# How to Vibe Code Your Own Bito (and Stop Paying for It) > The context layer for autonomous development - Site: https://bito.ai - Category: Developer Tools - Verdict: **Serious undertaking** (42/100 vibecodeable) - Estimated effort: 4-6 weeks of focused development ## Verdict You can build a personal subset that indexes a single local repository and serves context over MCP, but replicating Bito's multi-repo enterprise knowledge graph and automated PR review fleet requires serious engineering. Bito bridges scattered engineering silos into a unified knowledge graph. While a solo developer can spin up a Next.js frontend and a basic vector search pipeline over a single git repo in a weekend, building an incremental AST parser that maps cross-service dependencies across multiple repositories, handles GitHub webhook queues reliably, and exposes a performant Model Context Protocol (MCP) server is a substantial undertaking. The enterprise compliance layer (SOC 2, zero data retention) is entirely out of reach for a personal clone. ### What you can't replicate - SOC 2 Type II enterprise compliance certification and zero code retention architecture - Native enterprise connectors for Jira, Confluence, and self-hosted GitLab/Bitbucket instances - Battle-tested scale processing tens of thousands of weekly enterprise pull requests ## What it does An AI-powered developer platform and context layer that indexes repositories, commits, issues, and documentation into a living knowledge graph to ground coding agents, technical designs, and automated pull request reviews. ### Core features - Multi-repository AST parsing and dependency tree extraction - Dynamic knowledge graph construction and vector embedding storage - Model Context Protocol (MCP) server implementation for agent context injection - Git webhook event listener for pull request diff analysis and code review comments - Issue tracker integration (Jira/Linear) for automated spec breakdown and technical design - Dashboard for workspace administration, seat management, and usage analytics ## The business ### Pricing - Professional Plan (AI Architect): Custom — Usage-based pricing scaled by codebase size and agent volume. - Team Plan (AI Code Reviews): $12/mo per seat — Billed annually ($15/mo billed monthly). Includes 5k lines reviewed per seat/month. - Professional Plan (AI Code Reviews): $20/mo per seat — Billed annually ($25/mo billed monthly). 14-day free trial available. ### Funding ~$11.5M raised. - Seed (October 2021) - $1.2M - Seed (June 2023) - $3.2M - Seed Extension (May 2025) - $5.7M - Crowdfunding & Debt (2025-2026) - $2.5M+ Investors: Vela Partners, NextView Ventures, Eniac Ventures, Maxitech Ventures, NGP Capital Founded 2021. Team size: 50-70+. ## The hard parts - Building a reliable cross-repository AST code graph parser that updates incrementally on every commit - Maintaining low-latency MCP server responses for multi-gigabyte codebases during live agent execution - Executing accurate cross-service impact analysis without hallucinating dependency edges across different languages ## How to vibe code Bito ### Prerequisites - Node.js (free): Runtime environment for Next.js web application and TypeScript-based backend services. - GitHub (free): Git hosting platform for test repositories and webhook delivery simulation. - Anthropic API (pay-per-token): Provides Claude model access for generating technical designs and pull request reviews. ### Recommended AI tools - Claude Code: Primary agentic coding tool for scaffolding the full-stack architecture, database schemas, and MCP server implementation. - Cursor: AI-native code editor for reviewing diffs, inspecting frontend layouts, and iterating on UI components. ### Stack - Frontend: Next.js with Tailwind CSS - Backend: Next.js API Routes & TypeScript MCP Server - Database: Supabase (PostgreSQL with pgvector) - Auth: better-auth - Payments: None (Personal use clone) - Other: Resend for transactional notifications, Sentry for error tracking ### Hosting - Vercel (Hosting the Next.js dashboard, API routes, and webhook ingestion endpoints.): $0/mo (Hobby Tier) - Supabase (Hosting PostgreSQL database, vector embeddings with pgvector, and metadata storage.): $0/mo (Free Tier) ### Build guide 1. **Project Scaffolding & Database Schema** — Initialize the Next.js project with Tailwind CSS, configure Supabase with pgvector extension, and set up better-auth for personal dashboard access. ``` Create a new Next.js TypeScript project using App Router and Tailwind CSS. Configure Supabase as the database client with the `pgvector` extension enabled. Define Prisma or Drizzle schema