→ Live sample report

I had 5 years of WhatsApp conversationssitting on my phone and realized I had no idea what was actually in them. Not the events — I remembered those. But the patterns. Who I was at 2am. How I talked to people I was in love with. Whether I'd changed at all.

So I built this.

Parses your exported WhatsApp chats and tells you things like:

• words and phrases you repeat without noticing
• who always writes first (you or them)
• how the balance shifted over months
• your emoji signature
• the longest silences, and who broke them
• how your vocabulary changed year to year

Optional: plug in an OpenAI or Anthropic key and have a conversation about what it all means.

pip install chatself
# with AI support:
pip install "chatself[ai]"

Export a chat from WhatsApp: Settings → Chats → Export chat → Without media

# single chat
chatself analyze "Chat with Ana.txt" --name Ana

# whole folder
chatself analyze ./exports/ --name Ana

# save an HTML report
chatself analyze "Chat with Ana.txt" --name Ana --html report.html

# talk to an AI about it (needs ANTHROPIC_API_KEY or OPENAI_API_KEY)
chatself analyze "Chat with Ana.txt" --name Ana --ai anthropic

The --ai flag sends only pre-computed stats to the LLM — not your actual messages.

If you want everything, not just one export, you can use the decrypted msgstore.db from your Android backup. chatself includes a DbParser for that. Instructions coming in the wiki.

Everything runs locally. No accounts, no telemetry, no cloud. The only exception is --ai, which sends anonymized summaries (word frequencies, ratios, timestamps) to the API you choose.

Open issues, send PRs. The codebase is small and straightforward — parsers, analyzers, a CLI, an HTML builder.

MIT