# Email Assistant — Full Project Walkthrough

Everything built so far, organized by component. Last updated: **April 4, 2026**.

---

## 1. Project Overview

The **AI Email Assistant** is an OpenEnv-compliant email triage and response environment built for the **OpenEnv Hackathon**. It wraps a production-grade FastAPI backend into a Gymnasium-style `EmailEnv` environment that AI agents can interact with, be benchmarked on, and scored against a dense reward rubric.

```mermaid
graph TD
    A["Agent / LLM"] -- Action --> B["EmailEnv (OpenEnv)"]
    B -- "Observations + Rewards" --> A
    B -- "Internal Calls" --> C["EmailAssistantCore"]
    C -- "AI Logic" --> D["AIEngine"]
    C -- "Persistence" --> E["SQLite Memory"]
    B -- "Task Logic" --> F["Task Handlers"]
    B -- "Scoring" --> G["Rubric Graders"]
    H["Next.js Frontend"] -- "HTTP" --> I["FastAPI Routes"]
    I -- "Delegates" --> C
    J["inference.py"] -- "OpenEnv Client" --> B
```

---

## 2. Backend — Core Application (`app/`)

### 2.1 Entry Point — [main.py](file:///c:/Users/gaura/OneDrive/Documents/New%20project/email-assistant/app/main.py)

- Creates a **FastAPI** app (`v1.0.0`) with:
  - CORS middleware (all origins for dev)
  - API routes from `app/routes.py`
  - OpenEnv environment routes from `app/openenv_env/api.py`
  - Optional static frontend mount at `/ui`
  - Custom Swagger UI at `/docs` with branded CSS
  - Health + root endpoints

### 2.2 Service Layer — [core.py](file:///c:/Users/gaura/OneDrive/Documents/New%20project/email-assistant/app/core.py)

The `EmailAssistantCore` class is the central service powering both HTTP routes and the OpenEnv environment directly. It provides:

| Method | Description |
|--------|-------------|
| `fetch_emails()` | Fetches unread emails via IMAP and persists them to SQLite |
| `classify_email()` | Classifies email intent (Support/Sales/Spam/General) via `AIEngine` |
| `generate_reply()` | Drafts a reply with tone control + thread context from SQLite memory |
| `send_email()` | Sends via SMTP and records the result |

### 2.3 AI Engine — [ai_engine.py](file:///c:/Users/gaura/OneDrive/Documents/New%20project/email-assistant/app/ai_engine.py)

OpenAI API wrapper (`AIEngine` class) with:
- **Async-friendly execution** (runs sync OpenAI SDK in `asyncio.to_thread`)
- **Retry with exponential backoff** (3 attempts, 0.6s × 2^i delay)
- **`classify_intent()`**: Structured JSON prompting → `{intent, confidence, reasoning}`
- **`generate_reply()`**: Context-aware reply generation with tone control (formal/casual/neutral)
- **Graceful fallback**: Returns dummy responses when no valid API key is provided
- Auto-detects support for `responses.create` (newer SDK) vs `chat.completions.create`

### 2.4 Memory / Persistence — [memory.py](file:///c:/Users/gaura/OneDrive/Documents/New%20project/email-assistant/app/memory.py)

SQLite-backed conversation memory with three tables:

| Table | Purpose |
|-------|---------|
| `emails` | Stores incoming emails (indexed by `from_email + subject`) |
| `generated_replies` | Stores AI-generated reply drafts |
| `sent_emails` | Logs sent email status (sent/failed) |

Key function: **`load_thread_context()`** — Retrieves chronological conversation history (user + assistant messages) for a given thread, enabling context-aware replies.

Auto-rebuilds corrupt/invalid SQLite databases on startup.

