Memory & Context

Memory in AI workflows is like giving your automation a notebook to remember important information from previous interactions. This allows AI to provide more relevant responses, maintain conversation context, and build upon past knowledge.

Without memory, every interaction starts from scratch. With memory, AI can learn, adapt, and provide increasingly better assistance.

Why memory matters

Imagine talking to someone who forgets everything you said 5 minutes ago. That’s how AI works without memory - every interaction is completely isolated.

graph TD
    subgraph "Without Memory"
        A1[User: "What's the weather?"] --> B1[AI: "It's sunny"]
        C1[User: "What about tomorrow?"] --> D1[AI: "What location?"]
    end
    
    subgraph "With Memory"
        A2[User: "What's the weather?"] --> B2[AI: "It's sunny"]
        B2 --> Memory[(Remembers: Location, Context)]
        C2[User: "What about tomorrow?"] --> Memory
        Memory --> D2[AI: "Tomorrow will be cloudy"]
    end
    
    style Memory fill:#6d28d9,stroke:#fff,color:#fff

Types of memory

Conversation Memory

Purpose: Remember what was said in a conversation

Best for:

• Chatbots and AI assistants
• Customer support systems
• Interactive workflows

Example:

User: "I'm having trouble with my order"
AI: "I can help! What's your order number?"
User: "It's #12345"
AI: "I see order #12345 was placed yesterday. What specific issue are you having?"

The AI remembers the order number without asking again.

Task Memory

Purpose: Remember progress on multi-step tasks

Best for:

• Complex workflows
• Research projects
• Data collection tasks

Example:

Step 1: Collected pricing from 3 competitors ✓
Step 2: Analyzed feature comparisons ✓
Step 3: Currently gathering customer reviews...

The workflow knows what’s been completed and what’s next.

Knowledge Memory

Purpose: Build up knowledge over time

Best for:

• Learning systems
• Personalization
• Adaptive workflows

Example:

Learned: User prefers detailed technical explanations
Learned: User works in healthcare industry
Learned: User typically asks about compliance topics

Future interactions become more relevant and personalized.

Memory strategies

Short-term memory

Remembers recent interactions within a single session:

graph LR
    Recent[Last 5-10 Messages] --> Context[Current Context]
    Context --> Response[AI Response]
    Response --> Update[Update Memory]
    Update --> Recent
    
    style Context fill:#e1f5fe

Good for:

• Maintaining conversation flow
• Avoiding repetitive questions
• Understanding immediate context

Long-term memory

Stores important information across multiple sessions:

graph LR
    Important[Key Information] --> Storage[(Persistent Storage)]
    Storage --> Retrieval[Smart Retrieval]
    Retrieval --> Context[Enhanced Context]
    
    style Storage fill:#6d28d9,stroke:#fff,color:#fff

Good for:

• User preferences and history
• Learned patterns and insights
• Building knowledge over time

Implementing memory

1. Choose Memory Type: Decide what information needs to be remembered

2. Set Storage Limits: Determine how much history to keep (memory has costs)

3. Design Retrieval: Plan how to find relevant memories when needed

4. Handle Updates: Decide when to update, modify, or delete memories

5. Manage Privacy: Ensure sensitive information is handled appropriately

Memory patterns

Conversation buffer

Keeps a rolling window of recent messages:

Memory: [Message 1, Message 2, Message 3, Message 4, Message 5]
New message arrives → Remove Message 1, Add Message 6

Pros: Simple, predictable memory usage Cons: May lose important early context

Summary memory

Summarizes old conversations to save space:

Detailed Memory: Last 10 messages
Summary Memory: "User is researching competitors for SaaS pricing"

Pros: Retains key information, uses less storage Cons: May lose nuanced details

Selective memory

Only remembers important information:

All Messages: 50 messages
Stored Memory: 8 key facts and decisions

Pros: Highly efficient, focuses on what matters Cons: Requires smart filtering to avoid losing context

Real-world examples

Customer support bot

Memory stores:
- Customer name and account details
- Previous issues and resolutions
- Preferred communication style
- Current conversation context

Result: Personalized, efficient support

Research assistant

Memory stores:
- Research topics and findings
- Sources already checked
- Key insights discovered
- User's research preferences

Result: Builds comprehensive knowledge over time

Content moderator

Memory stores:
- Previous moderation decisions
- User behavior patterns
- Content policy updates
- Appeal outcomes

Result: Consistent, learning-based moderation

Privacy considerations

Memory systems store user data. Always consider privacy implications, data retention policies, and user consent when implementing memory features.

Memory challenges

Storage limits

• Browser storage has size limits
• Large memories slow down processing
• Need strategies for memory cleanup

Relevance filtering

• Not all information is worth remembering
• Need to identify what’s important
• Balance between too much and too little memory

Context retrieval

• Finding the right memories at the right time
• Avoiding information overload
• Maintaining conversation flow

Memory conflicts

• What to do when memories contradict
• Handling outdated information
• Updating vs. replacing memories

Memory transforms AI from reactive tools into proactive assistants that learn, adapt, and provide increasingly valuable interactions over time.