code-memory 1.0.9

A deterministic, high-precision code intelligence MCP server

code-memory

A deterministic, high-precision code intelligence layer exposed as a Model Context Protocol (MCP) server.

code-memory gives your AI coding assistant structured access to your codebase through three focused pathways — eliminating context-window bloat and vague "search everything" queries.

Supported Languages

Full AST Support (Tree-sitter)

These languages have structural parsing with symbol extraction (functions, classes, methods, etc.):

Language	Extensions
Python	.py
JavaScript	.js, .jsx
TypeScript	.ts, .tsx
Java	.java
Go	.go
Rust	.rs
C	.c, .h
C++	.cpp, .hpp, .cc, .cxx
Ruby	.rb
Kotlin	.kt, .kts

Fallback Support (Whole-file Indexing)

These file types are indexed as complete units for BM25 and semantic search:

Category	Extensions
C#	.cs
Swift	.swift
Scala	.scala
Lua	.lua
Shell	.sh, .bash, .zsh
Config	.yaml, .yml, .toml, .json
Web	.html, .css, .scss
Database	.sql
Docs	.md, .txt

Note: Files and directories matching patterns in your .gitignore are automatically skipped during indexing. This excludes build artifacts, dependencies, and other generated files.

Architecture: Progressive Disclosure

Instead of a single monolithic search, code-memory routes queries through three purpose-built tools:

Question Type	Tool	Data Source
"Where / What / How?" — find definitions, references, structure, semantic search	search_code	BM25 + Dense Vector (SQLite vec)
"Architecture / Patterns" — understand architecture, explain workflows	search_docs	Semantic / Fuzzy
"Who / Why?" — debug regressions, understand intent	search_history	Git + BM25 + Dense Vector (SQLite vec)
"Setup / Prepare" — index parsing & embedding generation	index_codebase	AST Parser + sentence-transformers

This forces the LLM to pick the right retrieval strategy before any data is fetched.

Installation

From PyPI (Recommended)

# Install with pip
pip install code-memory

# Or with uvx (for MCP hosts)
uvx code-memory

From Source

# Clone the repo
git clone https://github.com/kapillamba4/code-memory.git
cd code-memory

# Install dependencies
uv sync

# Run the MCP server (stdio transport)
uv run mcp run server.py

Quickstart

Prerequisites

• Python ≥ 3.13
• uv package manager (recommended) or pip

Install & Run

# Install from PyPI
pip install code-memory

# Or run directly with uvx
uvx code-memory

Development

# Run with the MCP Inspector for interactive debugging
uv run mcp dev server.py

# Run tests
uv run pytest tests/ -v

# Lint and format
uv run ruff check .
uv run ruff format .

# Build package
uv build

Configure Your MCP Host

Gemini CLI / Gemini Code Assist

Add to your MCP settings (e.g. ~/.gemini/settings.json):

{
  "mcpServers": {
    "code-memory": {
      "command": "uvx",
      "args": ["code-memory"]
    }
  }
}

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "code-memory": {
      "command": "uvx",
      "args": ["code-memory"]
    }
  }
}

Claude Code (CLI)

Add to .mcp.json in your project root or ~/.mcp.json for global access:

{
  "mcpServers": {
    "code-memory": {
      "command": "uvx",
      "args": ["code-memory"]
    }
  }
}

VS Code (Copilot / Continue)

Add to .vscode/mcp.json in your workspace:

{
  "servers": {
    "code-memory": {
      "command": "uvx",
      "args": ["code-memory"]
    }
  }
}

Configuration

Environment Variables

Variable	Description	Default
CODE_MEMORY_LOG_LEVEL	Logging verbosity (DEBUG, INFO, WARNING, ERROR)	INFO

Example:

CODE_MEMORY_LOG_LEVEL=DEBUG uvx code-memory

Tools

index_codebase

Indexes or re-indexes source files and documentation in the given directory. Run this before using search_code or search_docs to ensure the database is up to date. Uses tree-sitter for language-agnostic structural extraction and generates dense vector embeddings using sentence-transformers (runs locally, in-process) for semantic search.

index_codebase(directory=".")

search_code

Perform semantic search and find structural code definitions, locate where functions/classes are defined, or map out dependency references (call graphs). Uses hybrid retrieval (BM25 + vector embeddings) to find exact matches and semantic similarities.

search_code(query="parse python files", search_type="definition")
search_code(query="how do we establish the database connection", search_type="references")
search_code(query="src/auth/", search_type="file_structure")

search_docs

Understand the codebase conceptually — how things work, architectural patterns, SOPs. Searches markdown documentation, READMEs, and docstrings extracted from code.

search_docs(query="how does the authentication flow work?")
search_docs(query="installation instructions", top_k=5)

search_history

Debug regressions and understand developer intent through Git history.

search_history(query="fix login timeout", search_type="commits")
search_history(query="src/auth/login.py", search_type="file_history", target_file="src/auth/login.py")
search_history(query="server.py", search_type="blame", target_file="server.py", line_start=1, line_end=20)

Project Structure

code-memory/
├── server.py          # MCP server entry point (FastMCP)
├── db.py              # SQLite database layer with sqlite-vec
├── parser.py          # Tree-sitter-based code parser
├── doc_parser.py      # Markdown documentation parser
├── queries.py         # Hybrid retrieval query layer
├── git_search.py      # Git history search module
├── errors.py          # Custom exception hierarchy
├── validation.py      # Input validation functions
├── logging_config.py  # Structured logging configuration
├── tests/             # Test suite
├── pyproject.toml     # Project metadata & dependencies
└── prompts/           # Milestone prompt engineering files

Troubleshooting

"Git repository not found" error

Make sure you're running search_history from within a git repository. The tool searches upward from the current directory to find .git.

Empty search results

Run index_codebase(directory=".") first to index your code and documentation. The index is stored locally in code_memory.db.

Slow indexing

Indexing generates embeddings using a local sentence-transformers model. The first run downloads the model (~90MB). Subsequent runs are faster.

Embedding model errors

Ensure you have enough disk space and memory. The all-MiniLM-L6-v2 model requires ~500MB RAM when loaded.

Roadmap

• Milestone 1 — Project scaffolding & MCP protocol wiring
• Milestone 2 — Implement search_code with AST parsing + SQLite + sqlite-vec
• Milestone 3 — Implement search_history with Git integration
• Milestone 4 — Implement search_docs with semantic search
• Milestone 5 — Production hardening & packaging

Contributing

See CONTRIBUTING.md for development setup and guidelines.

Changelog

See CHANGELOG.md for version history.

License

MIT