ExamplescriptintermediateRunnablememory-lab
Email Example
Runnable example (intermediate) for script using mem0, python-dotenv.
Key Facts
- Level
- intermediate
- Runtime
- Python • Mem0 + Python Dotenv
- Pattern
- Memory-aware assistance with legible context
- Interaction
- Live sandbox • Script
- Updated
- 14 March 2026
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Linked principles
email_example.py
python
from mem0 import MemoryClient
from email.parser import Parser
from dotenv import load_dotenv
load_dotenv("../.env")
# Initialize Mem0 client
client = MemoryClient()
class EmailProcessor:
def __init__(self):
"""Initialize the Email Processor with Mem0 memory client"""
self.client = client
def process_email(self, email_content, user_id):
"""
Process an email and store it in Mem0 memory
Args:
email_content (str): Raw email content
user_id (str): User identifier for memory association
"""
# Parse email
parser = Parser()
email = parser.parsestr(email_content)
# Extract email details
sender = email["from"]
recipient = email["to"]
subject = email["subject"]
date = email["date"]
body = self._get_email_body(email)
# Create message object for Mem0
message = {
"role": "user",
"content": f"Email from {sender}: {subject}\n\n{body}",
}
# Create metadata for better retrieval
metadata = {
"email_type": "incoming",
"sender": sender,
"recipient": recipient,
"subject": subject,
"date": date,
}
# Store in Mem0 with appropriate categories
response = self.client.add(
messages=[message],
user_id=user_id,
metadata=metadata,
categories=["email", "correspondence"],
version="v2",
)
return response
def _get_email_body(self, email):
"""Extract the body content from an email"""
# Simplified extraction - in real-world, handle multipart emails
if email.is_multipart():
for part in email.walk():
if part.get_content_type() == "text/plain":
return part.get_payload(decode=True).decode()
else:
return email.get_payload(decode=True).decode()
def search_emails(self, query, user_id):
"""
Search through stored emails
Args:
query (str): Search query
user_id (str): User identifier
"""
# Search Mem0 for relevant emails
results = self.client.search(
query=query,
user_id=user_id,
categories=["email"],
output_format="v1.1",
version="v2",
)
return results
def get_email_thread(self, subject, user_id):
"""
Retrieve all emails in a thread based on subject
Args:
subject (str): Email subject to match
user_id (str): User identifier
"""
filters = {
"AND": [
{"user_id": user_id},
{"categories": {"contains": "email"}},
{"metadata": {"subject": {"contains": subject}}},
]
}
thread = self.client.get_all(
version="v2", filters=filters, output_format="v1.1"
)
return thread
# Initialize the processor
processor = EmailProcessor()
# Example raw email
sample_email = """From: alice@example.com
To: bob@example.com
Subject: Meeting Schedule Update
Date: Mon, 15 Jul 2024 14:22:05 -0700
Hi Bob,
I wanted to update you on the schedule for our upcoming project meeting.
We'll be meeting this Thursday at 2pm instead of Friday.
Could you please prepare your section of the presentation?
Thanks,
Alice
"""
# Process and store the email
user_id = "bob@example.com"
processor.process_email(sample_email, user_id)
# Later, search for emails about meetings
meeting_emails = processor.search_emails("meeting schedule", user_id)
print(f"Found {len(meeting_emails['results'])} relevant emails")
Related principles
- P2visibilityEnsure that background work remains perceptibleWhen the system is operating asynchronously or outside the user’s immediate focus, it should provide persistent and proportionate signals that work is continuing.Open principle →
- P3visibilityAlign feedback with the user’s level of attentionThe system should calibrate the depth and frequency of feedback according to whether the user is actively engaged, passively monitoring, or temporarily absent.Open principle →
- P7trustEstablish trust through inspectabilityUsers should be able to examine how a result was produced when confidence, accountability, or decision quality is important.Open principle →