Process unread email with local AI: classify messages, summarize them, draft replies, and check meeting conflicts. Edit queued drafts offline, then send approved messages through SMTP with explicit confirmation when connectivity returns.
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Local AI email assistant that automates unread-mail processing, offline-safe classification and reply drafting, local draft editing/approval queues, calendar conflict checks, voice transcription, and confirmed SMTP sending when connectivity returns. Use when the user wants to manage email, check inbox, draft or edit replies, queue messages offline, approve/send email, handle meeting invites, or mentions email assistant, IMAP, SMTP, voice memo, local AI email.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
A privacy-first email agent that reads, understands, and drafts replies to your emails. Text inference runs locally via OpenVINO GenAI with device priority NPU > GPU > CPU — no data leaves your machine.
Platforms: Windows · macOS · Linux (x86-64 and arm64)
IMAP Mailbox ──→ read_mailbox ──→ classify_summarize (OpenVINO GenAI, batched)
│
iCal Files ──→ check_calendar │
▼
Microphone ──→ start_voice_memo ──→ transcribe_memo (distil-whisper, optional)
│
▼
draft_reply (OpenVINO GenAI, 3 styles, single call)
Models: OpenVINO/Qwen3-4B-int4-ov (~2 GB, int4 OpenVINO format, auto-downloaded to state/models/) for classification and drafting; distil-whisper-large-v3-int8-ov (optional) for transcription.
Treat this as a cross-platform skill. Resolve SKILL_DIR from the loaded SKILL.md; never assume a Windows path or a particular shell. The bundled setup_auto.py detects OS, architecture, shell, package manager, Python, Intel NPU/OpenVINO devices, and local model availability, then installs only compatible components. setup.bat and setup.sh are thin wrappers.
Resolve this SKILL.md directory as SKILL_DIR; do not assume the current workspace contains the executable. On Windows/Qoder, prefer:
$skillDir = Join-Path $env:USERPROFILE ".qoder\skills\email-concierge"
& "$skillDir\venv\Scripts\python.exe" "$skillDir\email_concierge.py" --diagnose --json
Run setup_auto.py --project-dir "$skillDir" before automated inbox work, or when diagnostics report setup_required=true; setup installs missing components (including openvino-genai) and downloads the configured LLM model. Run --diagnose --json after setup. local_ai_ready defaults to true on supported Windows, macOS, and Linux systems. local_ai_installed reports whether OpenVINO, openvino-genai, and the model are all present. llm_device shows the device inference will use (highest available of NPU/GPU/CPU unless device pins one) and llm_model_downloaded whether the model files exist locally. NPU acceleration is ready only when aipc_ready=true and openvino_devices includes NPU.
The bootstrapper supports Windows PowerShell/CMD, macOS zsh/Bash, and Linux shells. It creates a local venv, installs core dependencies and OpenVINO GenAI via pip (no system elevation required), and downloads the LLM model directly from the ModelScope domestic source (https://modelscope.cn). Use --no-openvino or --no-llm-model only for constrained/offline environments. Setup also deletes the legacy EmailConcierge-Ollama logon task registered by older versions.
When executed without a TTY (e.g. by Qoder), setup_auto.py automatically enables --yes and skips system package manager installs on non-first installs. A marker file at state/.setup_completed.json records completion.
Performance notes: the first generation on a device compiles the model graph — on NPU this can take several minutes; the compiled cache in state/ov_cache/<device> makes subsequent runs start in seconds. Classification is one batched call for all emails and drafting is one merged call for all three styles (JSON-parsed), with keyword classification and template drafts as fallbacks so the workflow never blocks on model failure.
For inbox requests, run --inbox by default. It automatically reads unread mail without marking it read, falls back to cached mail while offline, classifies every message, checks calendar conflicts for meeting messages, creates local reply drafts, and persists them in state/draft_queue.json. If the local LLM is unavailable, create an editable deterministic draft and continue.
