Overview
The Small Memory node enables you to record and store outputs from flows, creating persistent memory that can be accessed across multiple executions. This node acts as a data repository, allowing you to build conversation history, maintain session context, or accumulate data over time for use by other nodes in your flows.
Input Configuration
Stored Content Section
Purpose: Displays and manages all stored data entries
Data Collection: Records outputs from connected nodes automatically when the flow executes
Connection Point: Receives data inputs from other nodes on the left side
Data Format: Stores entries with timestamps, roles, and content in a structured table format
Initial State: Shows placeholder text "Record and store all outputs of a flow by connecting generators to this node"
Session Management
Session Selection
Session Dropdown: Choose between different storage sessions using the dropdown (e.g., "Test", "Primary")
Session Types: Access different data storage containers for organizing related information
Session Creation: Click the "+" icon to create new sessions through the "Create New Session" dialog
Session Naming: Enter descriptive names in the Session Name field when creating new sessions
Session Operations
Clear Entries: Remove all stored data from the current session using the Clear Entries button
Data Persistence: Sessions maintain data across multiple flow executions until manually cleared
Session Switching: Change between sessions to access different data sets
Advanced Settings
Access configuration options by clicking the settings icon:
Clear Execution
Purpose: Automatically clear stored entries on every flow execution
Toggle Setting: Enable or disable automatic clearing behavior
Use Case: Useful when you want fresh data for each execution rather than accumulating entries
Agent Session
Purpose: Enable or disable agent session mode for AI interactions
Toggle Setting: Controls how the memory integrates with conversational AI systems
Use Case: Optimizes data format for chatbot and AI assistant applications
Store by User Session
Purpose: Record entries by sessions with customer ID/User ID organization
Session Management: Organize data by individual users or customer sessions
Create New Session: Generate new user-specific storage containers
Data Segregation: Keep different users' data separate and organized
Data Output Configuration
Entry Filtering
Pass Last X Entries: Control how many recent entries to output to connected nodes
Configuration Options:
Pass All Entries: Output complete stored data set
Pass Last X Entries: Output specified number of most recent entries
Entry Count: Set specific number of entries to pass when using "Pass Last X Entries" mode
Output Control: Fine-tune data volume sent to downstream nodes
Data Structure
Table Format: Data displayed with columns for #, Timestamp, Role, and Content
Entry Numbering: Sequential numbering for easy reference
Timestamp Tracking: Automatic timestamp recording for each stored entry
Role Classification: Entries categorized by role (User, Assistant, etc.)
Content Storage: Full content preservation with expandable view options
Best Practices
Memory Organization
Session Naming: Use descriptive names for different memory purposes or contexts
Data Categorization: Separate different types of conversations or data flows into distinct sessions
Regular Maintenance: Periodically clear old or unnecessary entries to maintain performance
Session Strategy: Plan session structure based on your application's data organization needs
Performance Optimization
Entry Limits: Use "Pass Last X Entries" to control data volume for better performance
Selective Clearing: Clear sessions when data is no longer needed rather than accumulating indefinitely
Memory Monitoring: Track stored data volume to prevent excessive memory usage
Execution Strategy: Consider using "Clear Execution" for temporary data storage needs
Integration Patterns
Conversation Memory: Store chat history for AI assistants and chatbots
Data Accumulation: Collect outputs from multiple flow executions for analysis
Session Context: Maintain user-specific context across interactions
Historical Reference: Keep records for audit trails or data analysis
Integration Considerations
Flow Architecture
Input Connections: Connect various generator nodes to automatically store their outputs
Output Distribution: Stored data can feed into AI generators, analysis nodes, or reporting systems
Memory Chaining: Use multiple Small Memory nodes for different data categories
Session Coordination: Plan session usage across related flows for consistent data organization
Data Management
Entry Volume: Monitor storage capacity and implement appropriate clearing strategies
Data Quality: Ensure meaningful data is being stored rather than excessive noise
Access Patterns: Design output filtering based on downstream node requirements
Backup Considerations: Plan for data persistence and recovery needs
Use Cases
Chatbot Memory: Maintain conversation history for AI assistants
User Sessions: Track individual user interactions and preferences
Data Collection: Accumulate results from repeated flow executions
Context Preservation: Maintain relevant information across flow sessions
Audit Logging: Keep records of flow outputs for compliance or analysis
The Small Memory node provides essential data persistence capabilities, enabling sophisticated memory management and context preservation across flow executions for enhanced automation and AI interaction workflows.