giancarloerra

giancarloerra / socraticode

Public

Enterprise-grade (40m+ lines) codebase intelligence in a zero-setup, private and local MCP: managed indexing, hybrid semantic search, polyglot code dependency graphs, and DB/API/infra knowledge. Benchmark: 61% less tokens, 84% fewer calls, 37x faster than standard AI grep.

ai ai-assistant ast claude code-graph
12
2
100% credibility
Found Mar 05, 2026 at 12 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TypeScript
AI Summary

SocratiCode indexes codebases to give AI coding assistants semantic search, code dependency graphs, and awareness of project artifacts like schemas and configs.

How It Works

1
🖥️ Discover SocratiCode

You find this helpful tool right in your favorite AI code editor like VS Code or Cursor with a simple install button.

2
✅ Add it easily

Click the button to connect it to your AI helper, and everything sets up automatically without any hassle.

3
🔍 Tell AI to learn your code

Just say to your AI 'index this project' and it starts building a smart understanding of your entire codebase in the background.

4
✨ Watch the magic happen

You see progress updates as it scans files, creates searchable pieces, maps dependencies, and even notes extra project details like schemas.

5
💭 Ask smart questions

Once ready, search naturally like 'how is login handled?' to get the best matching code snippets instantly.

6
📈 Explore connections

Dive into dependency maps or spot issues like loops to understand how your code fits together.

🎉 AI masters your code

Your AI now has instant, deep knowledge of your project, saving hours of manual searching and exploration.

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Star Growth

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AI-Generated Review

What is socraticode?

SocratiCode is a zero-setup MCP server that gives AI assistants like those in VS Code, Cursor, or Claude instant semantic understanding of your entire codebase, including polyglot dependency graphs and db/api/infra context artifacts. Written in TypeScript, it runs fully private and local via Docker, auto-indexing up to 40m+ lines with hybrid semantic + keyword search. Developers tell their AI to "index this codebase" once, then query via tools like codebase_search or codebase_graph_query for precise results without manual file reads.

Why is it gaining traction?

It crushes standard AI grep in benchmarks—61% less context tokens, 84% fewer calls, 37x faster on a 2.45M-line codebase—while delivering enterprise-grade security github copilot alternatives without cloud leaks. Zero config means npx install and go; file watchers keep indexes live, and polyglot graphs spot circular dependencies instantly. Unlike bloated RAG tools, it's focused: managed indexing, fused search, and Mermaid visuals in one lightweight package.

Who should use this?

Backend engineers onboarding to enterprise monorepos, where grep fails on conceptual queries like "auth middleware flow." AI agent users in Cursor/Claude tired of token-wasting file reads on large TypeScript/Python/Java codebases. Teams needing local, secure code intel for db/api/infra docs without GitHub Copilot's data risks.

Verdict

Promising for large-codebase AI workflows, with solid docs, 634 tests, and AGPL license—but only 12 stars and 1.0% credibility signal early maturity; test on small projects first. Worth npx-ing if you hate grep agents.

(198 words)

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