Custom Instructions

Control how Mem0 extracts and stores memories using natural language guidelines

What are Custom Instructions?

Custom instructions are natural language guidelines that let you define exactly what Mem0 should include or exclude when creating memories from conversations. This gives you precise control over what information is extracted, acting as smart filters so your AI application only remembers what matters for your use case.

Python

# Simple example: Health app focusing on wellness
prompt = """
Extract only health and wellness information:
- Symptoms, medications, and treatments
- Exercise routines and dietary habits
- Doctor appointments and health goals

Exclude: Personal identifiers, financial data
"""

client.project.update(custom_instructions=prompt)

// Simple example: Health app focusing on wellness
const prompt = `
Extract only health and wellness information:
- Symptoms, medications, and treatments
- Exercise routines and dietary habits
- Doctor appointments and health goals

Exclude: Personal identifiers, financial data
`;

await client.project.update({ customInstructions: prompt });

Why Use Custom Instructions?

• Focus on What Matters: Only capture information relevant to your application
• Maintain Privacy: Explicitly exclude sensitive data like passwords or personal identifiers
• Ensure Consistency: All memories follow the same extraction rules across your project
• Improve Quality: Filter out noise and irrelevant conversations

How to Set Custom Instructions

Basic Setup

Python

# Set instructions for your project
client.project.update(custom_instructions="Your guidelines here...")

# Retrieve current instructions
response = client.project.get(fields=["custom_instructions"])
print(response["custom_instructions"])

// Set instructions for your project
await client.project.update({ customInstructions: "Your guidelines here..." });

// Retrieve current instructions
const response = await client.project.get({ fields: ["customInstructions"] });
console.log(response.customInstructions);

Best Practice Template

Structure your instructions using this proven template:

Your Task: [Brief description of what to extract]

Information to Extract:
1. [Category 1]:
   - [Specific details]
   - [What to look for]

2. [Category 2]:
   - [Specific details]
   - [What to look for]

Guidelines:
- [Processing rules]
- [Quality requirements]

Exclude:
- [Sensitive data to avoid]
- [Irrelevant information]

Real-World Examples

E-commerce Customer Support

Python

instructions = """
Extract customer service information for better support:

1. Product Issues:
   - Product names, SKUs, defects
   - Return/exchange requests
   - Quality complaints

2. Customer Preferences:
   - Preferred brands, sizes, colors
   - Shopping frequency and habits
   - Price sensitivity

3. Service Experience:
   - Satisfaction with support
   - Resolution time expectations
   - Communication preferences

Exclude: Payment card numbers, passwords, personal identifiers.
"""

client.project.update(custom_instructions=instructions)

const instructions = `
Extract customer service information for better support:

1. Product Issues:
   - Product names, SKUs, defects
   - Return/exchange requests
   - Quality complaints

2. Customer Preferences:
   - Preferred brands, sizes, colors
   - Shopping frequency and habits
   - Price sensitivity

3. Service Experience:
   - Satisfaction with support
   - Resolution time expectations
   - Communication preferences

Exclude: Payment card numbers, passwords, personal identifiers.
`;

await client.project.update({ customInstructions: instructions });

Personalized Learning Platform

Python

education_prompt = """
Extract learning-related information for personalized education:

1. Learning Progress:
   - Course completions and current modules
   - Skills acquired and improvement areas
   - Learning goals and objectives

2. Student Preferences:
   - Learning styles (visual, audio, hands-on)
   - Time availability and scheduling
   - Subject interests and career goals

3. Performance Data:
   - Assignment feedback and patterns
   - Areas of struggle or strength
   - Study habits and engagement

Exclude: Specific grades, personal identifiers, financial information.
"""

client.project.update(custom_instructions=education_prompt)

const educationPrompt = `
Extract learning-related information for personalized education:

1. Learning Progress:
   - Course completions and current modules
   - Skills acquired and improvement areas
   - Learning goals and objectives

2. Student Preferences:
   - Learning styles (visual, audio, hands-on)
   - Time availability and scheduling
   - Subject interests and career goals

3. Performance Data:
   - Assignment feedback and patterns
   - Areas of struggle or strength
   - Study habits and engagement

Exclude: Specific grades, personal identifiers, financial information.
`;

await client.project.update({ customInstructions: educationPrompt });

AI Financial Advisor

Python

finance_prompt = """
Extract financial planning information for advisory services:

1. Financial Goals:
   - Retirement and investment objectives
   - Risk tolerance and preferences
   - Short-term and long-term goals

2. Life Events:
   - Career and income changes
   - Family changes (marriage, children)
   - Major planned purchases

3. Investment Interests:
   - Asset allocation preferences
   - ESG or ethical investment interests
   - Previous investment experience

Exclude: Account numbers, SSNs, passwords, specific financial amounts.
"""

client.project.update(custom_instructions=finance_prompt)

const financePrompt = `
Extract financial planning information for advisory services:

1. Financial Goals:
   - Retirement and investment objectives
   - Risk tolerance and preferences
   - Short-term and long-term goals

2. Life Events:
   - Career and income changes
   - Family changes (marriage, children)
   - Major planned purchases

3. Investment Interests:
   - Asset allocation preferences
   - ESG or ethical investment interests
   - Previous investment experience

Exclude: Account numbers, SSNs, passwords, specific financial amounts.
`;

await client.project.update({ customInstructions: financePrompt });

Advanced Techniques

Conditional Processing

Handle different conversation types with conditional logic:

Python

advanced_prompt = """
Extract information based on conversation context:

IF customer support conversation:
- Issue type, severity, resolution status
- Customer satisfaction indicators

IF sales conversation:
- Product interests, budget range
- Decision timeline and influencers

IF onboarding conversation:
- User experience level
- Feature interests and priorities

Always exclude personal identifiers and maintain professional context.
"""

client.project.update(custom_instructions=advanced_prompt)

Testing Your Instructions

Always test your custom instructions with real message examples:

Python

# Test with sample messages
messages = [
    {"role": "user", "content": "I'm having billing issues with my subscription"},
    {"role": "assistant", "content": "I can help with that. What's the specific problem?"},
    {"role": "user", "content": "I'm being charged twice each month"}
]

# Add the messages and check extracted memories
result = client.add(messages, user_id="test_user")
memories = client.get_all(filters={"AND": [{"user_id": "test_user"}]})

# Review if the right information was extracted
for memory in memories:
    print(f"Extracted: {memory['memory']}")

Best Practices

✅ Do

• Be specific about what information to extract
• Use clear categories to organize your instructions
• Test with real conversations before deploying
• Explicitly state exclusions for privacy and compliance
• Start simple and iterate based on results

❌ Don’t

• Make instructions too long or complex
• Create conflicting rules within your guidelines
• Be overly restrictive (balance specificity with flexibility)
• Forget to exclude sensitive information
• Skip testing with diverse conversation examples

Common Issues and Solutions

Issue	Solution
Instructions too long	Break into focused categories, keep concise
Missing important data	Add specific examples of what to capture
Capturing irrelevant info	Strengthen exclusion rules and be more specific
Inconsistent results	Clarify guidelines and test with more examples