models for `repositories`, `code_nodes` (storing file paths, AST symbols, and vector embeddings), `pull_requests` (storing diffs and review comments), and `technical_designs`. Implement better-auth with email/password authentication restricted to your personal user account. Ensure clean separation between database connection logic and frontend UI components. ``` 2. **Repository Ingestion & Vector Embedding Pipeline** — Build an ingestion script that clones local repositories, parses code files into semantic chunks, generates embeddings, and stores them in Supabase. ``` Build a TypeScript service inside the Next.js app that accepts a GitHub repository URL, clones or reads the file tree, and chunks code files by functions and classes using basic AST heuristics or regex-based parsing. Integrate the Anthropic embeddings API or OpenAI embeddings API to generate vector embeddings for each code chunk. Insert these chunks along with their file paths, function signatures, and vector embeddings into the `code_nodes` table in Supabase. Include an API route to trigger this indexing process manually. ``` 3. **Model Context Protocol (MCP) Server Implementation** — Develop a lightweight Model Context Protocol (MCP) server that exposes the indexed codebase knowledge graph to AI coding assistants like Cursor and Claude Code. ``` Create a Model Context Protocol (MCP) server module in TypeScript that can run locally or be queried via HTTP. Implement MCP tools `search_codebase` and `get_service_topology` that query the Supabase vector database for relevant code chunks and dependency relationships based on natural language queries from AI coding agents. Test the server connection locally to verify that tools return structured markdown context suitable for injection into Claude Code or Cursor. ``` 4. **AI Architect: Technical Design & Scoping Engine** — Build the AI Architect planning engine that analyzes requirements and generates technical design documents grounded in the knowledge graph. ``` Implement the AI Architect planning module. Create a backend service that takes a feature specification or issue description, queries the Supabase knowledge graph for relevant existing code patterns, and prompts Claude Sonnet to generate a structured output containing: (1) Feasibility analysis, (2) Technical design document, (3) Impact assessment across files, and (4) Scope breakdown into work items. Build a UI page in Next.js to display these generated designs and allow iterative refinement via prompt inputs. ``` 5. **GitHub Webhook & AI Code Review Agent** — Set up a GitHub webhook endpoint to listen for pull request events, fetch the git diff, run an AI code review, and post comments directly back to GitHub. ``` Create a GitHub webhook ingestion endpoint in Next.js that handles `pull_request` events (specifically `opened` and `synchronize`). When a PR event fires, fetch the full git diff using the GitHub REST API. Pass the diff along with relevant context retrieved from the Supabase knowledge graph to Claude Sonnet with a system prompt instructing it to perform a strict code review checking for bugs, security risks, and regressions. Format the AI review output into markdown and post it as a comment on the GitHub pull request using the GitHub API. ``` 6. **Dashboard UI & Analytics Polish** — Build the central dashboard interface for managing indexed repositories, viewing code review analytics, and monitoring system status. ``` Build a polished admin dashboard in Next.js using Tailwind CSS. Include views for: (1) Connected repositories list with status indicators and manual re-index buttons, (2) Recent AI code reviews with line count statistics and review summaries, (3) Technical design history log, and (4) Usage analytics tracking total indexed files and token consumption. Ensure responsive layouts and clean error states across all dashboard screens. ``` ### Cost vs paying **Starting costs (one-time):** - Domain name (optional): $12/yr - Initial LLM API credits for development: ~$25 - Total: ~$37 one-time **Ongoing costs (monthly):** - Vercel Hobby Hosting: $0/mo - Supabase Free Tier: $0/mo - LLM API usage for personal reviews/architect: ~$5-15/mo - Total: ~$10/mo - Paying for the SaaS instead: $20/mo (Professional Plan per seat) - Build time: 35-50 hours - AI tool credits: ~$20 (Claude Pro / API credits) - Break-even: Building is for learning and customization; paid SaaS is cheaper if valuing your time. ## Sources - [Bito Official Website & Product Pages](https://bito.ai) - [Bito Pricing Page](https://bito.ai/pricing) - [Bito AI Architect Documentation](https://bito.ai/product/ai-architect)