results.

• Understands context and intent — how and why something is implemented in your codebase.

2. with our team.

3. Lastly, email if you have any additional questions.

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

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

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), GitLab (Self-Managed), and Bitbucket (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.

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

Follow the steps below to get started:

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

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

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

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

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

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

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

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.

Security and privacy are fundamental to how 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.

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

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:

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

1. 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. , which includes a shell script (bito-actions.sh

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.

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.

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 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.

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

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:

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”:

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.

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.

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.

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.

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 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

1. Email with subject "Slack Data Deletion Request"

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

3. Bito verifies within 2 business days

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

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

• 我的程式碼

• 我的倉庫

• 我的項目

• 我的工作區

• Mi código

• Mi repo

• Mi proyecto

• 私のコード

• 私のリポ

• 私のプロジェクト

• Meu código

• Meu repo

• Meu projeto

• Mój obszar roboczy

• moje miejsce pracy

• mój obszar roboczy

When you open a project in Visual Studio Code or JetBrains IDEs, Bito lets you enable the indexing of code files from that project’s folder. Basically, this indexing mechanism leverages our new AI Stack 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 index and feeds them to the Large Language Models (LLMs) 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.

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 .

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

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.

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.

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 index and feeds them to the Large Language Models (LLMs) 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 Generative AI and Large Language Models (LLMs) 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

This will definitely improve the way you build software.

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.

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 .

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):

This guide walks you through setting up Bito-hosted . You'll connect your Git account (GitHub, GitLab, or Bitbucket) to index your repositories, then use the MCP server credentials to integrate AI Architect with your coding agents. This enables your agents to access codebase-aware context and provide more accurate, relevant answers.

Bito offers two deployment options for AI Architect:

1. Bito-hosted (Fully managed by Bito — no infrastructure setup required)

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

The becomes significantly more powerful when paired with .

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

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)

The 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.

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

• How much is the AI Architect being used?

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.

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

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

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

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.

Step 2: Install the Plugin

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.

Bito provides the context layer your engineering workflow is missing. It works across the entire software development lifecycle, from planning in Jira to generating code in your coding agent, and across your Git provider for codebase-aware code reviews.

Bito's AI Architect builds a knowledge graph of your codebase (from repos to modules to APIs) and operational history from Jira/Linear (capturing past decisions, incident patterns, and system behavior). This gives your team a shared, always-current understanding of the system, and makes that understanding available wherever engineering decisions are made:

• In issue trackers (Jira, Linear) for feasibility analysis, technical design, and cross-repo impact assessment.

• (Claude Code, Cursor, Windsurf, GitHub Copilot, and more) for grounded code generation, accelerated onboarding, and production issue triage.

• (GitHub, GitLab, Bitbucket) for codebase-aware AI code reviews.

• for on-demand architecture questions, incident triage, and team-wide access to codebase knowledge.

• (Claude.ai, Claude Desktop, ChatGPT) for conversational access to your codebase knowledge, system context, and architectural insights.

Choose what you'd like to set up:

• Technical design and planning — AI Architect analyzes your Jira tickets and posts a complete implementation plan directly in comments section: feasibility assessment, story breakdown, effort estimates, risk flags, and dependency mapping.

• Grounded 1-shot production-ready code — The AI Architect learns all your services, endpoints, code usage examples, and architectural patterns. The agent automatically feeds those to your coding agent (Claude Code, Cursor, Codex, any MCP client) to provide it the necessary information to quickly and efficiently create production ready code.

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

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.

Knowledge graph

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.

AI Architect connects to the tools your team already uses across planning, coding, review, and communication.

• is the primary surface for technical design and scoping. AI Architect listens for new or updated Epics and Stories, posts implementation plans as ticket comments, and can be triggered on demand via @bito in any comment. Plans include feasibility assessments, story breakdowns, effort estimates, and risk flags — all grounded in your knowledge graph.

• brings AI Architect into team discussions. Mention @Bito in any channel thread and the assistant reads the full conversation context, resolves referenced Jira tickets and Confluence pages, and responds with context-aware answers, task breakdowns, or implementation plans — directly in the thread.

When auto-analysis is enabled for Jira, AI Architect evaluates every new or updated Epic and Story before generating an implementation plan. It reads the full ticket — title, description, comments, attachments, and any linked Confluence pages — and assigns a complexity score from 1 to 10. Implementation plans are only generated for tickets that meet or exceed the complexity threshold (default: 7). Tickets below the threshold receive a short note explaining why a plan was skipped, along with a prompt to request one manually if needed.

This keeps your ticket history clean and ensures that AI Architect's output is directed at the work that actually benefits from it — complex, ambiguous, or cross-cutting changes — rather than straightforward tasks that engineers can act on immediately.