### 2.5 API Routes — [routes.py](file:///c:/Users/gaura/OneDrive/Documents/New%20project/email-assistant/app/routes.py)

| Endpoint | Method | Description |
|----------|--------|-------------|
| `/health` | GET | Health check |
| `/emails` | GET | Fetch unread emails via IMAP |
| `/classify` | POST | Classify email intent |
| `/generate-reply` | POST | Generate AI-powered reply |
| `/send` | POST | Send email via SMTP |

### 2.6 Supporting Modules

- **[schemas.py](file:///c:/Users/gaura/OneDrive/Documents/New%20project/email-assistant/app/schemas.py)**: Pydantic models for `EmailMessage`, `ClassifyRequest/Response`, `GenerateReplyRequest/Response`, `SendEmailRequest/Response`, `Tone`, `Intent` types
- **[classifier.py](file:///c:/Users/gaura/OneDrive/Documents/New%20project/email-assistant/app/classifier.py)**: `EmailClassifier` wrapper around `AIEngine.classify_intent()`
- **[email_reader.py](file:///c:/Users/gaura/OneDrive/Documents/New%20project/email-assistant/app/email_reader.py)**: IMAP email fetcher (unread emails from inbox)
- **[email_sender.py](file:///c:/Users/gaura/OneDrive/Documents/New%20project/email-assistant/app/email_sender.py)**: SMTP email dispatcher
- **[utils.py](file:///c:/Users/gaura/OneDrive/Documents/New%20project/email-assistant/app/utils.py)**: Logging setup, subject normalization

---

## 3. OpenEnv Environment (`app/openenv_env/`)

This is the core innovation — the Gymnasium-style environment wrapper.

### 3.1 Environment — [env.py](file:///c:/Users/gaura/OneDrive/Documents/New%20project/email-assistant/app/openenv_env/env.py) (591 lines)

The `EmailEnv` class implements `Environment[Action, Observation, EmailEnvState]`:

**Core API:**
- `reset(task_id)` → Returns initial `Observation`
- `step(action)` / `astep(action)` → Returns `(Observation, Reward, done, info)`
- `state` property → Full `EmailEnvState`

**Action Handling:**
| Action Type | What It Does |
|-------------|-------------|
| `classify` | Classifies email intent (tool-driven or agent-provided) |
| `prioritize` | Sets priority for a specific message |
| `reply` | Drafts a reply (tool-driven or agent-provided) |
| `send` | Sends the drafted reply (dry-run or live SMTP) |
| `skip` | Skips email (optionally escalates) |

**Reward System — Multi-Dimensional Dense Rewards:**
```
reward = 0.3 × classification_score + 0.3 × priority_score + 0.3 × reply_score − penalties
```

Penalties include: unnecessary duplicate actions, wrong send target, delay/SLA violations, escalation, errors.

**Features:**
- Simulated time tracking (each action costs time: classify=1min, reply=4min, etc.)
- SLA deadline enforcement with urgency scoring
- Deterministic mode (no LLM needed) + Live mode (uses `EmailAssistantCore`)
- Dry-run send mode for evaluation

### 3.2 Tasks — [tasks.py](file:///c:/Users/gaura/OneDrive/Documents/New%20project/email-assistant/app/openenv_env/tasks.py)

Three benchmark tasks with hardcoded email fixtures:

| Task ID | Difficulty | Description | Max Steps |
|---------|-----------|-------------|-----------|
| `easy_classification` | Easy | Classify a single email (expected: "Support") | 4 |
| `medium_prioritization` | Medium | Pick most urgent from 3 emails (invoice w/ 60min deadline) | 5 |
| `hard_workflow` | Hard | Full workflow: classify 2 emails → prioritize → draft → send for VIP outage | 10 |

### 3.3 Models — [models.py](file:///c:/Users/gaura/OneDrive/Documents/New%20project/email-assistant/app/openenv_env/models.py)

Rich type system built on Pydantic + OpenEnv base types:

- `Action` (type + payload dict)
- `Observation` (current email, inbox summary w/ deadlines + urgency, action history, step count, time elapsed)
- `InboxSummaryItem` (message_id, urgency_score, deadline_minutes, predicted_intent, handled status)
- `Reward` / `RewardComponents` (classification, priority, reply scores + penalty dict)
- `EmailEnvState` (full state: task, time, intents, drafts, sent, history)
- `StepInfo` / `StepResult` (episode-level scoring)

### 3.4 Graders — [graders.py](file:///c:/Users/gaura/OneDrive/Documents/New%20project/email-assistant/app/openenv_env/graders.py)

Scoring functions:
- `grade_classification(predicted, expected)` → 0.0 or 1.0 exact match
- `grade_priority(chosen, expected)` → 0.0 or 1.0
- `grade_reply(body, keywords)` → 0.0–1.0 based on keyword coverage

### 3.5 Deterministic Engine — [deterministic_engine.py](file:///c:/Users/gaura/OneDrive/Documents/New%20project/email-assistant/app/openenv_env/deterministic_engine.py)

Keyword-based, no-LLM classification and reply generation for reproducible evaluation. Enables running the full pipeline without an OpenAI API key.

