One more vacation season has arrived. It’s certainly essentially the most fantastic time of the 12 months. It’s additionally that point of the 12 months when working professionals set the identical previous out-of-office reply to each e mail they get. Effectively, the issue with automating e mail responses this fashion is that it offers the identical flavourless replies to all of the emails – each related and irrelevant ones. That is when even adults begin wishing Santa would reward them an e mail workflow optimisation answer or an AI e mail assistant that offers good replies. Effectively, this 12 months, Santa has come dressed as CrewAI! On this weblog, we’ll find out about automating e mail responses by constructing an agentic AI system with CrewAI to answer to your out-of-office emails well and guarantee good e mail administration.

Understanding the Context

First, let’s attempt to perceive the context of our downside assertion.

This screenshot captures the essence of the issue. Should you look carefully, you will see that emails the place my direct intervention is required, after which you will see that emails with subscribed newsletters and calendar notifications that don’t require any reply.

The present ‘Trip Responder’ responds to all of the messages with no functionality to alter the title of the recipient or the contents of the mail primarily based on who it’s responding to. Additionally, it responds to irrelevant emails, which embody newsletters, verification code emails, OTP emails, and many others.

That is the place the CrewAI framework involves the rescue for e mail response administration. With CrewAI, you’ll be able to shortly construct an e mail responder agent system, with some easy coding. Relieved? So, let’s construct an agentic AI system for automating e mail responses with CrewAI and produce a layer of optimisation to your e mail workflow.

Additionally Learn: Automating Electronic mail Sorting and Labelling with CrewAI

Google Authentication

Earlier than we bounce to the code for automating e mail responses in Gmail, you have to allow the Gmail API and generate the OAuth 2.0 credentials. This may give your e mail responder agentic system entry to your emails. Right here’s how one can get this completed.

Step 1: Create a New Undertaking in Google Cloud

Go to the Google Cloud console and log in along with your e mail deal with. First-time customers might want to create an account.

Then choose “New Undertaking” within the dropdown, give it a reputation, and click on Create. This challenge can have the required API-related configurations. Whereas including the brand new challenge, select your organisation title as the placement, as we’ve chosen analyticsvidhya.com.

Step 2: Allow Gmail API

Click on the Navigation Menu from the console’s Dashboard and head to Discover and Allow APIs beneath the Getting Began part.

On the left-hand aspect of the display screen, choose Library, and seek for “Gmail API”. Allow it for the challenge you created.

Step 3: Set Up OAuth 2.0 Credentials

Subsequent, arrange the OAuth consent display screen beneath APIs & Companies. Then click on Configure Consent Display screen.

Select the kind (e.g., Exterior for apps utilized by anybody). We are going to selected Inner since we’re utilizing it for our personal e mail ID. Then click on Create.

Then, title your app and add the Consumer help e mail and Developer contact info. Right here’s the place you must add your work e mail ID. As soon as completed, click on on SAVE AND CONTINUE on the backside of the display screen.

Now, we have to outline the scopes within the consent display screen setup. Scopes, within the context of Google Console, dictate what the API can entry. For email-related duties, you’ll want the next: ‘https://www.googleapis.com/auth/gmail.modify‘. This scope will enable the e-mail responder system to ship and modify emails in your Gmail account. Click on on ADD OR REMOVE SCOPES after which choose the scope talked about above.

Then click on on Replace. You’ll be able to see that the scope has been added. Press SAVE AND CONTINUE.

Now undergo the abstract, after which click on BACK TO DASHBOARD.

Step 4: Create Credentials

Now select Credentials beneath APIs & Companies and click on CREATE CREDENTIALS.

Then choose OAuth shopper ID.

For native improvement, we are going to select the Desktop App choice, after which press CREATE.

Step 5: Obtain the Credential.json

Now obtain the JSON file and put it aside domestically at your most well-liked location.

And that concludes our use of Google Search Console.

To allow CrewAI brokers to carry out internet searches and knowledge retrieval from the web, it is going to want the API to the SerperDev Device. The SerperDev Device is a Python utility that interfaces with the Serper API, a cheap and speedy Google Search API. It permits builders to programmatically retrieve and course of Google Search outcomes, together with reply containers, information graphs, and natural listings.

Let’s undergo the steps to get the API.

• Go to serper.dev and click on on Join.
• Create your account and login. You will note the Dashboard when you login.

• On the left of the display screen, click on on API Key.

Now, let’s bounce to the Python code and construct our AI e mail assistant for automated e mail responses.

Python Code for Automating Electronic mail Responses

Step 1: Import Vital Libraries

We are going to start by importing the related libraries to construct an agentic system for automating e mail responses.