AI Architect can be invoked in two ways: automatically when a ticket is created or updated, or on demand by any team member. On-demand triggering is done by commenting @bito, /bito, or #bito directly on the Jira ticket, or by adding a bito, bito-analyse, or bito-analyze label.

The same comment syntax also works for follow-up requests — you can ask AI Architect to revise its analysis, focus on a specific aspect, or regenerate the plan after requirements change.

This gives teams full control over when AI Architect runs. You can start with on-demand mode to evaluate output quality on specific tickets, then graduate to automatic analysis once you're confident in the results.

Requirements rarely stay fixed. AI Architect supports iterative refinement by generating new versions of any artifact — technical designs, scope breakdowns, feasibility assessments — as the ticket evolves. As engineers update descriptions, add comments, or link new context, they can re-trigger AI Architect to produce a revised plan that reflects the current state of the work.

This means technical planning stays in sync with changing requirements without manual rework. Each iteration builds on the ticket's full history, so AI Architect understands what has changed and why.

By default, AI Architect formats its output using Bito's standard structure. Custom templates let you override that with your team's own format — whether that's a specific TDD layout, an internal RFC structure, or a planning format tied to your sprint process. Output then matches what your engineers expect to see and what your workflow tooling can consume.

Custom templates are configured per workspace. To get started, contact the Bito team at .

AI Architect supports free-form prompting directly inside the Jira ticket, coding agents, or Slack. Engineers can mention Bito followed by a question/instruction in natural-language, such as:

• @bito analyze technical feasibility

• @bito focus on database migration risk

• @bito write this as a spike document

• @Bito PROJ-456 is causing errors in production — here are the logs. Identify the root cause?

• @Bito help us break this feature into smaller tasks

• @Bito explain the difference between these two approaches

Open a chat or conversation in your AI tool and try a test query to confirm AI Architect is working:

• "What repositories are available in my organization?"

• "Show me all Python repositories"

• "List the available tools"

Custom prompts work alongside the knowledge graph context AI Architect already has. You're directing the output, not replacing the grounding.

AI Architect can pull external technical context alongside your internal knowledge graph when generating plans. When web research is enabled, it incorporates relevant industry patterns, library documentation, and external best practices into its analysis — useful for greenfield work, third-party integrations, or areas where your codebase doesn't yet have established patterns.

Web research is applied selectively. It supplements internal context rather than replacing it, so the output remains grounded in your actual system.

The Bito AI Assistant brings AI Architect directly into Slack. You can ask system-level questions, trigger implementation plans, iterate on technical designs, and triage production issues — all without leaving the channel where the discussion is already happening. Mention @Bito in any thread, and the assistant reads the full conversation context, including any referenced Jira tickets or Confluence pages, before responding.

Common use cases include summarizing long planning threads, generating task breakdowns from a discussion, and pulling context from a specific ticket mid-conversation. The Slack agent is available in both public and private channels and supports file attachments including code files, configs, and logs.

Agent Skills are structured instruction files that define how AI Architect approaches specific engineering tasks. Each skill is purpose-built for a different type of work: feasibility analysis, epic planning, spike investigations, production triage, PRD or TRD generation, pre-commit reviews, and more. Skills can be triggered in Jira and Slack by commenting with natural language (@bito is this feasible?, @bito turn this epic into tasks), or invoked directly inside your coding agent via MCP.

Skills have full access to your knowledge graph — codebase, Jira history, Confluence docs, and observability data — so their output is always grounded in how your system actually works, not a generic template. In coding agents, skills are installed automatically and discoverable via / in the chat interface.

After a technical plan is approved, AI Architect can transform it into self-contained workstream agent specs — structured documents that give a coding agent everything it needs to implement a single workstream without further clarification. Each spec includes file paths, relevant patterns from your codebase, verification gates, and a dependency contract describing what other workstreams it relies on or produces.

Agent specs are designed to be passed directly into Cursor, Claude Code, Codex, or any MCP-compatible coding agent. They eliminate the back-and-forth between planning and implementation by encoding architectural decisions into the spec itself.

Once a workstream plan is approved, AI Architect can execute it directly. It creates a new branch from your default branch, implements the code changes across the relevant repositories, and follows the patterns and conventions already present in your codebase. Implementation is guided by the agent spec, with verification gates checked at each step.

This capability is available via the bito-agent-spec-executor skill in your connected coding agent. It is designed for well-scoped workstreams with clear acceptance criteria and works best when paired with the detailed agent spec output.

After implementation is complete, AI Architect opens a pull request per workstream, linked back to the originating Jira ticket. PRs include a summary of the changes made, the workstream spec they were generated from, and any verification results from the implementation run. This closes the loop between planning and review, giving engineers and reviewers immediate context on what was built and why.

