# How to Vibe Code Your Own Sourcegraph (and Stop Paying for It)

> Code understanding, oversight, and evolution for enterprise scale

- Site: https://sourcegraph.com
- Category: Developer Tools
- Verdict: **Serious undertaking** (38/100 vibecodeable)
- Estimated effort: 3-5 months of intensive part-time work

## Verdict

You can build a personal codebase search and RAG assistant over a few local repos, but replicating its exact multi-repo AST indexer and enterprise MCP server is a serious undertaking.

Sourcegraph's core value relies on custom-engineered Go indexing infrastructure, exact regex search engines over massive codebases, and precise AST symbol graphs (SCIP) across languages. While a solo developer can build a lightweight TypeScript clone that indexes a few local git repositories using vector embeddings and an MCP server, matching the lightning-fast deterministic search and deep agentic oversight of Sourcegraph requires building complex distributed systems far beyond a weekend project.

### What you can't replicate

- Proprietary SCIP indexing engines for dozens of programming languages with sub-second symbol navigation
- Enterprise-grade SOC 2 compliance, single-tenant VPC deployments, and zero-data-retention security
- Native integrations and webhook management across hundreds of enterprise GitHub/GitLab instances

## What it does

An enterprise-grade code intelligence platform providing exact code search, agentic AI search (Deep Search), batch changes, and MCP servers for AI coding agents.

### Core features

- Multi-repository exact and regex code search
- AST parsing and cross-language symbol navigation (SCIP)
- Agentic Deep Search with natural language Q&A and code citations
- Model Context Protocol (MCP) server for feeding external AI tools
- Agentic Batch Changes across thousands of repositories
- Code Insights and monitoring dashboards
- CLI tool (`src`) for terminal search and automation
- Single sign-on, RBAC, and zero-data-retention guarantees

## The business

### Pricing

- Enterprise Plan: $16,000/yr — Base price for enterprise-scale code understanding, code search, and AI credits.

### Funding

$248M raised.
- Seed
- Series A
- Series B
- Series C
- Series D
Investors: Andreessen Horowitz, Sequoia Capital, Craft Ventures, Redpoint Ventures, Felicis

Founded 2013.
Team size: 150-220.

## The hard parts

- Building a lightning-fast custom search engine and AST parser over gigabytes of source code across dozens of languages
- Maintaining an agentic loop with reliable multi-step tool calling and code grounding
- Real-time webhook ingestion and indexing synchronization across hundreds of distributed git repositories
- Exposing a low-latency Model Context Protocol (MCP) server with full symbol graph context

## How to vibe code Sourcegraph

### Prerequisites

- Node.js (free): Runtime for the TypeScript backend and MCP server implementation.
- GitHub (free): Source code hosting and webhook integration targets.

### Recommended AI tools

- Claude Code: Agentic terminal tool best suited for writing complex TypeScript parsers and MCP server endpoints.
- Cursor: Ideal IDE for reviewing code diffs and refining the React frontend dashboard.

### Stack

- Frontend: Next.js
- Backend: Node.js with Express & MCP SDK
- Database: Turso
- Auth: better-auth
- Payments: none
- Other: Vercel AI SDK, Anthropic API, OpenAI API

### Hosting

- Fly.io (Hosting the background git indexing worker and the MCP server process): $5/mo
- Vercel (Hosting the Next.js web application frontend and API routes): $0/mo

### Build guide

1. **Project Scaffolding & Git Ingestion Pipeline** — Initialize a Next.js monorepo with Tailwind CSS and set up a Node.js background worker to clone and parse local or remote Git repositories into a Turso SQLite database.

```
Scaffold a Next.js 16 application with TypeScript, Tailwind CSS v4, and a companion Express backend server. Set up a Turso SQLite database schema using Drizzle ORM with tables for repositories, commits, files, and symbol index nodes. Implement a background ingestion service using simple-git that clones specified GitHub repositories, walks the directory tree, extracts file contents, and inserts them into the database with proper foreign key relations. Ensure proper error handling for large binary files and git authentication tokens.
```

2. **Exact Code Search & Regex Engine** — Implement server-side search capabilities supporting exact string matching, case sensitivity, and regular expressions across the indexed repositories.

```
Build a high-performance code search service in TypeScript that queries the Turso SQLite database for exact strings, substrings, and regular expressions across indexed files. Implement query filters for repository name, file path patterns, and programming language. Create a fast search API endpoint with pagination and match highlighting, returning file paths, line numbers, and surrounding code context. Optimize query performance with SQLite FTS5 full-text search virtual tables.
```

3. **Deep Search AI Agent Loop** — Integrate the Vercel AI SDK and Anthropic API to build an agentic search interface that investigates codebases and cites relevant code files.

```
Implement the agentic Deep Search backend loop using the Vercel AI SDK and Anthropic Claude Sonnet. Expose custom tool definitions to the LLM agent, including keyword search, file content reading, and symbol lookup against our Turso database. Build a streaming chat endpoint that executes multi-step tool calls, reasons across system layers, and formats responses with explicit code block citations. Handle token budgeting and conversation state persistence.
```

4. **Model Context Protocol (MCP) Server** — Expose code search and symbol intelligence as an MCP server so external AI coding tools like Claude Code and Cursor can query your codebase.

```
Build a Model Context Protocol (MCP) server conforming to the official MCP specification using TypeScript and stdio transport. Expose custom tools such as `sg_keyword_search`, `sg_read_file`, and `sg_symbol_lookup` that query our local code intelligence database. Include authentication header validation, structured logging, and robust error handling so external developer agents running in Cursor or Claude Code can seamlessly fetch codebase context and minimize token waste.
```

5. **Web Dashboard & Rich Text Interface** — Create a responsive web UI featuring a ProseMirror rich text editor for Deep Search prompts, search results highlighting, and repository management.

```
Build a modern React frontend dashboard in Next.js matching Sourcegraph's clean design system. Implement a Code Search results view with syntax highlighting using Shiki, file tree navigation, and repository filters. Build the Deep Search chat interface integrating a ProseMirror rich text editor supporting @-mention file tagging, infinite scroll for past conversations, and split-pane diff views for code changes. Connect all UI components to the backend REST and streaming APIs.
```

### Cost vs paying

**Starting costs (one-time):**

- Custom domain name: $12
- Initial Anthropic API developer credits: $25
- Total: ~$37 one-time

**Ongoing costs (monthly):**

- Fly.io backend & worker hosting: $5/mo
- Anthropic API usage for Deep Search and MCP queries: $15/mo
- Total: ~$20/mo

- Paying for the SaaS instead: $1,333/mo ($16,000/yr)
- Build time: 60 hours
- AI tool credits: $20
- Break-even: Immediate

## Sources

- [Sourcegraph Official Website](https://sourcegraph.com)
- [Wikipedia: Sourcegraph](https://en.wikipedia.org/wiki/Sourcegraph)
- [Contrary Research: Sourcegraph Business Breakdown](https://contraryresearch.com)