Only request user input for missing required IMAP/SMTP credentials, explicit reply instructions, draft edits, or final send confirmation. Never send automatically: require --confirm-send after the user has approved the queued draft. When offline, finish all local work and leave sending pending for a later --send.
A qoder.json manifest is included. Qoder can install the skill automatically with the platform-specific installer below. Existing email_concierge_config.json and state/ are preserved across reinstalls.
Windows
.\install_qoder.ps1
# Copy files only, without package/model installation
.\install_qoder.ps1 -NoSetup
All dependencies (openvino-genai included) install into the local venv and the model downloads to state/models/; no administrator elevation is needed. Subsequent reinstalls are silent and preserve email_concierge_config.json and state/.
From CMD, run install_qoder.bat; it delegates to the same PowerShell flow and returns the installer exit code.
macOS / Linux
bash install_qoder.sh
Everything installs into the local venv without root. PortAudio (for voice recording) is the only optional system package; install it with your package manager when needed.
After installation, restart Qoder or open a new agent session so it reloads SKILL.md.
Copy the skill directory into the target app's skill folder and run the bootstrapper:
python3 install_skill.py --target qoder --destination ~/.qoder/skills
# or
python3 install_skill.py --target codex --destination ~/.codex/skills
Use --no-setup to copy files without installing dependencies. The installer honours .qoderignore, merges upgrades, and skips venv/, __pycache__/, state/, tests/, and transient audio files. A non-zero setup result is returned to the caller and included in --json output.
python -m venv venv
venv\Scripts\pip install -r requirements.txt -r requirements-openvino.txt
venv\Scripts\python email_concierge.py --diagnose --json
python3 -m venv venv
venv/bin/pip install -r requirements.txt -r requirements-openvino.txt
venv/bin/python email_concierge.py --diagnose --json
For system audio recording on Unix, install PortAudio first:
# macOS
brew install portaudio
# Debian/Ubuntu
sudo apt install libportaudio2 portaudio19-dev
# Fedora/RHEL
sudo dnf install portaudio-devel
# Arch
sudo pacman -S portaudio
Run the interactive wizard only when IMAP details are missing. It asks only for the required host, email address, and app password; defaults cover the port, folder, model, calendar, and transcription settings.
# macOS / Linux
venv/bin/python email_concierge.py --setup
# Windows
venv\Scripts\python.exe email_concierge.py --setup
For unattended use, set local environment variables instead. Environment values override JSON configuration.
EMAIL_CONCIERGE_IMAP_HOST=imap.example.com
EMAIL_CONCIERGE_IMAP_PORT=993
EMAIL_CONCIERGE_IMAP_USER=you@example.com
EMAIL_CONCIERGE_IMAP_PASSWORD=your-app-password
EMAIL_CONCIERGE_IMAP_FOLDER=INBOX
Optional overrides use the same pattern: EMAIL_CONCIERGE_LLM_MODEL_ID, EMAIL_CONCIERGE_LLM_MODEL_DIR, EMAIL_CONCIERGE_OLLAMA_MODEL_TRANSCRIBE (optional Ollama transcription backend), EMAIL_CONCIERGE_CALENDAR_DIR, EMAIL_CONCIERGE_WHISPER_MODEL_DIR, EMAIL_CONCIERGE_DEVICE (AUTO/NPU/GPU/CPU), EMAIL_CONCIERGE_MAX_EMAILS, EMAIL_CONCIERGE_SMTP_HOST, EMAIL_CONCIERGE_SMTP_PORT, EMAIL_CONCIERGE_SMTP_USER, EMAIL_CONCIERGE_SMTP_PASSWORD, EMAIL_CONCIERGE_SMTP_USE_TLS, and EMAIL_CONCIERGE_STATE_DIR.
