u/Desperate-Ad-9679

A lot of AI coding tools today feel weirdly centralized and opaque.

So I’ve been building an open-source alternative approach called CodeGraphContext.

Instead of sending your repository off to some remote indexing service, it builds a local graph representation of your codebase and exposes it through MCP for tools like Cursor, Claude, Gemini CLI, VS Code, etc.

The interesting part for me wasn’t “AI autocomplete”.

It was making repositories understandable:

• function relationships
• imports
• inheritance
• architecture traversal
• dependency graphs

A few things I cared about while building it:

• fully open source
• local-first workflow
• works on your own machine
• incremental indexing + caching
• tiny graph storage compared to huge vector indexes

Started as a side project and somehow grew to:

• 100k+ downloads
• 3k+ GitHub stars
• 250+ Discord members in community

Current AI code writing agents still view codebases as text. And that seems fundamentally wrong to me. A codebase is not a document.

A codebase is a graph where:

• functions call other functions
• classes inherit from other classes
• modules reference other modules
• symbols link to other symbols
• architectures form from connections

Thus, I created CodeGraphContext. https://github.com/CodeGraphContext/CodeGraphContext

It's an open-source MCP server that indexes code repositories into a graph database, enabling AI code writing assistants to get highly specific relationship-based context instead of blindly uploading chunks into prompts.

Currently, it supports 20 programming languages and integrates with all of the IDEs: Cursor, Claude, Gemini CLI, VS Code, any MCP-supported client

Here are some surprising statistics:

• 100k+ downloads - 100k+
• ~3.5k GitHub stars
• 575+ forks
• ~100 contributors
• 250+ member Discord community - https://discord.gg/dR4QY32uYQ