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

AI Code Reviews in Git

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: https://openai.com/enterprise-privacy/

2. Anthropic: https://www.anthropic.com/uk-government-internal-ai-safety-policy-response/data-input-controls-and-audit

3. Google Cloud: https://cloud.google.com/blog/products/ai-machine-learning/google-cloud-unveils-ai-and-ml-privacy-commitment (5th paragraph)

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

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

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

SOC 2 Type II Compliance

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

Our SOC 2 Type II compliance means:

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

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

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

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

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

Code Flow through Bito’s System

AI Code Review Agent

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

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

AI Chat and Code Completions

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

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

Data and Business Privacy Policy

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

Data Retention Policy

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

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

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

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

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

Sub-processor

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

Personal Data

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

Slack Integration

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

→ Slack App – Data Handling

If you have any questions about our security and privacy, please email support@bito.ai

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

Setup instructions

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

1. Log in to Bito Cloud

2. Open the AI Architect Settings dashboard.

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

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

Need help?

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

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

• The code is not retained after the request

Code caching

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.

Self-hosted AI Architect

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.

Performance considerations

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.

Code security and encryption

• 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

No AI model training

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.

Privacy and third parties

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.

Metadata and usage analytics

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.

Compliance and certifications

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

For our full privacy practices, see our Privacy Policy.

Questions?

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

The AI Code Review Agent supports two deployment options:

• Bito Cloud (fully managed)

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

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

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

Bito Cloud

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

Pros:

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

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

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

Cons:

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

Install/run using Bito Cloud

Self-hosted service

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

Pros:

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

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

Cons:

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

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

Install/run as a self-hosted service

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.

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

Installation and Configuration Steps

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

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

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

• Option 1: Using the bito_action.properties File

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

• Run the following command:

  • ./bito_actions.sh bito_action.properties

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

• Option 2: Using Runtime Values

  • Provide all necessary values directly on the command line:

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

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

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

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

    • Replace <pr_url> with your specific values.

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

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 Code Review Agents page.

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

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

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

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

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

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

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.

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

Difference Between CLI and webhooks service

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

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

When to Use CLI and When to Use webhooks service

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

CLI: Ideal for Specific, One-Time Reviews

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

• Conducting quick assessments of specific pull requests.

• Performing periodic, scheduled code analyses.

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

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

• Using in educational settings to demonstrate code review practices.

• Experimenting with different code review configurations.

• Reviewing code on local setups or for personal projects.

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

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

Webhooks service: For Continuous, Automated Reviews

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

• Continuously monitoring all pull requests in a repository.

• Providing instant feedback in collaborative projects.

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

• Performing automated code quality checks in team environments.

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

• Ensuring adherence to coding standards in every pull request.

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

• Maintaining consistency in code review across multiple projects.

• Enhancing workflows in remote or distributed development teams.

• Offering prompt feedback in agile development settings.

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

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

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

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

How to chat?

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

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

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

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

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

Bito usually responds within about 10 seconds.

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

• On GitLab, updates happen automatically.

What you can ask about

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

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

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

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

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

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

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

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

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

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

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

What you cannot ask about

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

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

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

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

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 AI Chat, and more.

Install on Windsurf

Installation Steps

Step 1: Open Windsurf IDE

Launch Windsurf IDE on your computer.

Step 2: Access the extensions marketplace

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

Step 3: Search for Bito and install the extension

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

2. Look for the official Bito extension in the search results. The extension should be published by "Bito"

3. Click the Install button and wait for the installation to complete (this usually takes just a few seconds).

Step 4: Sign in to Bito

Once installed, you'll need to authenticate:

1. Click "Sign up or Sign-in"

2. Bito authentication screen will display.

3. Log in with your Bito account credentials (or create a new account)

Step 6: Verify installation

To confirm Bito is working correctly:

1. You should see the Bito icon in your Windsurf IDE sidebar

2. Click on it to open the Bito panel

3. You should now have access to all Bito features

4. Try asking Bito a simple question to test the connection

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

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

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

Let's see how it is done:

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

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

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

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

5. Share on X (Twitter):

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

   2. Simply click "Open" to proceed.

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

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

6. Share Through Email:

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

   2. Select your email account if needed.

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

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

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

7. Share the Link:

   1. Click on Link from the menu.

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

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

Bito provides the context layer you need for autonomous development. 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.

