Rapport

Open-source, local-first memory for the people in your life.

Record your real-world conversations, organize them around people — not time — and, when you need it, replay the facts, understand the other side, and think it through.

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🚧 Status: early development. M1–M4 work today — record → transcribe → local SQLite + full-text search, a people-centric bilingual (EN/中) desktop app (Today · conversation · people · relationship graph · review), and on-demand AI readings that run 100% locally via Ollama (no API key) and cite the original audio behind every judgment. Star the repo to follow along.

Stop guessing where you stand

• What does your boss really think of you?

• Do the people you lead actually respect you?

• Is the person you're seeing into it — or just being polite?

You guess. Everyone guesses. Rapport means you don't have to.

It keeps the real record of what was actually said — so you can replay the facts, see the moment from the other side, and get a straight answer to the question you couldn't crack. Not mind-reading; just the truth, kept.

Related MCP server: RewindMCP

What is Rapport?

Memory is fuzzy. After a conversation you keep an impression — and impressions drift: they fill in gaps, soften or sharpen, and quietly rewrite themselves over time.

Rapport keeps the real record instead. It records and transcribes your face-to-face conversations, organized around the people in your life, so you can go back to what was actually said — review it honestly, understand the other person, and see a moment from their side.

One idea underneath it all: the truth of what happened beats whatever you happened to remember.

What makes it different

• Always-on by design — it keeps the real record, not just the moments you remembered to hit record.

• Audio, not screen — your real-world, face-to-face conversations. Smaller footprint, sharper purpose.

• Organized around people, not time — profiles, relationships, and perspective-switching, instead of an endless timeline.

• 100% local & open source — the only kind of always-on recorder worth trusting: the data never leaves your machine, and the code is yours to read.

Privacy: 5 promises you can verify in the code

1. 100% local by default — audio and transcripts live in a SQLite file on your machine; nothing is uploaded.

2. No account required.

3. Your data is yours — export, delete, or back it up anytime. The database is just a file on your disk.

4. Sync is optional and end-to-end encrypted — leave it off and zero bytes leave your machine.

5. Local AI optional — run your questions through a local model (Ollama) so even the analysis stays on-device.

Because the code is open, you can read exactly what listens to you. That turns "trust us" into "trust the code."

Rapport records real people. Recording-consent laws vary by region — everything stays on your device, but using it lawfully is on you.

How it works

Record → local transcription → split & label speakers → encrypted local store
   → you ask a question → RAG retrieves the relevant bits → LLM answers → you

Rapport also exposes your data to the AI tools you already use, through a local REST API + MCP server — ask Claude Desktop "remind me what I last talked about with Alex" and it pulls from your local Rapport store. The data never leaves your machine.

The bigger idea: a human-context layer for your AI

An AI becomes a real assistant not by talking well, but by two things: knowing your real context, and being able to act on it.

Rapport owns the most private, hardest-to-get slice of that context — your real relationships and conversations with the people around you — and hands it, safely and on-device, to the AI tools you already use. So Claude or Cursor stop being clever strangers and start giving answers that actually fit your situation with the people in your life.

Others built memory search for your digital life. Rapport is the understanding layer for your human one.

Tech stack

Python · faster-whisper (local ASR) · pyannote (speaker diarization) · SQLite + FTS5 · pluggable LLM (local Ollama or your own API key) · FastAPI + MCP · React (Vite) SPA → PyWebview desktop shell. Windows first, macOS later.

Quick start

Python 3.10+ (3.12 recommended for the widest prebuilt-wheel coverage). Runs on CPU by default — no GPU or CUDA required.

git clone https://github.com/sssst1118/Rapport.git
cd Rapport
python -m venv .venv
# Windows:        .venv\Scripts\activate
# macOS / Linux:  source .venv/bin/activate
pip install -e .            # or:  uv pip install -e .

rapport transcribe path/to/audio.wav    # transcribe an audio file
rapport ui                              # or launch the web UI

First run downloads a small Whisper model. Choose a model size or enable GPU via env vars:

RAPPORT_WHISPER_MODEL=small rapport transcribe audio.wav   # tiny | base | small | medium | large-v3
RAPPORT_WHISPER_DEVICE=cuda  rapport transcribe audio.wav   # GPU (needs CUDA runtime libs)

Store a recording in your local database, then browse and search it:

rapport ingest audio.wav   # transcribe → store as a conversation
rapport show 1             # print a conversation's lines
rapport search "project"   # full-text search across everything
rapport devices            # list microphones (record --device N to pick one)

Roadmap

• M1 — Record + local transcription + simple UI ✅

• M2 — Local SQLite storage + full-text search + ingest ✅ (diarization seam ready; pyannote optional)

• Always-on capture — continuous background recording (the real record, not just manual clips)

• M3 — People-centric desktop app + annotations ✅ (FastAPI + React, bilingual EN/中: Today · conversation · people · relationship graph · review)

• M4 — On-demand AI readings ✅ (pluggable LLM — local Ollama, no API key, or bring your own; every reading separates fact from interpretation and cites the original quote + audio)

• M5 — Local REST API + MCP server + Windows packaging

• M6 — Voiceprint ID · macOS

License

AGPL-3.0 — free and open for everyone, forever. Its strong copyleft means no one can quietly close it up or "acquire it away."

Commercial licenses available — to use Rapport inside a closed-source / commercial product without AGPL obligations, contact the author at @sssst1118.

Star this repo ⭐

Rapport is in early development. If the idea resonates, star the repo to follow along — it genuinely helps.