SocratiCode is a zero-configuration MCP server that builds and maintains a deep semantic index of your entire codebase. It gives AI assistants (Claude, Cursor, Codex, Windsurf, Cline) instant structural knowledge — where features are implemented, how modules connect, what depends on what — without reading files one by one. Only Docker is required. No API keys, no data leaves your machine.

What are the real benchmark numbers?

Tested on VS Code's own 2.45 million line codebase with Claude Opus 4.6: SocratiCode used 61% less context tokens, made 84% fewer tool calls, and was 37x faster than grep-based exploration for the same queries. These aren't synthetic benchmarks — they're measured on a production codebase at real scale.

How does SocratiCode search work?

Hybrid search combining vector semantic search and BM25 keyword search, fused with RRF (Reciprocal Rank Fusion). Semantic search catches conceptually related code; BM25 catches exact function names, variable names, and error strings. The combination consistently outperforms either alone. AST-aware chunking means chunks respect code structure (function boundaries, class definitions) rather than arbitrary line counts.

What is the dependency graph feature?

SocratiCode builds a polyglot code dependency graph across your entire codebase — showing which modules import what, which files depend on each other, and circular dependency detection with visualization. Ask 'what depends on this module?' and get an accurate answer, not a grep across import statements.

How do I install SocratiCode?

Prerequisites: Docker running (that's it). Then: npx -y socraticode. One-click installs available for VS Code and Cursor. For Claude Desktop, Windsurf, Cline, and Codex CLI, add the MCP server config manually. The indexer starts automatically on first use, handles pause/resume checkpointing, and a file watcher keeps the index updated on every file change.

Does SocratiCode send my code to any server?

No. Private and local by default — Docker handles the vector database (Qdrant) and embedding locally. No API keys required and no data leaves your machine in the default configuration. Cloud embedding (OpenAI, Google Gemini) and remote Qdrant are available as optional configuration for teams that want them.

SocratiCode: Give Your AI Instant Knowledge of Your Entire Codebase

By Prahlad Menon Published 2026-03-15 5 min read

When an AI coding assistant needs to understand your codebase, it typically does one of two things: greps for keywords, or reads files one by one as you reference them. Neither gives it structural knowledge — which module owns which responsibility, how components connect, what would break if you changed this function.

SocratiCode takes a different approach: build a full semantic index of the entire codebase upfront, wire it into your AI assistant via MCP, and let the AI query structure directly instead of searching blindly.

The result, benchmarked on VS Code’s own 2.45 million line codebase with Claude Opus 4.6: 61% fewer context tokens, 84% fewer tool calls, 37x faster than grep-based exploration.

What it actually does

When you point SocratiCode at a codebase, it builds three things:

1. A hybrid semantic search index

Every file is chunked using AST-aware parsing — chunks respect function boundaries, class definitions, and code structure rather than arbitrary line counts. Each chunk gets both a vector embedding (for semantic search) and a BM25 keyword index. Queries run both in parallel and fuse results with RRF (Reciprocal Rank Fusion).

This matters because the two search modes catch different things. Semantic search finds conceptually related code — searching “authentication logic” finds the auth middleware even if it doesn’t say “authentication” literally. BM25 catches exact names — function names, variable names, error strings, constants that don’t embed well. The combination consistently outperforms either alone.

2. A polyglot dependency graph

SocratiCode builds a full cross-file dependency graph: which modules import what, which files depend on each other, circular dependency detection with visualization. This is what makes questions like “what would break if I changed this?” answerable in seconds instead of requiring a manual trace through imports.

3. Context artifacts

Beyond source code, SocratiCode indexes infrastructure knowledge: database schemas, API specs, infra configs (Docker, Kubernetes, Terraform), architecture docs. An AI assistant can answer “how does our API handle rate limiting?” by querying both the implementation code and the API spec simultaneously.

The benchmark: VS Code, 2.45M lines, Claude Opus 4.6

The numbers come from a real test on a production codebase, not a synthetic benchmark:

Metric	grep-based	SocratiCode	Improvement
Context tokens used	baseline	-61%	37% of baseline
Tool calls made	baseline	-84%	16% of baseline
Speed	baseline	37x faster	—

The mechanism is straightforward: instead of Claude making 20 tool calls to grep for related files, read each one, and piece together the structure, SocratiCode answers “where is rate limiting implemented?” in one query that returns the exact relevant chunks with context. Fewer round trips, less context bloat, faster answers.

Installation

Prerequisites: Docker Desktop running. That’s the only requirement — no API keys, no external services, nothing leaves your machine.

# Any editor — one command
npx -y socraticode

VS Code / Cursor: One-click install buttons in the README.

Claude Desktop — add to claude_desktop_config.json:

{
  "mcpServers": {
    "socraticode": {
      "command": "npx",
      "args": ["-y", "socraticode"]
    }
  }
}

Windsurf / Cline / Codex CLI: Same MCP server config pattern — add npx -y socraticode as the command.

On first run, SocratiCode starts indexing automatically. Large codebases are indexed in batches with resumable checkpoints — if it’s interrupted, it picks up where it left off. A file watcher keeps the index current across sessions; every file change triggers an incremental update.

What you can ask after setup

Once indexed, your AI assistant has structural knowledge it didn’t have before:

"How does authentication work in this project?"
→ Returns the auth middleware, token validation, session handling — 
  all in one query, with file locations and relationships

"Where is rate limiting implemented?"
→ Finds it across middleware, config, and tests simultaneously

"What depends on the UserService module?"
→ Returns the full dependency graph for that module

"Show me all database transaction handling"
→ Semantic search across ORM calls, raw queries, and migration files

"What would break if I removed the cache layer?"
→ Dependency graph traversal showing everything that imports it

Multi-agent ready

SocratiCode is designed for teams: multiple AI agents can work on the same codebase simultaneously, sharing a single index with automatic coordination. No manual synchronization, no race conditions on index updates. The file watcher handles concurrent changes.

How it connects to what we’ve covered

SocratiCode is an MCP server — it sits in the same infrastructure layer as the agent tools we’ve been covering. Think of it as the “codebase memory” layer for AI coding agents, parallel to how soul.py is the conversational memory layer and Hindsight is the experience memory layer.

The hybrid search architecture (semantic + BM25 + RRF fusion) is the same pattern we covered in 7 RAG Patterns for 2026 — applied here specifically to code rather than documents. The AST-aware chunking is the code equivalent of the page-level indexing argument from our PageIndex vs Vector DBs post: respect the natural structure of the content instead of cutting at arbitrary boundaries.

And the dependency graph feature is what turns SocratiCode from a search tool into a structural understanding tool — something closer to Rikugan’s cross-reference analysis for reverse engineering, but applied to your own code.

Limitations worth knowing

Docker required — if you can’t run Docker in your environment (restricted corporate machine, CI without Docker), it won’t work
Initial indexing time — large codebases take time to index on first run; resumable checkpoints mean it recovers from interruption but the first pass on a multi-million line repo will take a while
Local embedding model — the default local embeddings are fast but smaller than cloud models; for the best semantic search quality on ambiguous queries, cloud embedding (OpenAI/Gemini) is an option
MCP host required — works with Claude Desktop, VS Code, Cursor, Windsurf, Cline, Codex CLI; doesn’t work in editors without MCP support
Repo: github.com/giancarloerra/socraticode
npm: npmjs.com/package/socraticode
License: MIT