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

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

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

• In Confluence for pulling design docs, RFCs, and runbooks into ticket analysis — and creating, updating, and commenting on pages from Jira or Slack.

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

Start freeGetting started guide

Getting started

Choose what you'd like to set up:

Key capabilities of the AI Architect

• 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.

  • Watch demo video

• 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.

• Consistent design adherence — Code generated aligns with your architecture patterns and coding conventions.

• Spec-driven development — Automatically generate highly detailed, implementation-ready technical requirement documents (TRDs) and low-level designs (LLDs) with a deep, context-aware understanding of your codebase, services, and design patterns, ensuring architectural integrity and consistency at a granular level.

• Triaging production issues — Easily and quickly find root causes to production issues based on errors/logs/etc.

• Faster onboarding — New engineers or AI agents can quickly understand how a system or component system structure.

• Enhanced documentation and diagramming — Through its internal understanding of interconnections between modules and APIs.

• Smarter code reviews — Reviews with system-wide awareness of dependencies and impacts.

Helpful resources

AI Architect

AI Code Review Agent

Video library

Need help?

If you have any questions, feel free to email us at support@bito.ai

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

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:

• 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.

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.

👉

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.

Use Bito's 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.

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.

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.

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:

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.

Bito’s AI Code Review Agent is the first agent built with Bito’s AI Agent framework and engine. It is an automated AI assistant (powered by 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 .

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

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

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

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

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

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

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

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

The 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’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.

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.

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

To ensure the AI Code Review Agent operates smoothly with your GitHub (Self-Managed), 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

• 3.23.173.30

• 18.216.64.170

The agent response can come from any of these IPs.

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

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

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

• GitHub Personal Access Token (Classic):

• GitLab Personal Access Token:

• Bitbucket Personal Access Token:

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

Bito requires:

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

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

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

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

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

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

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

1. Select repositories for AI-powered reviews

2. Customize review settings to fit your workflow

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

Contact for any assistance.

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

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 AI Chat, and more.

Install on Cursor

Installation Steps

Step 1: Open Cursor IDE

Launch Cursor IDE on your computer.

Step 2: Access the extensions marketplace

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

Step 3: Search for Bito and install the extension

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

2. Look for the official Bito extension in the search results. The extension should be published by "Bito".

3. Click the Install button

4. Wait for the installation to complete (this usually takes just a few seconds)

Step 4: Sign in to Bito

Once installed, you'll need to authenticate:

1. Click "Sign up or Sign-in"

2. Bito authentication screen will display.

3. Log in with your Bito account credentials (or create a new account)

Step 5: Verify installation

To confirm Bito is working correctly:

1. You should see the Bito icon in your Cursor IDE sidebar

2. Click on it to open the Bito panel

3. You should now have access to all Bito features

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

Updating Bito Plugin on VS Code

1. Open your VS Code IDE

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

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

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

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

Updating Bito Plugin on JetBrains IDEs

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

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

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

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

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

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

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

Copy Answer

Copy the answer to the clipboard.

Regenerate Answer

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

Rate Response

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

Modify Last Prompt

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

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.

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.

1. Performance Check: Optimize code performance and efficiency

2. Security Check: Identify and fix security vulnerabilities

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

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

This will definitely improve the way you build software.

When you open a project in Visual Studio Code or JetBrains IDEs, Bito lets you enable the 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 Available Keywords page.

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

Security of your code

As usual, security is top of mind at Bito, especially when it comes to your code. A fundamental approach we have taken is to keep all code on your machine, and not store any code, code snippets, indexes, or embedding vectors 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 Bito’s Privacy and Security Practices.

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.

• my code

• my repo

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.

Bito's Knowledge graph is the context engine behind Bito's AI Architect 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.)

• Code reviews (GitHub, GitLab, Bitbucket)

• Team communication (Slack)

The knowledge graph is great at answering questions like "What will be affected if we change Service A?" or "Have we seen this issue before?" without manual research.

The knowledge graph ingests data from multiple sources to form a complete context layer.

The following sources are fed into Bito's indexing pipeline, which scans and parses each type of data to populate the knowledge graph.

• Code and commits history: All source code (microservices, libraries, modules) and Git history (commits, branches). The graph records entities like classes, functions, and API endpoints, and notes code changes (e.g. refactor patterns) from commit metadata.