Key JSON fields:
| Field | Meaning |
|---|---|
llm_model_id | HuggingFace repo of the OpenVINO int4 model (default OpenVINO/Qwen3-4B-int4-ov) |
llm_model_dir | Explicit model directory override; empty → state/models/<repo-name> |
device | AUTO (default, NPU → GPU → CPU) or a pinned NPU/GPU/CPU |
ollama_model_transcribe | Optional Ollama model for transcription only (e.g. whisper); empty disables |
IMAP app passwords:
Fetches unread emails via IMAP.
python email_concierge.py --inbox
Classifies each email into todo|meeting|notification|promotion and generates a one-line summary. One batched OpenVINO GenAI call for all emails; keyword fallback when the model is unavailable. Runs automatically with --inbox.
Parses .ics files and finds events overlapping a target time.
python email_concierge.py --check-cal 2026-09-01
python email_concierge.py --check-cal today
Records audio from the default microphone.
python email_concierge.py --record 30
Saves 16 kHz mono WAV. Ctrl+C stops early.
Transcribes audio to text. Priority: distil-whisper (OpenVINO) → Ollama (if ollama_model_transcribe set) → transcribe-translate skill.
python email_concierge.py --transcribe voice_memo.wav
Generates 3-style replies (formal / friendly / concise) in a single merged OpenVINO GenAI call; per-style template fallback.
python email_concierge.py --queue --json
python email_concierge.py --edit MESSAGE_ID "Updated reply text" --style friendly
python email_concierge.py --approve MESSAGE_ID
python email_concierge.py --send
python email_concierge.py --send --confirm-send
python email_concierge.py --send --confirm-send --wait-for-network --retry-seconds 60
--send never transmits without both prior approval and --confirm-send. With --wait-for-network, keep retrying locally and send only those approved drafts after connectivity returns. Missing SMTP settings are reported as structured input requirements.
Reads every unread email, classifies it, checks calendar conflicts for meeting messages, and writes local reply drafts without sending mail.
python email_concierge.py --inbox
When disconnected, the same command uses the local mailbox cache and writes drafts to the queue. Re-run --send after connectivity is restored; inspect and approve drafts first.
Reports the exact missing configuration fields instead of prompting when IMAP configuration is unavailable.
python email_concierge.py --inbox --non-interactive --json
python email_concierge.py --inbox --email-index 1 --user-text "Accept the meeting, suggest 2pm instead"
python email_concierge.py --inbox --interactive
python email_concierge.py --inbox --interactive --voice --voice-duration 20
python email_concierge.py --inbox --output results.json --json
python email_concierge.py --check-cal today
python email_concierge.py --record 15
python email_concierge.py --transcribe voice_memo.wav
Voice transcription uses distil-whisper through OpenVINO on supported platforms (Windows x64, Linux x64, macOS x64/arm64 CPU).
pip install openvino "optimum[openvino]" transformers librosa
Set whisper_model_dir in config to the downloaded distil-whisper model path.
Use --device CPU (default), GPU, NPU, or AUTO for inference device selection.
Alternatively, set ollama_model_transcribe (e.g. whisper) to use a locally installed Ollama for transcription only. Ollama is never used for text inference.
| Issue | Fix |
|---|---|
| Model download fails or is slow | Downloads use https://modelscope.cn directly and resume interrupted files automatically. Check domestic network access to ModelScope and rerun setup. |
| First NPU generation takes minutes | Normal: one-time graph compilation; cached in state/ov_cache, later runs start in seconds |
| NPU/GPU init fails | Device chain auto-falls back NPU → GPU → CPU; pin device: GPU or CPU to skip a broken device |
openvino-genai import error | Version must match the openvino line; rerun pip install -r requirements-openvino.txt |
| Ollama transcription missing | Install Ollama separately and ollama pull whisper, or use whisper_model_dir instead |
| IMAP auth failed | Use app password, not account password |
sounddevice OSError (PortAudio) | See platform install steps above |
| No calendar events found | Export .ics files from Calendar app to configured calendar_dir |
| Windows venv not found | Use venv\Scripts\python.exe directly, not python |
icalendar not found | pip install icalendar inside venv |
Legacy EmailConcierge-Ollama startup task remains | Re-run setup (removes it automatically) or schtasks /Delete /TN EmailConcierge-Ollama /F |
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