https://github.com/kaymen99/langgraph-email-automation
Multi AI agents for customer support email automation built with Langchain & Langgraph
https://github.com/kaymen99/langgraph-email-automation
ai-agents ai-automation ai-customer-service ai-customer-support customer-support customer-support-automation email-automation gmail-api langchain langgraph llama3 rag rag-agents rag-application
Last synced: about 2 months ago
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Multi AI agents for customer support email automation built with Langchain & Langgraph
- Host: GitHub
- URL: https://github.com/kaymen99/langgraph-email-automation
- Owner: kaymen99
- Created: 2024-06-10T15:16:03.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-02-13T15:31:40.000Z (3 months ago)
- Last Synced: 2025-03-24T08:09:06.900Z (about 2 months ago)
- Topics: ai-agents, ai-automation, ai-customer-service, ai-customer-support, customer-support, customer-support-automation, email-automation, gmail-api, langchain, langgraph, llama3, rag, rag-agents, rag-application
- Language: Python
- Homepage:
- Size: 321 KB
- Stars: 83
- Watchers: 1
- Forks: 25
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🚀 **Customer Support Email Automation with AI Agents and RAG**
## 📩 **FULL TUTORIAL: Build AI-Powered Email Automation Using AI Agents + RAG!** 👉 [Read Now](https://dev.to/kaymen99/boost-customer-support-ai-agents-langgraph-and-rag-for-email-automation-21hj) 🎯
## **Introduction**
In today's **fast-paced environment**, customers demand **quick, accurate, and personalized responses**—expectations that can overwhelm traditional support teams. Managing large volumes of emails, categorizing them, crafting appropriate replies, and ensuring quality consumes **significant time and resources**, often leading to **delays or errors**, which can harm customer satisfaction.
**Customer Support Email Automation** is an **AI solution** designed to enhance **customer communication** for businesses. Leveraging a **Langgraph-driven workflow**, multiple **AI agents** collaborate to efficiently manage, categorize, and respond to customer emails. The system also implements **RAG (Retrieval-Augmented Generation)** technology to deliver **accurate responses** to any business or product-related questions.
## **Features**
### **Email Inbox Management with AI Agents**
- **Continuously monitors** the agency's Gmail inbox
- **Categorizes emails** into '**customer complaint**,' '**product inquiry**,' '**customer feedback**,' or '**unrelated**'
- **Automatically handles irrelevant emails** to maintain efficiency
### **AI Response Generation**
- **Quickly drafts emails** for customer complaints and feedback using **Langgraph**
- Utilizes **RAG techniques** to answer **product/service-related questions** accurately
- **Creates personalized email content** tailored to each customer's needs
### **Quality Assurance with AI**
- **Automatically checks** email **quality, formatting, and relevance**
- **Ensures every response** meets high standards before reaching the client
## **How It Works**
1. **Email Monitoring**: The system **constantly checks** for new emails in the agency's Gmail inbox using the **Gmail API**.
2. **Email Categorization**: **AI agents** sort each email into **predefined categories**.
3. **Response Generation**:
- **For complaints or feedback**: The system **quickly drafts** a tailored email response.
- **For service/product questions**: The system uses **RAG** to retrieve **accurate information** from agency documents and generates a response.
4. **Quality Assurance**: Each draft email undergoes **AI quality and formatting checks**.
5. **Sending**: **Approved emails** are sent to the client **promptly**, ensuring **timely communication**.
## System Flowchart
This is the detailed flow of the system:
[](https://mermaid.live/edit#pako:eNqllEuP2jAQx7-KZa6AgAB5HFrxFlJBXbarIsIeTDwBi2CntrPAEr57TRIoW_Wwojk585_fvJLxCQeCAvZwGIl9sCFSox_9JUfm6fgTwZkWEo0mnfE3NOYrcXgtNFSpfEHd01jlZjTYxfr49Zyr3YuaTgWawt4ohEUqRQt_wCn6LkUASr3eOw5FYpQXTrjagwR6Q3p-j2hYC8neITcWXC_jXriEyDjQFHXv7b1EabEDiXpiF0eEcY1ME0MAuiLBNkV9_6dk2uidNXD9IaQpjyaBRgP-K2HymKKB_3zkegPqUsJTApKBQqEJN-uMCnKQzWLuj0CjTtYCCqXY3XnMM49_pu1n0tA3iUU4A0L_0odZWZ04luINUjTyn4HTD7PIPaamO4Vm8MYUE9z0mIujQjzonMkmlUtKHyMwHzJkUeSVQjcsKy3FFryS4zjFubJnVG-8RnwoByIS0ivVarV7vFvgq9Uf3LKsz-K9a_bV6hG8f80ePoQP_i_78DY69xF8VOBu-BA-v2Z_DF8UOKX08zguY7NW5j-i5sI4XcItsdmNHSyxZ46UyO0SL_nZ-JFEC7M5Afa0TKCMpUjWG-yFJFLmLYmp2ds-I2tJdjdrTDj2TviAvUbLrjYtt2G1XLdVr7XtZhkfjbnqNJyW4zZt17LdpuO0z2X8LoQJUau6rbbt2la7btmWW6s3s3iLTMxLAHq5zCb5dRcIHrI1Pv8GXQeX4g)
## Tech Stack
* Langchain & Langgraph: for developing AI agents workflow.
* Langserve: simplify API development & deployment (using FastAPI).
* Groq and Gemini APIs: for LLMs access.
* Google Gmail API
## How to Run
### Prerequisites
- Python 3.7+
- Groq api key
- Google Gemini api key (for embeddings)
- Gmail API credentials
- Necessary Python libraries (listed in `requirements.txt`)
### Setup
1. **Clone the repository:**
```sh
git clone https://github.com/kaymen99/langgraph-email-automation.git
cd langgraph-email-automation
```
2. **Create and activate a virtual environment:**
```sh
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
3. **Install the required packages:**
```sh
pip install -r requirements.txt
```
4. **Set up environment variables:**
Create a `.env` file in the root directory of the project and add your GMAIL address, we are using the Groq llama-3.1-70b model and the Google gemini embedding model so you must also get API keys to access them:
```env
[email protected]
GROQ_API_KEY=your_groq_api_key
GOOGLE_API_KEY=your_gemini_api_key
```
5. **Ensure Gmail API is enabled:**
Follow [this guide](https://developers.google.com/gmail/api/quickstart/python) to enable Gmail API and obtain your credentials.
### Running the Application
1. **Start the workflow:**
```sh
python main.py
```
The application will start checking for new emails, categorizing them, synthesizing queries, drafting responses, and verifying email quality.
2. **Deploy as API:** you can deploy the workflow as an API using Langserve and FastAPI by running the command below:
```sh
python deploy_api.py
```
The workflow api will be running on `localhost:8000`, you can consult the API docs on `/docs` and you can use the langsergve playground (on the route `/playground`) to test it out.
### Customization
You can customize the behavior of each agent by modifying the corresponding methods in the `Nodes` class or the agents prompt `prompts` located in the `src` directory.
You can also add your own agency data into the `data` folder, then you must create your own vector store by running (update first the data path):
```sh
python create_index.py
```
### Contributing
Contributions are welcome! Please open an issue or submit a pull request for any changes.
### Contact
If you have any questions or suggestions, feel free to contact me at `[email protected]`.