ai-email

AI QQ Email Support Agent

An AI-powered email handling system for automating customer support over QQ email. The system automatically monitors new QQ emails, classifies them with an LLM (by intent and urgency), routes them to the appropriate handler, drafts a professional response, and sends the reply via SMTP. Complex or high-priority emails are escalated to a human and optionally pushed via the official QQ Bot.

Key Features

• Asynchronous monitoring of new QQ emails (IMAP polling)
• AI-powered classification by intent, urgency, and target terminal platform
• Prompt-embedded JSON Schema for classification with tolerant output parsing (bare JSON or fenced code block) — no API-level structured output required
• Automatic drafting of professional, accurate responses
• Automatic reply via SMTP
• Human escalation for complex or high-priority emails
• Optional QQ Bot push notifications (c2c / group) for escalations

Technical Architecture

The core workflow is implemented as a custom asynchronous state machine (EmailWorkflow) — it does not depend on the LangGraph library. It is built on:

• openai (AsyncOpenAI) — LLM classification & response drafting; compatible with OpenAI / DeepSeek / other OpenAI-compatible APIs
• aioimaplib — asynchronous IMAP email fetching
• aiosmtplib — asynchronous SMTP reply sending
• httpx — QQ Bot HTTP API client
• beautifulsoup4 — HTML email body cleaning
• tenacity — classification retry on failure
• python-dotenv — .env configuration loading
• Python 3.10+ (uses PEP 604 X | None union syntax)

Note: Push notifications use the official QQ Bot (c2c / group). The earlier Feishu (Lark) integration — both push and Bitable record persistence — has been removed; classification and handling results are kept in memory only. If you need audit logging, see "Extending Functionality" below.

Requirements

• Python 3.10+
• QQ email account with IMAP enabled and its authorization code (not the login password)
• API key for an OpenAI-compatible chat model (OpenAI, DeepSeek, etc.)
• (Optional) A QQ official Bot for push notifications

Installation

1. Clone the project

git clone <repository-url>
cd AIHandleQQEmail

2. Install with uv (recommended)

This project is managed with uv — install it first if you don't have it yet (see the uv installation guide).

uv sync                 # create .venv and install exactly what uv.lock pins (incl. dev group)

This installs the ai-email console entry point. Run it through uv run:

uv run ai-email --help

pip fallback: pip install . still installs the runtime dependencies (the dev toolchain — pytest / pyinstaller — is managed via uv dependency groups).

Configuration

The configuration file is stored at ~/.ai-email/.env (outside the project directory, so secrets are not committed). The recommended way to create it is the interactive setup wizard, which validates your model connection and QQ Bot credentials:

ai-email setup

For the QQ Bot notification section, the wizard uses scan-to-configure: it renders a QR code in the terminal, and after you scan it with QQ, the App ID, Client Secret, and your real openid (as the notification target) are filled in automatically. Manual entry is intentionally not offered — there is no API to look up an openid; it only surfaces via scan-binding or bot message events.

You may also copy .env.example to ~/.ai-email/.env and fill it in manually.

Required environment variables:

Notes:

• QQ email requires an authorization code instead of the login password; generate it in QQ email settings.
• MODEL, BASE_URL, API_KEY are configured according to your model provider.
• QQ Bot notifications are enabled only when all three QQ_* variables are set; placeholder values (e.g. the literal c2c:openid) are detected and treated as unset.

Usage

After uv run ai-email setup, use the CLI subcommands (prefix each with uv run, or activate .venv first):

uv run ai-email setup      # Interactive configuration wizard
uv run ai-email            # Run in the foreground
uv run ai-email daemon     # Run as a background daemon
uv run ai-email status     # Check daemon status
uv run ai-email stop       # Stop the daemon

Running the service starts the listener, which polls for new emails and processes each one through the workflow automatically.

Workflow

1. Email Monitoring: Asynchronously polls the QQ mailbox for new emails via IMAP UID-incremental fetch (only UIDs beyond the persisted last_uid), with a UIDVALIDITY guard that re-baselines safely if the mailbox's UID space changes.
2. Email Classification: The LLM analyzes the email and classifies it by intent, urgency, and terminal platform.
3. Routing: Emails are routed based on intent and urgency:
   • complex_request, or high/critical urgency → escalated to a human (with optional QQ Bot push)
   • question / feature → knowledge base search step
   • bug → ticket creation step (priority P0/P1/P2 assigned in memory)
4. Response Drafting: The LLM drafts a response based on the email content and handling results.
5. Email Sending: The reply is sent automatically via SMTP.

