ReasonFlow — Autonomous Inbox AI Agent

Intelligent email processing powered by LangGraph + Gemini, with human-in-the-loop approval workflows.

Overview

ReasonFlow is a full-stack autonomous inbox agent that:

• Classifies incoming emails by intent (inquiry, meeting request, complaint, etc.)
• Retrieves relevant context from past emails, CRM data, and calendar
• Generates draft responses using Google Gemini via LangChain
• Routes low-confidence drafts to humans for approval
• Dispatches approved responses automatically

Tech Stack

Layer	Technology
Backend	FastAPI, LangGraph, LangChain + Gemini, SQLAlchemy
Frontend	Next.js 14 (App Router), shadcn/ui, Zustand, TanStack Query
Database	PostgreSQL + pgvector
LLM	Google Gemini API (via langchain-google-genai)

Architecture

┌─────────────┐ ┌──────────────────────────────────────────┐
│ Next.js │────▶│ FastAPI Backend │
│ Dashboard │◀────│ │
└─────────────┘ │ ┌─────────┐ ┌──────────┐ ┌────────┐ │
│ │ Agent │ │Retrieval │ │ APIs │ │
│ │ Engine │ │ Module │ │ │ │
│ │(LangGraph│ │(pgvector)│ │Gmail │ │
│ │+ Gemini) │ │ │ │Calendar│ │
│ └─────────┘ └──────────┘ │CRM │ │
│ └────────┘ │
└──────────────────────────────────────────┘
│
┌───────────────┼───────────────┐
│ │ │
PostgreSQL External
+ pgvector APIs

Agent Workflow

Email Input → Classification → Context Retrieval → Decision
→ Tool Execution → Response Generation → Human Review → Dispatch

Each step is a LangGraph node with full observability and state tracking.

Quick Start

Backend

cd backend
pip install -e ".[dev]"
cp .env.example .env
# Edit .env with your credentials
alembic upgrade head
uvicorn app.main:app --reload

Frontend

cd frontend
npm install
cp .env.example .env.local
npm run dev

Docker (Full Stack)

docker compose -f docker/docker-compose.yml up --build

Project Structure

backend/
├── app/
│ ├── agent/ # LangGraph workflow engine
│ │ ├── nodes/ # Classification, retrieval, decision, generation, review
│ │ ├── state/ # Agent state definitions
│ │ └── tools/ # Tool manager + tool implementations
│ ├── api/ # FastAPI routes + middleware
│ ├── core/ # Config, database, security
│ ├── integrations/ # Gmail, Calendar, CRM clients
│ ├── llm/ # Gemini client + prompts (via LangChain)
│ ├── models/ # SQLAlchemy models
│ ├── retrieval/ # Embeddings + vector search
│ ├── schemas/ # Pydantic request/response schemas
│ └── services/ # Business logic layer
├── tests/
└── alembic/
frontend/
├── src/
│ ├── app/
│ │ ├── (auth)/ # Login, OAuth callback
│ │ └── (dashboard)/ # Inbox, Drafts, Metrics, Traces
│ ├── components/ # UI components per feature
│ ├── hooks/ # React Query hooks
│ ├── stores/ # Zustand state stores
│ ├── lib/ # API client, utilities
│ └── types/ # TypeScript interfaces

Documentation

• System Architecture
• Database Schema
• Agent Workflow
• API Reference
• Gmail Integration
• Agent Engine
• Calendar Integration
• CRM Integration
• Retrieval Module
• Inbox Dashboard
• Draft Review
• Metrics Dashboard
• Trace Viewer

Performance Targets

Metric	Target
API latency	< 300ms
Agent full cycle	< 4s
Dashboard load	< 1s

License

MIT