# Importing needed libraries
import os  # Supplies features to work together with the working system
# Importing modules from the CrewAI framework
# CrewAI is a framework for managing brokers, duties, processes, and instruments.
from crewai import Agent, Job, Crew, Course of  # Handle brokers, duties, and processes
from crewai_tools import SerperDevTool  # Device from CrewAI for connecting to Google search
# Importing modules for Google OAuth2 authentication and API interplay
from google.auth.transport.requests import Request  # To deal with token refresh requests
from google.oauth2.credentials import Credentials  # To handle OAuth2 credentials
from google_auth_oauthlib.stream import InstalledAppFlow  # To deal with OAuth2 login flows
from googleapiclient.discovery import construct  # To create service objects for Google APIs
# Importing modules for e mail creation
import base64  # To encode e mail messages in base64
from e mail.mime.textual content import MIMEText  # To create MIME-compliant e mail messages

Step 2: Set Scopes

Now, let’s set the SCOPES variable that defines permissions for the Gmail API. ‘gmail_modify’ permits studying, sending, and modifying emails, excluding everlasting deletion, making certain restricted entry.

# Gmail API setup
SCOPES = ['https://www.googleapis.com/auth/gmail.modify']

Subsequent, we create the get_gmail_service perform that authenticates and connects to the Gmail API. It checks for saved credentials in token.json, refreshing them if expired. If unavailable, it initiates a brand new login stream utilizing credentials.json. It saves legitimate credentials for reuse, and returns a Gmail API service object for e mail operations.

# Operate to authenticate and join gmail API

def get_gmail_service():

    creds = None

    if os.path.exists('token.json'):

        creds = Credentials.from_authorized_user_file('token.json', SCOPES)

    if not creds or not creds.legitimate:

        if creds and creds.expired and creds.refresh_token:

            creds.refresh(Request())

        else:

            stream = InstalledAppFlow.from_client_secrets_file('credentials.json', SCOPES)

            creds = stream.run_local_server(port=0)

        with open('token.json', 'w') as token:

            token.write(creds.to_json())

    return construct('gmail', 'v1', credentials=creds)

Step 3: Set Up Electronic mail Retrieval

Then, we create the get_unread_emails perform to retrieve unread emails from the Gmail inbox. It makes use of the Gmail API service object to checklist messages with the labels ‘INBOX’ and ‘UNREAD’. The outcomes are executed as a question, and the perform returns an inventory of messages or an empty checklist if none exist.

# Operate to retrieve unread emails

def get_unread_emails(service):

    outcomes = service.customers().messages().checklist(userId='me', labelIds=['INBOX', 'UNREAD']).execute()

    return outcomes.get('messages', [])

Subsequent, we create the get_email_content perform to retrieve and parse e mail particulars from Gmail utilizing the message ID. It fetches the complete e mail, extracts the topic and sender from headers, and decodes the physique. It helps multi-part emails (extracting plain textual content) and single-part emails by decoding the Base64-encoded content material. The perform returns a dictionary containing the e-mail’s topic, sender, and physique, making certain complete dealing with of various e mail codecs.

def get_email_content(service, msg_id):

    message = service.customers().messages().get(userId='me', id=msg_id, format="full").execute()

    payload = message['payload']

    headers = payload['headers']

    topic = subsequent(header['value'] for header in headers if header['name'] == 'Topic')

    sender = subsequent(header['value'] for header in headers if header['name'] == 'From')

    physique = ''

    if 'components' in payload:

        for half in payload['parts']:

            if half['mimeType'] == 'textual content/plain':

                physique = base64.urlsafe_b64decode(half['body']['data']).decode('utf-8')

                break

    else:

        physique = base64.urlsafe_b64decode(payload['body']['data']).decode('utf-8')

    return {'topic': topic, 'sender': sender, 'physique': physique}

Step 4: Set Up Electronic mail Filters

Now, we come to an important piece of code. The half that helps us filter out irrelevant emails primarily based on sure key phrases within the physique of the mail or the sender. For me, I are not looking for my agentic system to reply to emails that are- “subscribed newsletters”, “advertising emails”, “automated studies”, “calendar notifications”, “verification code emails”, “OTP emails”, “HRMS”, “emails containing the phrases like ‘don’t reply’, ‘no-reply’, ‘accepted your invitation’, ‘rejected your invitation’, and many others.”

So, we are going to create the filter_emails perform that identifies emails to disregard primarily based on predefined standards. It makes use of two lists: ignore_keywords for phrases like “e-newsletter,” “OTP,” or “advertising” that point out irrelevant content material, and ignore_senders for sender patterns like “noreply” or “calendar-notification.” The perform checks if the sender matches any ignored patterns or if the topic or physique comprises any ignored key phrases. It converts textual content(sender’s e mail id + physique of the e-mail) to lowercase for constant, case-insensitive comparisons. If an e mail matches any standards, the perform returns True to filter it out; in any other case, it returns False.

