AI coding assistants can significantly improve their reasoning capabilities when provided with a comprehensive knowledge graph of the entire codebase, rather than reading files individually. This knowledge graph approach enables AI agents to understand code relationships, dependencies, and architectural decisions, resulting in faster response times (37x improvement), reduced token consumption (61% reduction), and more accurate answers. The system uses AST-aware chunking to split code at meaningful boundaries and hybrid search combining vector embeddings with keyword matching to provide comprehensive codebase understanding.
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THE BEST TOOL FOR AI AGENTS | BETTER THAN GRAPHIFYAdded:
Your AI assistant is brilliant. It can write code, fix bugs, refactor anything you show it. But there's a problem, a fundamental one. Your AI reads files one at a time. It has no map to your code base, no understanding of relationships, no awareness of history. Ask it what breaks if you rename a function and it guesses. Ask it about an API contract across three services and it guesses again. This is not a model failure. This is an information failure. The model is capable but ignorant. Ignorant of your architecture, ignorant of your dependencies, ignorant of the decisions that shaped your system over years.
Every developer team hits this wall and it slows everything down. Sacratis Code is a code base intelligence engine built for AI assistants. It is named after a principle borrowed from Socrates himself. There is only one good, knowledge, and one evil, ignorance. Your AI should not be ignorant of your code base. Sacratis Code fixes that. It runs as a model context protocol server. One command and every compatible agent, Claude, Cursor, Gemini CLI, VS Code Copilot, Windsurf client, immediately gains deep structured understanding of your entire project. Not just file reading, semantic search, dependency graphs, impact analysis, context artifacts, including database schemas, OpenAPI specs, and infrastructure configs. Index once, use across every tool you work with. No lock-in, no black box. At its core, Sacratis Code runs a hybrid search engine. Dense vector search captures semantic meaning. Ask about authentication logic and it finds it even if the file is named something unrelated. BM25 handles exact identifiers. Function names, variable names, class definitions, searched literally and fast. These two signals fuse together using reciprocal rank fusion, giving you the best of both worlds in every query.
But the real innovation is how it understands code structure. Secret eye code uses AST grep for AST aware chunking. Files are not split at arbitrary line counts. They're split at function boundaries, class definitions, and meaningful code units. The result is higher quality context at a fraction of the token cost. Your AI gets better answers and uses fewer resources doing it. Secret eye code builds a full polyglot dependency graph of your entire code base. It analyzes import, export, require, use, and include statements across 18 or more languages using AST grep. JavaScript, TypeScript, Python, Go, Rust, Java, Kotlin, C#, Ruby, PHP, Swift, and more. All of them without external tools. When you change a symbol, Secret eye code shows you the blast radius. Every function, every module, every service that will be affected by that change. It detects circular dependencies automatically and generates mermaid diagrams you can paste anywhere. It also serves an interactive HTML graph you can navigate directly in your browser. This is symbol level impact analysis before you write a single line. Not just syntactically valid changes, but structurally correct ones. These are not hypothetical benchmarks. The Secret eye code team tested it against the VS Code code base.
2.45 million lines of code. More than 5,000 files. 55,000 index chunks.
Running on Claude Opus. Compared to standard grep based AI exploration, the results were clear. 37 times faster response time, not 37% 37 times. 84% fewer tool calls per query. The model reached the answer in a fraction of the steps. 61% less context consumed. Smaller, tighter, more accurate answers. SocratAI Code handles 40 million lines of code or more.
Enterprise scale running entirely on your own hardware. The numbers speak.
The architecture delivers. Getting started takes one command. Type NPX why SocratAI Code and the engine starts. To connect it to Claude, type Claude MCP add SocratAI Code two dashes NPX why SocratAI Code. SocratAI Code automatically pulls and starts Qdrant for vector storage and Ollama for local embeddings. No Docker Compose files to write. No configuration to manage. No infrastructure to maintain. Your code never leaves your machine. No API keys required for the local mode. Complete privacy by default. You can also choose remote embeddings if you prefer using OpenAI or Google Gemini. Beyond source code, SocratAI Code indexes everything that matters. Database schemas, OpenAPI specifications, Terraform configs, Dockerfiles, Kubernetes manifests. The full picture. It watches your files.
When code changes, the index updates automatically. SocratAI Code is free and open source. You can find on GitHub under Jean Carlo Era {slash} SocratAI Code or install it right now at socrat.ai site. It works with Claude Code, Cursor, VS Code Co-pilot, Gemini CLI, WinSurf, Cline, and Roo Code. Any tool that supports the Model Context Protocol. The VS Code and Cursor extensions auto-register the MCP server.
No manual configuration needed. Open the extension and you are ready. There's a philosophy behind this project and it matters. Socrates believed that ignorance was the only real obstacle to good action. Your AI agent does not need to be ignorant of your code base. It never did. Give it the knowledge. Watch what it can do. So, Cradcode.site.
Go get knowledge.
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