Install AI Architect (self-hosted)

Deploy AI Architect in your own infrastructure for complete data control and enhanced security

This guide walks you through installing Bito's AI Architect as a self-hosted service in your own infrastructure. Self-hosting gives you complete control over where your code knowledge graph resides and how AI Architect accesses your repositories.

Why choose self-hosted deployment? Organizations with strict data governance requirements, air-gapped environments, or specific compliance needs benefit from running AI Architect within their own infrastructure. Your codebase analysis and knowledge graph stay entirely within your control, while still providing the same powerful context-aware capabilities to your AI coding tools.

What you'll accomplish: By the end of this guide, you'll have AI Architect running on your infrastructure, connected to your Git repositories, and ready to integrate with AI coding tools like Claude Code, Cursor, Windsurf, and GitHub Copilot through the Model Context Protocol (MCP).

Deployment options

AI Architect can be deployed in three different configurations depending on your team size, infrastructure, and security requirements:

a. Personal use (with your LLM keys)

Set up AI Architect on your local machine for individual development work. You'll provide your own LLM API keys for indexing, giving you complete control over the AI models used and associated costs.

Best for: Individual developers who want codebase understanding on their personal machine.

b. Team / shared access (with your LLM keys)

Deploy AI Architect on a shared server within your infrastructure, allowing multiple team members to connect their AI coding tools to the same MCP server. Each team member can configure AI Architect with their preferred AI coding agent while sharing the same indexed codebase knowledge graph.

Best for: Development teams that want to share codebase intelligence across the team while managing their own LLM costs.

c. Enterprise deployment (requires Bito Enterprise Plan)

Deploy AI Architect on your infrastructure (local machine or shared server) with indexing managed by Bito. Instead of providing your own LLM keys, Bito handles the repository indexing process, simplifying setup and cost management.

Best for: Organizations that prefer managed indexing without handling individual LLM API keys and costs.

Note: All deployment options are self-hosted on your infrastructure — your code and knowledge graph remain under your control.

Prerequisites

a. Required accounts and tokens

1

Bito API Key (required)

This is your Bito Access Key, which you can obtain from the Bito Cloud settings.

• View Guide

2

Git provider

We support the following Git providers:

• GitHub

• GitLab

• Bitbucket

So, you'll need an account on one of these Git providers to index your repositories with AI Architect.

3

Git Access Token (required)

A personal access token from your chosen Git provider is required. You'll use this token to allow AI Architect to read and index your repositories.

1. GitHub Personal Access Token (Classic): To use GitHub repositories with AI Architect, ensure you have a CLASSIC personal access token with repo access. We do not support fine-grained tokens currently.

   • View Guide

2. GitLab Personal Access Token: To use GitLab repositories with AI Architect, a token with API access is required.

   • View Guide

3. Bitbucket Token: To use Bitbucket repositories with AI Architect,

b. System Requirements

Installation guide

1

Download AI Architect

2

Extract package

Run the following command to extract the downloaded package.

tar -xzf bito-cis-*.tar.gz

Move inside the folder.

cd bito-cis-*

3

Run setup

./setup.sh

You'll need to provide the following details when prompted:

• Bito API Key (required) - Your Bito authentication key

• Select your Git provider from available options:

  • GitLab

  • GitHub

  • Bitbucket

• Git Access Token (required) - Personal access token for your Git provider

Note: Refer to the Prerequisites section for details on how to obtain these.

Note: Once the setup is complete, your Bito MCP URL and Bito MCP Access Token will be displayed. Make sure to store them in a safe place, you'll need them later when configuring MCP server in your AI coding agent (e.g., Claude Code, Cursor, Windsurf, GitHub Copilot (VS Code), etc.).

4

Add repositories

Edit config.yaml file to add your repositories for indexing:

repository:
  configured_repos:
    - namespace: your-org/repo-name-1
    - namespace: your-org/repo-name-2
    - namespace: your-org/repo-name-3

Then apply the configuration:

bitoarch config repo add config.yaml

5

Start indexing

Trigger workspace synchronization to index your repositories:

bitoarch manager sync

Indexing process will take approximately 3-10 minutes per repository. Smaller repos take less time.

6

Check indexing status

Run this command to check the status of your indexing:

bitoarch manager status

Status indicators:

• in_progress - Indexing is running

• completed - All repositories indexed

• failed - Check logs for errors

How to use AI Architect

Configure MCP server in supported AI coding tools such as Claude Code, Cursor, Windsurf, and GitHub Copilot (VS Code).

Select your AI coding tool from the options below and follow the step-by-step installation guide to seamlessly set up AI Architect.

• Guide for Claude Code

• Guide for Cursor

• Guide for Windsurf

• Guide for GitHub Copilot (VS Code)

Update repository list

Edit config.yaml file to add/remove repositories.

vim config.yaml

To apply the changes, run this command:

bitoarch config repo update config.yaml

Start the re-indexing process using this command:

bitoarch manager sync

Available commands

For complete reference of AI Architect CLI commands, refer to Available commands.