Email Classification Rules

The system classifies emails on the following dimensions:

• Intent (intent):

  • question: General inquiries
  • bug: Bug reports
  • building: Deployment-related issues
  • feature: Feature requests
  • complex_request: Complex requests requiring human handling

• Urgency (urgency):

  • low: Low priority
  • medium: Medium priority
  • high: High priority
  • critical: Critical

• Terminal (terminal): Web, Windows, Android, Mac, iOS, or Not provided

Reliability & State

The daemon keeps all state in SQLite at ~/.ai-email/seen.db (stdlib sqlite3, no extra dependencies), so a restart or crash never causes lost or duplicate processing:

• UID-incremental fetch — each poll pulls only UIDs greater than the persisted last_uid (UID SEARCH last_uid+1:*); last_uid is advanced as emails are fetched.
• UIDVALIDITY guard — the IMAP UIDVALIDITY value is persisted; if it changes (the mailbox's UID space rolled over), the dedup table and last_uid baseline are reset so reprocessing stays safe.
• Atomic dedup — each email is claimed with a single INSERT OR IGNORE (status='processing'), collapsing the check-then-insert race across concurrent workers; only the winning worker processes it.
• Crash reconciliation — on startup, reconcile_orphans rolls any email still in processing (interrupted mid-handling) back into the retry queue; emails already marked done are preserved.
• Retry with backoff — failed sends or exceptions go to a retry_queue and are re-fed into the pipeline after a backoff window; after more than 5 attempts the email is dropped as a dead letter.
• Bounded concurrency — up to WORKER_CONCURRENCY (default 4) emails are handled in parallel.

Runtime files under ~/.ai-email/:

Continuous Integration

The project includes a GitHub Actions workflow (.github/workflows/build-and-test.yml) that runs on push/PR to main and on releases, across a Python 3.10 / 3.13 matrix:

1. ruff check (lint) + ruff format --check (formatting) + mypy (type check)
2. pytest (test suite — no secrets required; tests/conftest.py supplies placeholder env)
3. uv build, artifact upload, and a wheel install + smoke test (ai-email --help)

No repository secrets are needed: the test suite never touches the network.

Project Structure

.
├── ai_email/
│   ├── __init__.py                # Package entry, re-exports main
│   ├── __main__.py                # Enables `python -m ai_email`
│   ├── cli.py                     # CLI: setup wizard, daemon/worker, stop/status, PID+lock liveness
│   ├── workflow.py                # Core engine: EmailWorkflow, LLM calls, routing, SMTP, pipeline
│   ├── qq_email_listener.py       # Async IMAP listener (UID-incremental fetch, UIDVALIDITY guard)
│   ├── persistence.py             # SQLite state: dedup ledger, last_uid/UIDVALIDITY, retry queue
│   ├── qq_bot.py                  # QQ official Bot notification client (OAuth + token cache)
│   ├── qq_onboard.py              # QQ Bot scan-to-configure onboarding (QR bind task, AES-GCM secret decrypt)
│   └── log_setup.py               # JSON single-line logging setup
├── tests/                         # pytest suite (workflow, listener, persistence, concurrency, ...)
├── pyproject.toml                 # Project metadata and dependencies
├── build.sh                       # Build helper (uv + PyInstaller packaging)
├── Dockerfile                     # Container image definition
├── .env.example                   # Template for ~/.ai-email/.env
└── .github/workflows/
    └── build-and-test.yml         # GitHub Actions build, lint & test configuration

Development Guide

Main Components

1. qq_email_listener.QQEmailListener — async generator that fetches new emails via IMAP UID-incremental search (with a UIDVALIDITY guard) and transparently reconnects on failure.
2. EmailWorkflow — the custom async workflow carrying WorkflowState through nodes:
   • classify_intent — LLM classification (JSON Schema embedded in prompt, tolerant JSON extraction, with retry)
   • search_knowledge_base — knowledge base lookup for question/feature (placeholder; swap the method body to wire up a RAG/vector-retrieval backend — contract: hits go into state.handle_results)
   • create_ticket — ticket creation for bug (placeholder; swap the method body to wire up a ticketing platform — contract: ticket number/link goes into state.handle_results)
   • to_human — human escalation (+ QQ Bot push) for complex/high-priority emails
   • draft_response — LLM response drafting
   • send_reply — SMTP reply
3. QQBotNotifier — QQ official Bot client with OAuth token caching and auto-refresh.
4. CLI — setup wizard (with online validation), plus daemon run/stop/status. Liveness is tracked by an exclusive lock on ~/.ai-email/ai-email.lock (held while the worker runs, so a recycled PID can't be mistaken for the daemon); the worker PID is also recorded in ~/.ai-email/ai-email.pid.

Extending Functionality

You can extend the following based on your requirements:

• Add more email classification types
• Integrate a RAG / vector-retrieval knowledge base behind search_knowledge_base
• Wire create_ticket to a ticket management platform
• Add multi-language support
• Enhance the human-review interface

Notes

1. IMAP service must be enabled for QQ email.
2. The QQ email authorization code must be used instead of the login password.
3. Ensure a stable network connection to access the AI API.
4. In production, consider adding additional error handling and logging.

License

This project is licensed under the MIT License — see the LICENSE file for details.