• Issue trackers: Jira or Linear tickets, epics, and bugs. Each ticket is connected to the code or service it involves, and recurring incident patterns (like frequent hotfixes) become part of the context.

• Documentation: Design documents, architecture decisions, wikis (e.g. Confluence). The graph links these to the relevant code components, capturing business intent and past decisions for reference.

The knowledge graph evaluates proposed changes against your actual system state. It understands service boundaries, existing patterns, historical constraints, and known limitations. This allows it to determine what is realistically buildable, highlight risks early, and surface constraints that may not be obvious from specifications alone.

Before any change is made, the knowledge graph maps its full system impact. It traces dependencies across services, APIs, and shared components, and combines this with historical usage and change patterns. This ensures that decisions are based on a complete view of downstream effects, not partial assumptions.

The knowledge graph grounds design decisions in how your system actually works. It reflects existing architectural patterns, prior design decisions, and service-level responsibilities. This leads to designs that are consistent with your environment and easier to implement, operate, and maintain.

The knowledge graph connects high-level work to real system components. It uses past implementation patterns, system dependencies, and team workflows to break down epics into actionable units. This results in tasks that are aligned with how work is actually executed in your organization.

When generating code, the knowledge graph provides system-specific context such as API contracts, service interactions, error handling patterns, and operational constraints. This ensures that generated code aligns with your architecture and integrates correctly with existing systems.

The knowledge graph adds system-wide context to code reviews. It highlights relevant patterns, prior issues, and cross-service implications that may not be visible within a single pull request. This helps reviewers make more informed decisions with less manual investigation.

Most AI coding tools use retrieval-augmented generation: embedding code as vectors and retrieving relevant snippets. This treats your codebase as searchable text rather than an interconnected system.

When you ask about authentication, vector search finds files containing "auth" keywords. It doesn't understand that your authentication service depends on specific Redis configuration, that an Architecture Decision Record (ADR) documents why Redis was chosen, or that a recent incident involved rate limiting.

The difference is between finding information and understanding relationships. A vector database can retrieve your authentication service code and a Jira ticket separately, but can't connect that the ticket describes an incident that led to the code change, which implemented a pattern from an ADR, and monitoring shows the pattern's effectiveness.

Some tools add more context to prompts — feeding more code and documentation to the AI. But retrieving fifty potentially relevant files when only three matter creates noise, not clarity.

The knowledge graph solves a different problem: identifying which information matters for specific decisions and why it matters.

Traditional AI tools treat codebases as relatively static, re-indexing periodically without modeling change over time. The knowledge graph understands that your current architecture results from decisions made in specific contexts.

When your team has been migrating from monolith to microservices for a year, that's not historical trivia — it's a pattern that should inform how new features are designed.

Code analysis tools build dependency graphs and trace function calls, but they don't capture operational reality. Knowing Service A calls Service B is useful. Knowing that Service B has rate limits that caused a production incident eight months ago, that the response involved implementing circuit breakers, and that a shared library now exists for this pattern — that's the context difference between adequate code and production-ready code.

The constraint in scaling engineering isn't hiring developers — it's enabling them to make good decisions without complete context. When critical knowledge lives in senior engineers' heads, scaling means either accepting decisions with less context (leading to rework) or creating bottlenecks.

The knowledge graph distributes accumulated knowledge. Mid-level engineers access the same contextual understanding staff engineers bring, not through years of experience but through the graph making that context explicit and queryable.

Technical debt is the gap between how your system is designed and how it needs to work. The graph makes this visible by connecting what was decided (ADRs, design docs), what was built (code), what happened (incidents, monitoring), and what's planned (Jira epics).

This visibility enables informed decisions about what technical debt costs and what remediation provides value.

For organizations with regulatory requirements, the graph provides an auditable record of technical decisions. When you need to demonstrate why architectural choices were made, what alternatives were considered, and how security requirements were addressed, the graph traces these relationships explicitly.

Most AI tools excel at isolated tasks: writing functions, explaining code, suggesting refactors. The knowledge graph makes AI useful for decisions requiring system-wide understanding: feasibility analysis, architectural design, impact assessment, etc.

These are tasks senior engineers spend most time on, and where AI historically provided least value due to lacking context.

Every decision captured in the graph becomes training data for better future decisions. Organizations accumulate knowledge over time, but it usually remains implicit until people leave and understanding walks out the door.

The knowledge graph makes accumulation explicit, preserving it in a form that informs decision-making as teams evolve.