一款智能邮件助手，基于 Microsoft 的 Phi-4 本地模型，具备现代化的网页界面、人工审核、日历集成，以及通过 MCP（模型上下文协议）连接 Microsoft 365。

概述

本项目演示如何利用以下方法构建本地电子邮件代理：

• Foundry Local（完全运行在你的机器上），这个项目展示了phi-4，但你可以使用任何你喜欢的模型
• PostgreSQL + pgvector 用于语义邮件搜索
• Microsoft 365 MCP 服务器用于电子邮件/日历操作
• LangGraph 用于带有人工循环（HITL）批准的代理编排
• **FastAPI 后端支持SSE流式实时更新
• Next.js 前端 - 用于管理对话和审批的代理收件箱界面

🌱 快速启动

先决条件

1. Python 3.11+
2. Docker （for PostgreSQL）
3. Node.js 18+（用于MCP服务器和前端）

布置

1.安装Foundry Local：

Windows：

MacOS：

注：Foundry Local 目前不支持 Linux

2.安装依赖：

3.Start PostgreSQL + pgvector：

如果成功，你应该会看到列出的vector扩展：

4.配置环境：

编辑’。

Local Email Agent

An intelligent email assistant powered by Microsoft’s Phi-4 local model, featuring a modern web UI, human-in-the-loop approvals, calendar integration, and Microsoft 365 connectivity via MCP (Model Context Protocol).

Overview

This project demonstrates how to build a local email agent using:

• Foundry Local (runs entirely on your machine), this project shows phi-4 but you can use any model you like
• PostgreSQL + pgvector for semantic email search
• Microsoft 365 MCP Server for email/calendar operations
• LangGraph for agent orchestration with Human-in-the-Loop (HITL) approvals
• FastAPI Backend with SSE streaming for real-time updates
• Next.js Frontend - Agent Inbox UI for managing conversations and approvals

🌱 Quick Start

Prerequisites

1. Python 3.11+
2. Docker (for PostgreSQL)
3. Node.js 18+ (for MCP server and frontend)

Setup

1. Install Foundry Local:

Windows:

winget install Microsoft.FoundryLocal

MacOS:

brew install microsoft/foundrylocal/foundrylocal

Note: Foundry Local doesn’t currently support Linux

2. Install Dependencies:

# Create virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install Python packages
pip install -r email_agent/requirements.txt

# Install Microsoft 365 MCP Server
npm install -g @softeria/ms-365-mcp-server

# Login to the M365 account you want to use
npx @softeria/ms-365-mcp-server --login

# Install frontend dependencies
cd email_agent/frontend
npm install
cd ../..

3. Start PostgreSQL + pgvector:

# Start Docker container
docker compose up -d

# Wait ~10 seconds for PostgreSQL to initialize

# Enable pgvector extension
docker exec -it email-postgres psql -U postgres -d emaildb -c "CREATE EXTENSION IF NOT EXISTS vector;"

# Verify
docker exec -it email-postgres psql -U postgres -d emaildb -c "\dx"

If successful, you should see the vector extension listed:

  Name   | Version |   Schema   |                        Description
---------+---------+------------+-----------------------------------------------------------
 plpgsql | 1.0     | pg_catalog | PL/pgSQL procedural language
 vector  | 0.5.0   | public     | vector data type and ivfflat and hnsw access methods

4. Configure Environment:

# Copy local env template
cp email_agent/.env.local.example email_agent/.env

Edit the .env file and add your Azure OpenAI credentials:

AZURE_OPENAI_ENDPOINT=https://your-endpoint.openai.azure.com/
AZURE_OPENAI_API_KEY=your-api-key
AZURE_OPENAI_EMBEDDING_DEPLOYMENT=text-embedding-ada-002

5. Import Your Emails (Optional)

# Import last 3 months of emails from Outlook
python3 -m email_agent.import_emails --months 3 --batch-size 50 --storage local

6. Run the Agent:

Start both the backend and frontend: