Memory Patterns Library

Production-ready patterns for building AI agent memory systems. Each pattern includes the problem, architecture, and working Dakera implementation code.

User Preference Recall

Store and retrieve user preferences across sessions. Personalize responses based on accumulated knowledge of individual users.

Cross-Session Context

Maintain conversation continuity across disconnected sessions. Resume context without requiring users to repeat themselves.

Multi-Agent Shared Memory

Enable multiple agents to share and build on each other's memories while maintaining isolation where needed.

Conversation Summarization & Decay

Automatically consolidate verbose conversation logs into concise summaries while letting low-importance details decay over time.

Knowledge Graph Entity Linking

Extract entities from memories and link them into a traversable knowledge graph for multi-hop reasoning.

Temporal Event Tracking

Track events over time and answer temporal queries like "what changed last week" or "when did the user start using X".

Importance-Based Forgetting

Score memories by importance and let low-value ones decay. Configure decay strategies to mimic natural human forgetting curves.

Namespace Isolation

Isolate memory by user, tenant, or agent using namespaces with scoped API keys for secure multi-tenant deployments.

Session Handoff

Transfer conversation context between agents or sessions. Enable seamless escalation from bot to human or between specialized agents.

Semantic Deduplication

Detect and merge semantically similar memories to prevent redundancy and keep the memory store clean and efficient.

RAG-Augmented Memory

Combine persistent agent memory with document retrieval for richer, more accurate context in LLM responses.

Memory Conflict Resolution

Detect and resolve conflicting memories when users change preferences or provide contradictory information over time.

Hierarchical Memory

Organize memory into tiers — short-term conversation, long-term facts, and permanent identity — with different retention policies.

Tool Usage Learning

Agents learn which tools work best for which tasks by storing outcomes and recalling successful patterns for similar queries.

Emotional Context Tracking

Track user sentiment across conversations to adjust tone, pacing, and response style for better user experience.

Memory Compression

Consolidate verbose memories into concise summaries to reduce storage while preserving key information for future recall.

Episodic Replay

Replay past interactions in sequence to understand how context evolved over time within and across sessions.

Track user goals across conversations with status management — active, completed, or abandoned — for continuity in long-running work.

Feedback Loop Memory

Store user corrections and feedback to avoid repeating mistakes and continuously improve response quality over time.

Context Window Management

Rank and trim recalled memories to fit LLM token budgets while maximizing relevance of injected context.

Memory Permissions

Implement role-based access control on memories using namespaces and scoped API keys for secure multi-tenant systems.

Batch Ingestion

Bootstrap agent memory from existing data sources — CRM exports, chat logs, documentation — with chunking and metadata tagging.