AI-Powered Email and Calendar Assistant

Created on January 30, 2026

2026   ·   AI   LLM   LangChain   Streamlit   SQLite   HuggingFace

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Situation

Managing a busy inbox involves repetitive tasks — classifying emails, drafting replies, and scheduling meetings — that consume significant time but follow predictable patterns. Modern LLMs have made it possible to automate these workflows intelligently, but most solutions operate as black boxes without meaningful human oversight.

Task

I built a full-stack AI assistant that automates email classification, reply drafting, and meeting scheduling while keeping the user firmly in control. The project served as a deep dive into LLM application development, integrating LangChain orchestration, Google APIs, and a human-in-the-loop approval system into a cohesive product.

Action

Built a Streamlit web app with a modular architecture:

  • AI Agents (LangChain + HuggingFace Llama 3.1): An email classifier that categorises messages (needs reply, FYI, meeting request, action item) with confidence scoring; a reply drafter with tone control and iterative improvement; and a meeting scheduler that extracts details and proposes free calendar slots.
  • Human-in-the-Loop Approval: Every action (sending emails, creating calendar events) requires explicit user approval. A risk assessment layer automatically flags unknown senders, sensitive content, and low-confidence classifications.
  • Google API Integration: Full OAuth 2.0 flow with Gmail (fetch, send, label) and Google Calendar (availability checking, event creation, free slot detection with working-hours awareness).
  • Persistence & Learning: SQLAlchemy/SQLite database tracking classifications, drafts, approvals, and user feedback to build an audit trail and support future model improvement.
  • Graceful Degradation: Every AI agent includes fallback strategies (heuristic keyword matching, template-based drafts) so the app remains functional even when the LLM is unavailable.

Result

A fully functional, production-structured application that demonstrates end-to-end LLM integration — from prompt engineering and chain composition to API orchestration and state management. The security-first design (layered approvals, sensitive content detection, trusted sender lists) reflects real-world considerations for AI-assisted communication. The project solidified practical skills in LangChain, Google Cloud APIs, OAuth 2.0, SQLAlchemy, and Streamlit, with clean separation of concerns across agents, services, database, and UI layers.

Tech stack: Python · LangChain · HuggingFace · Streamlit · SQLAlchemy · Gmail API · Google Calendar API · OAuth 2.0