Automated PRs work across GitHub, GitLab, and Bitbucket. They follow your existing branch naming and PR conventions.

SSO integration lets your team authenticate with AI Architect through your organization's existing identity provider instead of managing shared access tokens. When someone joins or leaves, access is granted or revoked through the same system that controls their email, Slack, and other business tools — no separate credential rotation required.

AI Architect's SSO implementation supports a broad range of identity providers including Google Workspace, Okta, Azure Entra ID, Microsoft AD FS, Auth0, Keycloak, and others, as well as any custom SAML 2.0 or OIDC configuration.

For self-hosted deployments, SSO runs entirely on-premises — no authentication traffic leaves your environment except for identity provider federation (if Enterprise IdP is configured) and Bito API calls for SSO configurations.

Session duration, refresh token TTL, and concurrent session limits are all configurable per workspace.

Use Bito's AI Architect with Claude.ai (Web) to enhance your AI-powered coding experience.

Once connected via MCP (Model Context Protocol), Claude.ai 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.

   • Note: The Bito MCP URL must be publicly accessible. Localhost or private network URLs (for example, http://localhost or internal IP addresses) are not supported and will not work.

2. . 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. A paid Claude.ai subscription - MCP integrations require one of the following:

   • Claude Pro

   • Claude Max

   • Claude Team

Claude.ai uses OAuth 2.1 with PKCE for secure authentication, so you don't need to manually manage access tokens. Your email will be collected during the OAuth consent flow for tracking purposes.

How OAuth authentication works:

1. You add the MCP server URL in Claude.ai Integrations settings

2. Claude.ai initiates an OAuth flow

3. Your browser opens a consent page hosted by Bito

4. You enter your email and approve the connection

Benefits:

• No manual token management

• Secure browser-based authentication

• Automatic token refresh

• Email collected during consent (no separate header needed)

OAuth Callback URL: https://claude.ai/api/mcp/auth_callback

• Verify you have a paid Claude subscription (Pro, Max, Team, or Enterprise)

• Free tier accounts do not have MCP access

• Contact Anthropic support if you have a paid plan but don't see the option

• Ensure pop-ups are allowed for claude.ai

• Check that your Workspace ID is correct

• Verify your organization has OAuth enabled for the MCP server

• Try clearing browser cache and cookies, then retry

• Click the server entry and select "Reconnect"

• OAuth tokens may have expired - re-authorize when prompted

• Check if your Bito workspace is still active

• Ensure the MCP server shows "Connected" status

• Try starting a fresh conversation

• Some tools may require specific prompts to activate

AI Architect's MCP server provides a comprehensive suite of tools for exploring, analyzing, and understanding your organization's codebase. These tools enable AI coding assistants to access deep repository intelligence, architectural insights, and code-level information across all your Git repositories.

Below is the complete list of MCP tools provided by AI Architect:

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

• 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

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

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

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

The sections below provide configuration guidance for each setup.

Bito-hosted agent configuration

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

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

The agent settings page allows configuration of options such as:

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

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

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

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

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

Key configuration options include:

• Mode

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

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

Each property is documented in detail on the page.

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

• Go to your project → Settings → Merge requests

• Under Merge checks, enable:

  • ✅ All threads must be resolved

• Go to your repository → Repository settings → Branch restrictions

• Click Add a branch restriction button.

• Under Select branches, define the target branches where this restriction should apply. Pull requests merging into these branches will be blocked until all "Request changes" comments are resolved. You can choose one of two options:

• Go to Repositories in the Bito dashboard.

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

• Enable the toggle: “Request changes comments”

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.

• 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.

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.

• 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.

Custom Instructions allows you to enrich with additional context that's relevant to your engineering workflow. By providing supplementary documentation, you help AI Architect develop a more complete understanding of your project ecosystem.

By default, AI Architect builds a knowledge graph using multiple inputs such as your codebase, issue tracking systems, documentation tools, and observability data. While this provides strong coverage, there is often still important context that lives outside these systems or is not easily discoverable.

Custom instructions allow you to fill that gap by providing documents that describe your system in a more complete way.

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.)

The 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 for automated code reviews.

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.

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

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:

Unlock the power of AI-driven code reviews in VS Code, Cursor, Windsurf, and all JetBrains IDEs (including IntelliJ IDEA, PyCharm, WebStorm, and more) with Bito's . This tool provides real-time, human-like feedback on your code changes, catching common issues before you submit a pull request.

The AI Code Review Agent helps you improve your code as you develop, so you don't have to wait for days to get feedback. This accelerates development cycles, boosts team productivity, and ensures higher code quality.

You can start using the Agent immediately—no setup is required!

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.

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.

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: