ai-browser-profile

ai-browser-profile

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

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

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

Usage

cd ~/ai-browser-profile && 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 ai-browser-profile update                             # updates code, preserves memories.db

Python API

from ai_browser_profile 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
ai_browser_profile/
  __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