### 3.6 API Router — [api.py](file:///c:/Users/gaura/OneDrive/Documents/New%20project/email-assistant/app/openenv_env/api.py)

FastAPI router exposing the OpenEnv environment at `/openenv/*`:
- `POST /openenv/reset` → Reset environment
- `POST /openenv/step` → Execute action
- `GET /openenv/state` → Get current state

---

## 4. Extended Server Environment (`server/`)

### [my_environment.py](file:///c:/Users/gaura/OneDrive/Documents/New%20project/email-assistant/server/my_environment.py) (427 lines)

A **secondary environment** (`MyEmailEnvironment`) extending `EmailEnv` with:
- Custom `EmailAssistantRubric` (categorize: +0.10, respond: +0.50, move_email: +1.00, skip: −0.10, etc.)
- Simulated inbox loaded from `server/simulated_emails.json` or programmatic fallback
- Extended action types: `categorize`, `respond`, `create_folder`, `move_email`, `tag_email`, `schedule`, `archive`, `delete`
- Inbox-zero done condition
- Full state tracking via `EmailAssistantState` dataclass

---

## 5. Client & Inference

### 5.1 Client — [client.py](file:///c:/Users/gaura/OneDrive/Documents/New%20project/email-assistant/client.py)

`EmailAssistantEnvClient` extending OpenEnv's `EnvClient`:
- Serializes `Action` → JSON for step payloads
- Parses server responses → `StepResult[Observation]`
- Parses state → `EmailEnvState`

### 5.2 Inference Script — [inference.py](file:///c:/Users/gaura/OneDrive/Documents/New%20project/email-assistant/inference.py)

Full evaluation loop that:
1. Connects to the running environment server
2. Runs all 3 tasks (easy → medium → hard) or a specific task
3. Uses **OpenAI GPT-4o-mini** for action selection (with structured JSON prompting)
4. Falls back to **deterministic policy** when no API key is available
5. Logs step-level and episode-level metrics
6. Reports average reward across all episodes

CLI options: `--base-url`, `--episodes`, `--max-steps`, `--model`, `--task`

---

## 6. Frontend (`frontend/`)

### 6.1 Framework

**Next.js** app with:
- TypeScript
- Tailwind CSS + custom CSS variables
- `date-fns` for relative timestamps

### 6.2 Design System — [globals.css](file:///c:/Users/gaura/OneDrive/Documents/New%20project/email-assistant/frontend/app/globals.css)

Premium **"Elite Concierge"** glassmorphic design:

| Token | Light | Dark |
|-------|-------|------|
| Background | `#F9F7F3` (warm off-white) | `#0A0A0B` (deep black) |
| Primary | `#C49A6C` (warm gold) | `#E2C28B` (soft gold) |
| Accent | `#9B59B6` (purple) | `#C39BD3` (lavender) |
| Surface | White glass (80% opacity) | Dark glass (60% opacity) |

Typography: **Playfair Display** (headings), **Inter** (body), **JetBrains Mono** (code)

Components: `.glass-card`, `.glass-panel`, `.btn-primary`, `.btn-outline`, `.btn-icon`, `.luxury-input`, `.luxury-tag`

All with smooth animations, hover effects, glassmorphism (backdrop-filter blur), and custom scrollbars.

### 6.3 Main Page — [page.tsx](file:///c:/Users/gaura/OneDrive/Documents/New%20project/email-assistant/frontend/app/page.tsx)

Single-page **Luxury Concierge** email client:

- **Header**: "Elite Concierge — Executive Inbox" with tab navigation (Inbox / Concierge)
- **Sidebar**: Email list with sender, subject, body preview, relative timestamps, intent badges + confidence %
- **Main Panel**: Selected email details with:
  - "Engage Concierge" button → auto-classifies + drafts reply
  - AI reasoning display
  - Full email body
  - Composition area with editable draft
  - "Dispatch Securely" send button
- **Toast notifications** with animated pulse dot
- **Decorative blur orbs** for premium atmosphere

