> ## Documentation Index
> Fetch the complete documentation index at: https://docs.memloom.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Use cases

> What you can do with a local memory engine

## Persistent memory for coding agents

This is one of the best use cases memloom was built for. Register memloom as an MCP server in Claude Code, Claude Desktop, or
Cursor ([setup](/mcp/setup)), and the agent gets `save_memory` / `recall_memory` tools backed by *your* store:

* The agent saves what it learns, like *"deploys go through `scripts/release.sh`, never manual"*
  (`procedure`) or *"user prefers using vue over next.js for frontend development"* (`preference`), and recalls it in later
  sessions.
* Because every agent talks to the **same daemon**, memory saved in Claude Code is there when
  you switch to Cursor, Codex, or any other MCP-compatible agent/IDE. The memory belongs to you, so you're not locked into any vendor.
* Dedup means agents can save enthusiastically without flooding the store, and contradiction
  detection means a confused agent can't silently overwrite a valid fact.

## Personal, unified second brain

Point memloom at your files and they become part of the same memory. If you know
NotebookLM: this is that idea, but local, open source, and shared with every agent
you connect.

```bash theme={null}
memloom context add ./notes
```

* Your .md/.txt/.pdf files are recallable by meaning *and* by exact phrase, with citations
  back to the file, section, and page.
* The store is the one place your knowledge lives. Every agent and assistant you connect
  reads from it, instead of each tool keeping its own partial copy.
* Edit a file and re-run `context add`: changed files are re-ingested, unchanged files are
  skipped (content-hash check), so the store stays in step with your folder.