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by pi22by7 • Search & Retrieval
An MCP server that provides persistent, local codebase memory, pattern learning, and semantic tools for AI coding assistants.
Persistent, local memory of a codebase's patterns and architecture to produce context-aware suggestions across sessions
Semantic search and file retrieval (AST-aware) to locate relevant code by meaning rather than keywords
Pattern-consistent recommendations (naming, signatures, project organization) when generating or refactoring code
In Memoria analyzes codebases (AST parsing + pattern learning) and exposes learned intelligence via the Model Context Protocol so AI assistants can access persistent memory across sessions. It extracts naming conventions, architectural preferences, and semantic relationships, stores structured and vector data locally (SQLite + SurrealDB), and offers 17 MCP tools for search, analysis, and recommendations. The server runs locally (Rust core + TypeScript MCP layer), integrates with tools like Copilot and Claude, and helps agents give context-aware, style-consistent suggestions without re-analyzing the repo every session.
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