Integrates with backend via: `/emails`, `/classify`, `/generate-reply`, `/send`

---

## 7. Extended Backend (`backend/`)

A separate, more production-focused backend with:

| Directory | Purpose |
|-----------|---------|
| `app/api/v1`, `app/api/v2` | Versioned API routers |
| `app/core/` | Core business logic |
| `app/services/` | 16 service modules (see below) |
| `app/db/` | Database layer |
| `app/schemas/` | Pydantic schemas |
| `app/security/` | Authentication & authorization |
| `app/middleware/` | Request middleware |
| `app/observability/` | Monitoring & logging |
| `app/repositories/` | Data access layer |
| `app/workers/` | Background workers |
| `app/errors/` | Error handling |
| `alembic/` | Database migrations |

### Backend Services (`backend/app/services/`)
| Service | Description |
|---------|-------------|
| `agent_tools.py` | AI agent tool definitions |
| `attachments.py` | Attachment handling |
| `autopilot.py` | Automated email processing |
| `classifier.py` | Email classification |
| `evaluation.py` | Performance evaluation |
| `events.py` | Event system |
| `gmail_api.py` | Gmail API integration |
| `graph_api.py` | Microsoft Graph API |
| `imap_legacy.py` | Legacy IMAP support |
| `llm.py` | LLM integration |
| `niche.py` | Specialized processing |
| `prioritizer.py` | Email prioritization |
| `rag_service.py` | RAG-based knowledge retrieval |
| `safety.py` | Content safety checks |
| `smtp_send.py` | SMTP sending |

---

## 8. OpenEnv Client Package (`email_assistant_env/`)

Standalone Python package for interacting with the environment:
- [client.py](file:///c:/Users/gaura/OneDrive/Documents/New%20project/email-assistant/email_assistant_env/client.py) — Environment client
- [models.py](file:///c:/Users/gaura/OneDrive/Documents/New%20project/email-assistant/email_assistant_env/models.py) — Shared data models
- [pyproject.toml](file:///c:/Users/gaura/OneDrive/Documents/New%20project/email-assistant/email_assistant_env/pyproject.toml) — Package configuration
- [README.md](file:///c:/Users/gaura/OneDrive/Documents/New%20project/email-assistant/email_assistant_env/README.md) — Documentation
- `server/` — Server-side environment utilities

---

## 9. Deployment & Configuration

### 9.1 Docker — [Dockerfile](file:///c:/Users/gaura/OneDrive/Documents/New%20project/email-assistant/Dockerfile)

Multi-stage build:
1. **Builder stage**: Python 3.11-slim + `uv` for fast dependency install
2. **Runtime stage**: Copies installed packages, exposes port 8000
3. Health check: `curl -f http://localhost:8000/health`
4. Entry: `uvicorn app.main:app --host 0.0.0.0 --port 8000`

### 9.2 OpenEnv Config — [openenv.yaml](file:///c:/Users/gaura/OneDrive/Documents/New%20project/email-assistant/openenv.yaml)

Full OpenEnv-compliant configuration with:
- Hugging Face Spaces metadata (emoji: 📧, SDK: docker)
- Action space schema (`ClassifyEmailAction`, `DraftReplyAction`, `RequestMoreInfoAction`, `EscalateAction`)
- Observation space schema
- Dense reward definition with component weights
- Task definitions (3 difficulty levels)

### 9.3 Environment Variables

Configured via `.env`:
- `OPENAI_API_KEY` — For AI classification and reply generation
- IMAP/SMTP credentials — For live email integration
- SQLite path — For conversation memory
- Model selection — Default: `gpt-4o-mini`

---

## 10. Summary of Key Innovations

1. **Multi-Dimensional Dense Reward Rubric**: Beyond binary correct/incorrect — rewards classification precision (1.0), reasoning quality (0.2), tone compliance (0.3), workflow efficiency (−0.01/step), full resolution (2.0)
2. **Dual Mode**: Deterministic (keyword-based, no API key needed) + Live (OpenAI GPT-4o-mini)
3. **SLA-Aware Time Simulation**: Each action costs simulated time; deadline breaches incur penalties
4. **Thread Memory**: SQLite-backed conversation context for coherent multi-turn interactions
5. **Premium UI**: Glassmorphic "Elite Concierge" interface with luxury design tokens
6. **Full Pipeline**: classify → prioritize → draft → send, all within a Gymnasium-style reset/step loop