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+📧 AI-Driven Email Agent 🧠
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+A production-grade, multi-agent system built with LangGraph and FastAPI that automates email triage, context retrieval, and drafting. This project demonstrates advanced implementation of Long-term Memory, State Persistence, and Human-in-the-Loop Interrupts using LangGraph's Functional API Command pattern.
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+🚀 Key Features
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+Advanced Learning Implementation: Throughout this project, I successfully implemented Semantic Memory, Checkpointer Persistence, and Functional Interrupts, allowing the agent to maintain state and handle user feedback reliably.
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+Multi-Agent Workflow: Specialized agents for Triage, Context Synthesis, and Email Drafting.
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+Intelligent Triage: Automatically classifies emails, assigns priority, and determines if a reply is required.
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+Semantic Memory: Uses langmem and PostgresStore to retrieve past interactions, ensuring the agent "remembers" previous project details.
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+Resource Management: A dedicated Token Count Node ensures large emails (like deployment logs) are summarized before processing to optimize costs.
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+Human-in-the-Loop: The graph pauses using interrupt() to allow users to review, approve, or provide feedback on drafts via Command(resume=...).
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+Scalable Architecture: Built with FastAPI, Docker, and a modular folder structure for enterprise-level deployment.
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+🛠️ Tech Stack
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+Orchestration: langgraph (Functional API), langchain
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+LLM Interface: langchain-groq 
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+Memory & Persistence: langmem, PostgresCheckpoint, PostgresStore (via Neon/PostgreSQL)
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+Database ORM: SQLAlchemy 2.0
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+Embeddings: langchain_huggingface (DistilBERT)
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+Backend: FastAPI + Uvicorn
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+Configuration: pydantic-settings (Type-safe .env management)
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+Authentication: google-auth (Gmail API Integration)
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+📂 Project Structure
+
+app/
+├── agents/             # Brains: Specialized LLM logic (Triage, Writer, Context)
+├── database/           # Data: SQLAlchemy models and Connection Pooling
+├── nodes/              # Workflow: Functional steps of the graph (Safety, Tokens)
+├── persistance/        # Persistence: Postgres Checkpointer & Memory Store config
+├── state/              # Schema: Pydantic & TypedDict state definitions
+├── utils/              # Toolbox: Token counters, Embeddings, and Auth helpers
+├── graph.py            # Logic: StateGraph construction and compilation
+└── main.py             # Entry: FastAPI app and Controller logic
+
+
+🔄 Graph Architecture
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+The system follows a pre-processing pipeline before reaching the agentic loop:
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+Safety Check: Filters out malicious content.
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+Token Check: Routes large bodies to a Summarization Node.
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+Triage: Analyzes intent and priority.
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+Context Retrieval: Queries PostgresStore for relevant past facts.
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+Drafting Agent: Creates a reply based on the full context.
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+Interrupt: Pauses for User Review and feedback.
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+🛡️ Integrated Security
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+The safety classification in this agent is informed by my specialized project on threat detection:
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+Hybrid Phishing Detection Model: GitHub Repository
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+👨‍💻 Author
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+Atharva Gaykar AI Engineer | IIIT Nagpur
+