# Operate to filter out emails

def filter_emails(email_content, sender, topic):

    ignore_keywords = [

        "newsletter", "marketing", "automated report", "calendar notifications", "verification code", "otp", "Join with Google Meet",

        "HRMS", "do not reply", "no-reply", "accepted your invitation", "rejected your invitation", "Accepted:"

    ]

    ignore_senders = [

        "[email protected]", "calendar-notification", "noreply", "no-reply", "calendar-server.bounces.google.com"

    ]

    # Examine sender

    if any(ignore in sender.decrease() for ignore in ignore_senders):

        return True

    # Examine topic and physique for key phrases

    if any(key phrase in topic.decrease() or key phrase in email_content.decrease() for key phrase in ignore_keywords):

        return True

    return False

Step 5: Create the Ship Reply Operate

Subsequent, we create the send_reply perform that replies to an e mail utilizing the Gmail API. To keep up the context of the dialog, we are going to create a message with the required recipient (to), physique, and thread ID. The message is Base64-encoded and despatched through the API. This perform ensures replies are linked to the unique thread for seamless communication.

def send_reply(service, to, topic, physique, thread_id):

    message = MIMEText(physique)

    message['to'] = to

    raw_message = base64.urlsafe_b64encode(message.as_bytes()).decode('utf-8')

    return service.customers().messages().ship(

        userId='me',

        physique={'uncooked': raw_message, 'threadId': thread_id}

    ).execute()

Step 6: Construct the AI Brokers

Now, we are going to construct the three brokers required to execute the duty: the email_analyzer, response_drafter, and proofreader brokers. I’ve added the related prompts to every agent as per my choice. I like to recommend you undergo the backstory and objective accordingly.

# Outline brokers

email_analyzer = Agent(

    position="Electronic mail Analyzer",

    objective="Analyze incoming emails and decide applicable responses to related emails",

    backstory="You are an knowledgeable at understanding e mail content material and context. "

              "With this understanding, you establish whether or not to answer to a selected e mail or not. "

              "It's good to ignore emails which might be newsletters, advertising emails, Google doc feedback, google doc notifications,"

              "calendar notifications, HRMS mails, automated studies, and many others.",    

    verbose=True,

    instruments=[SerperDevTool()],

    allow_delegation=False

)

response_drafter = Agent(

    position="Response Drafter",

    objective="Draft applicable responses to emails",

    backstory="You are expert at crafting skilled and contextually applicable e mail responses."

              "Don't Generate responses to emails which might be unread newsletters, advertising emails,"

              "googl doc feedback(from: [email protected]), calendar notifications, HRMS mails, automated studies, and many others."

              "Make the responses crisp. Guarantee it's recognized to those that the I'm celebrating the Holidays and can be capable of ship any required paperwork as soon as I'm be a part of again on third January 2025."

              "YOUR NAME is my title and that is the title for use within the sign-off for every mail you generate response for."

              "Within the salutation use the recipient's title after 'Hello'",

    verbose=True

)

proofreader = Agent(

    position="Proofreader",

    objective="Guarantee e mail responses are error-free and polished",

    backstory="You could have a eager eye for element and wonderful grammar abilities. Make sure the response is crisp and to the purpose"

              "Additionally discard e mail replies generated for emails which might be newsletters, advertising emails, googl doc feedback, calendar notifications, HRMS mails, automated studies, and many others.",

    verbose=True

)

Now we are going to outline three totally different duties for the three brokers we’ve created. The outline will replicate the instructions written within the backstory of their respective brokers.

# Outline job for email_analyzer agent

def analyze_email(email_content):

    return Job(

        description=f"Analyze the content material and context of the next e mail:nn{email_content}n"

                    f"Decide if this e mail requires a response. Ignore newsletters, advertising emails, "

                    f"Google doc feedback (from: [email protected]), Google doc notifications, calendar invitations, "

                    f"HRMS mails, automated studies, and many others.",

        expected_output="An in depth evaluation of the e-mail content material and context, together with whether or not it requires a response",

        agent=email_analyzer

    )

# Outline job for response_drafter agent

def draft_response(evaluation):

    return Job(

        description=(

            f"Draft an expert and contextually applicable response to the next e mail evaluation:nn{evaluation}nn"

            f"Make sure the response is crisp and fascinating. Keep away from producing e mail responses for newsletters, advertising emails, "

            f"Google doc feedback (from: [email protected]), Google doc notifications, "

            f"calendar invitations, HRMS mails, and automatic studies. "

            f"Moreover, embody a observe indicating that the sender is celebrating the vacations and can be capable of present "

            f"any required paperwork or help after returning."

            f"YOUR NAME is the title of the sender and this needs to be utilized in sign-off for every mail you generate response for."

