user-memories

Extract what your browser knows about you into a self-ranking SQLite database. Reads autofill, login data, browsing history, bookmarks, WhatsApp contacts, LinkedIn connections, and Notion workspaces — directly from local browser files.

What it extracts

| Source | Data | Browser files | |--------|------|---------------| | Web Data | Autofill, addresses, credit cards | Web Data SQLite | | Login Data | Accounts, emails, usernames | Login Data SQLite | | History | Tool/service usage frequency | History SQLite | | Bookmarks | Interests, saved tools | Bookmarks JSON | | IndexedDB | WhatsApp contacts | LevelDB via ccl_chromium_reader | | Local Storage | LinkedIn connections | LevelDB via ccl_chromium_reader | | Notion | Workspace users, pages | IndexedDB |

Supported browsers: Arc, Chrome, Brave, Edge, Safari, Firefox.

Install

npx user-memories init                               # sets up ~/user-memories, Python venv, core deps
npx user-memories install-embeddings                  # optional: semantic search (~180MB)

Requires Python 3.10+ and Node.js 16+. macOS only (reads from ~/Library/Application Support/).

This creates ~/user-memories/ with a Python venv, installs dependencies, and symlinks Claude Code skills to ~/.claude/skills/.

Usage

cd ~/user-memories && source .venv/bin/activate
python extract.py                                    # scan all browsers
python extract.py --browsers arc chrome              # specific browsers
python extract.py --no-indexeddb --no-localstorage   # skip LevelDB (faster)
python extract.py --output /path/to/memories.db      # custom output path

To update after a new release:

npx user-memories update                             # updates code, preserves memories.db

Python API

from user_memories import MemoryDB, extract_memories

# Extract from browsers
mem = extract_memories("memories.db")

# Query
mem.search(tags=["identity", "contact_info"])
mem.text_search("github")
mem.semantic_search("what tools do I use most")

# Profile summary
print(mem.profile_text())

# History + supersession chain
mem.history("email")

How it works

Self-ranking — each memory tracks appeared_count (how often it was seen during extraction) and accessed_count (how often it was queried). The ratio accessed_count / appeared_count is the hit_rate, used to surface the most relevant memories.

Semantic dedup — new entries are compared against existing ones using nomic-embed-text-v1.5 embeddings (768-dim, ONNX Runtime). If cosine similarity >= 0.92 with the same key prefix, the old entry is superseded rather than duplicated.

Key schema — memories use structured keys with cardinality rules:

• Single-value (first_name, last_name, full_name, ...): new values automatically supersede old ones
• Multi-value (email, phone, account:github.com, tool:vscode, ...): multiple values coexist

Entity linking — accounts sharing the same username/email are automatically linked via same_identity relations.

Schema

memories (id, key, value, confidence, source, appeared_count, accessed_count,
          created_at, last_appeared_at, last_accessed_at, superseded_by,
          superseded_at, search_text, reviewed_at)

memory_tags (memory_id, tag)     -- identity, contact_info, address, payment,
                                 -- account, tool, contact, work, knowledge,
                                 -- communication, social, finance

memory_links (source_id, target_id, relation, created_at)

memory_embeddings (memory_id, embedding)  -- 768-dim BLOB

Project structure

extract.py                          # CLI entry point
user_memories/
  __init__.py                       # exports MemoryDB, extract_memories
  db.py                             # MemoryDB: schema, upsert, search, profile
  embeddings.py                     # ONNX Runtime embeddings + cosine search
  extract.py                        # extraction orchestrator
  ingestors/
    browser_detect.py               # find browser profiles
    constants.py                    # lookup maps, browser paths
    webdata.py                      # autofill, addresses, credit cards
    history.py                      # browsing history → tool usage
    logins.py                       # saved logins → accounts
    bookmarks.py                    # bookmarks → interests
    indexeddb.py                    # WhatsApp contacts
    localstorage.py                 # LinkedIn connections
    notion.py                       # Notion workspace data
    messages.py                     # message extraction

License

MIT