Agents with persistent memory
A support agent learns preferences on session 1 and applies them on session 100.
User preference recallMemory patterns,production-ready
A library of production-ready patterns for building AI agent memory systems. Each pattern includes the problem, the architecture and working Dakera implementation code.
- 22production patterns
- 8categories
- 4languages: Python, TypeScript, Go, Rust
What developers build with persistent memory
From solo agent projects to production multi-agent pipelines. Six starting points, each with a working implementation.
Multi-agent knowledge pipelines
A researcher stores findings; a writer recalls them by meaning in the next task.
Multi-agent shared memoryRAG with decay-weighted recall
Fresh sources surface, stale ones sink. Retrieval stays relevant without curation.
RAG-augmented memoryChatbots that remember preferences
Personalization that compounds across weeks, without prompt engineering.
Cross-session contextCopilots that learn your workflows
Naming conventions, architecture decisions and library patterns, learned via MCP.
Tool usage learningTeam memory for LLM dev tools
New engineers query the same memory experienced ones have been building.
Hierarchical memoryAll 22 patterns
Retrieval, lifecycle, architecture and more. Search by name or idea, or narrow to a category.
22 patterns
User preference recall
RetrievalStore and retrieve user preferences across sessions. Personalize responses based on accumulated knowledge of individual users.
Cross-session context
RetrievalMaintain conversation continuity across disconnected sessions. Resume context without requiring users to repeat themselves.
Multi-agent shared memory
ArchitectureEnable multiple agents to share and build on each other's memories while maintaining isolation where needed.
Conversation summarization and decay
LifecycleAutomatically consolidate verbose conversation logs into concise summaries while letting low-importance details decay over time.
Knowledge graph entity linking
KnowledgeExtract entities from memories and link them into a traversable knowledge graph for multi-hop reasoning.
Temporal event tracking
RetrievalTrack events over time and answer temporal queries like "what changed last week" or "when did the user start using X".
Importance-based forgetting
LifecycleScore memories by importance and let low-value ones decay. Configure decay strategies to mimic natural human forgetting curves.
Namespace isolation
ArchitectureIsolate memory by user, tenant or agent using namespaces with scoped API keys for secure multi-tenant deployments.
Session handoff
ArchitectureTransfer conversation context between agents or sessions. Enable seamless escalation from bot to human or between specialized agents.
Semantic deduplication
LifecycleRemove duplicate memories with Dakera's deduplication and AutoPilot, and merge paraphrased facts in your app, so the memory store stays clean.
RAG-augmented memory
RetrievalCombine persistent agent memory with document retrieval for richer, more accurate context in LLM responses.
Memory conflict resolution
LifecycleDetect and resolve conflicting memories when users change preferences or provide contradictory information over time.
Hierarchical memory
ArchitectureOrganize memory into tiers (short-term conversation, long-term facts and permanent identity) with different retention policies.
Tool usage learning
Agent behaviorAgents learn which tools work best for which tasks by storing outcomes and recalling successful patterns for similar queries.
Emotional context tracking
Agent behaviorTrack user sentiment across conversations to adjust tone, pacing and response style for a better user experience.
Memory compression
OptimizationConsolidate verbose memories into concise summaries to reduce storage while preserving key information for future recall.
Episodic replay
RetrievalReplay past interactions in sequence to understand how context evolved over time within and across sessions.
Goal tracking
Agent behaviorTrack user goals across conversations with status management (active, completed or abandoned) for continuity in long-running work.
Feedback loop memory
Agent behaviorStore user corrections and feedback to avoid repeating mistakes and continuously improve response quality over time.
Context window management
OptimizationRank and trim recalled memories to fit LLM token budgets while maximizing the relevance of injected context.
Memory permissions
SecurityImplement role-based access control on memories using namespaces and scoped API keys for secure multi-tenant systems.
Batch ingestion
OperationsBootstrap agent memory from existing data sources (CRM exports, chat logs, documentation) with chunking and metadata tagging.
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Further readingHow agent memory worksMemory decay and temporal weightingKnowledge graphs for AI agentsMulti-agent memory systemsQuickstart guide