Agent Memory

Agent Memory in Raindrop provides persistent memory systems for AI agents that need to maintain context, learn from interactions, and build ongoing relationships with users. Unlike stateless AI interactions, agent memory enables conversational continuity and personalized experiences across multiple sessions.

Ready to implement agent memory?

Check out the Agent Memory reference documentation for detailed API information, configuration options, and implementation examples.

What Agent Memory Provides

Session-Based Context Maintain conversation history and context within dedicated sessions that persist across multiple interactions and time periods.

Semantic Understanding Store and retrieve memories based on meaning and relationships rather than exact keywords, enabling intelligent context recall.

Timeline Organization Organize memories along different topics or themes within sessions for efficient retrieval and context management.

Metadata Tracking Rich metadata captures when memories were created, their relationships, and contextual information for intelligent search and retrieval.

When to Use Agent Memory

Good Fit

• Persistent AI Relationships: Personal assistants, tutors, or collaborative agents that improve through ongoing interaction
• Multi-Turn Workflows: Complex projects spanning multiple conversations that require continuity and context
• Learning Systems: AI agents that adapt behavior based on user preferences and interaction patterns
• Conversational Context: Chat systems where history and context significantly improve user experience

Consider Alternatives

• Transactional Interactions: Simple Q&A or one-off requests don’t benefit from persistent memory overhead
• Large Reference Data: Extensive knowledge bases work better in vector databases or traditional storage
• Business Logic: Complex rules and workflows belong in traditional application logic, not memory systems

Integration Patterns

Actor-Based Agents Combine agent memory with actors to create AI agents that have both persistent identity and persistent context across sessions.

Service Integration Services can access agent memory to provide context-aware responses while maintaining stateless request handling patterns.

Cross-Session Learning Aggregate insights across multiple memory sessions to identify patterns and improve agent behavior at scale.

Event-Driven Memory Respond to external events by updating memory context and contributing to system-wide intelligence and adaptation.

Agent Memory excels at enabling AI agents to build meaningful, persistent relationships and maintain rich conversational context over time.