            f"Within the salutation use the recipient's title after 'Hello'"

        ),

        expected_output="A well-crafted e mail response primarily based on the evaluation",

        agent=response_drafter

    )

# Outline job for proofreader agent

def proofread_response(draft):

    return Job(

        description=f"Evaluation and refine the next drafted e mail response:nn{draft}",

        expected_output="A cultured, error-free e mail response",

        agent=proofreader

    )

Step 7: Add the Course of Electronic mail Operate

Subsequent, we create the process_email perform to course of an e mail by first making use of filter_emails to disregard irrelevant messages. If the e-mail passes the filter, a crew occasion manages the sequential execution of duties: analyzing the e-mail, drafting a response, and proofreading it. The result’s evaluated to examine if a response is required. If not, it returns None; in any other case, it returns the processed output. This perform automates e mail dealing with effectively with clear decision-making at every step.

# Process_email to incorporate filtering logic

def process_email(email_content, sender, topic):

    if filter_emails(email_content, sender, topic):

        print(f"Filtered out e mail from: {sender}")

        return None

    crew = Crew(

        brokers=[email_analyzer, response_drafter, proofreader],

        duties=[

            analyze_email(email_content),

            draft_response(""),

            proofread_response("")

        ],

        course of=Course of.sequential,

        verbose=True

    )

    outcome = crew.kickoff()

    # Examine if the result's a CrewOutput object

    if hasattr(outcome, 'outcome'):

        final_output = outcome.outcome

    else:

        final_output = str(outcome)

    # Now examine if a response is required

    if "requires response: false" in final_output.decrease():

        return None

    return final_output

Step 8: Create the Ship Reply Operate

And now we come to our remaining perform for this code. We are going to create the run_email_replier perform which automates e mail administration for CrewAI brokers. It fetches unread emails, analyzes their content material, and responds if wanted. It does this by retrieving detailed e mail info (physique, sender, topic), processing it with process_email to filter irrelevant messages, and figuring out if a response is required. If that’s the case, it sends a reply whereas sustaining the e-mail thread; in any other case, it skips the e-mail. This streamlined course of effectively handles e mail triage, making certain solely related emails obtain consideration and automating responses the place needed.

# Run_email_replier to cross sender and topic to process_email

def run_email_replier():

    service = get_gmail_service()

    unread_emails = get_unread_emails(service)

    for e mail in unread_emails:

        email_content = get_email_content(service, e mail['id'])

        response = process_email(email_content['body'], email_content['sender'], email_content['subject'])

        if response:

            send_reply(service, email_content['sender'], email_content['subject'], response, e mail['threadId'])

            print(f"Replied to e mail: {email_content['subject']}")

        else:

            print(f"Skipped e mail: {email_content['subject']}")

Step 9: Set the Surroundings Variables for API Keys

Lastly, we set the setting variables for API keys (OPENAI_API_KEY, SERPER_API_KEY that you simply saved) required for e mail processing. It executes the run_email_replier perform to automate e mail administration utilizing CrewAI brokers, together with analyzing, filtering, and replying to unread emails. The if __name__ == “__main__”: block ensures the method runs solely when executed instantly.

# units setting variables 
if __name__ == "__main__":

    # Arrange setting variables

    os.environ['OPENAI_API_KEY'] = 'Your API Key'

    os.environ['SERPER_API_KEY'] = 'Your API Key'

    # Run the e-mail replier

    run_email_replier()

And that’s our e mail response administration agent in full motion!

Let’s take a look on the emails. As you’ll be able to see, for emails the place my direct intervention was required, it has generated a personalized e mail as per the context.

And for emails akin to newsletters and notifications from HRMs, the place replies aren’t required, it has not given any reply.

And there you’ve gotten it! A totally practical autonomous agentic system for automating e mail responses for out-of-office replies This manner you should utilize AI brokers for e mail workflow optimization. If you’re happy with the responses you’ll be able to arrange a job scheduler to routinely run this code at particular occasions in the course of the day. When the code is run it is going to routinely reply to the related unread emails.

Additionally Learn: Construct LLM Brokers on the Fly With out Code With CrewAI

Conclusion

Automating e mail responses with Brokers(Crew AI, Langchain, AutoGen) can rework how we handle replies to out-of-office emails. Furthermore, organising such a system provides a glimpse right into a extra hopeful future for office effectivity. As AI continues to evolve, instruments like CrewAI will empower us to take care of seamless communication with out compromise, paving the way in which for a future the place know-how enhances each productiveness and private well-being. The probabilities are vivid, and the long run is promising!

Incessantly Requested Questions

My title is Abhiraj. I’m at present a supervisor for the Instruction Design workforce at Analytics Vidhya. My pursuits embody badminton, voracious studying, and assembly new individuals. Each day I like studying new issues and spreading my information.