Code Memory FAQs

Question 1

How is Code Memory different from traditional search tools like grep or ripgrep?

Accepted Answer

Code Memory goes beyond keyword matching by using semantic search to understand the meaning and intent behind your queries. It also analyzes Git history for architectural decisions, builds dependency graphs across 16 languages, and learns from your coding sessions to provide deeper, more relevant insights into your codebase.

Question 2

Can I integrate Code Memory with my existing AI coding assistants or code editors?

Accepted Answer

Yes! Code Memory functions as an MCP (Model Context Protocol) server. This design makes it compatible with any MCP-enabled tool, including the Claude Code CLI and other editors or AI assistants that support the MCP protocol, allowing for seamless integration into your workflow.

Question 3

Does Code Memory send my codebase to an external API or cloud service?

Accepted Answer

No, Code Memory operates entirely locally and offline. All code indexing, embedding generation (using fastembed), and search processes occur on your machine. Your code never leaves your computer, ensuring complete privacy and security.

Question 4

What kind of architectural decisions can Code Memory extract from Git history?

Accepted Answer

Code Memory's Git history analysis can extract various architectural decisions and their rationale, including explicit decisions in commit messages, design choices from PR descriptions, reasons for specific refactorings, and the context behind breaking changes, helping AI assistants understand the 'why' behind the code.

Question 5

How much disk space does Code Memory typically use?

Accepted Answer

For a typical project, Code Memory uses approximately 50MB per 10,000 files indexed. Additionally, the embedding model requires a one-time download of about 90MB. Total disk usage for an average project is generally between 150-300MB.

Code Memory

Code Memory

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Key Features

Use Cases