Memory Decay: First-Class Feature in Dakera, Not a Plugin
Why AI agents need built-in memory lifecycle management — and how Dakera ships native decay curves, importance weighting, and automatic cleanup without pip.
Dakera AI Team
Blog category
Engineering
How Dakera works inside: retrieval, decay, knowledge graphs, architecture and the Rust engine.
7 posts
Building Multi-Agent Memory Systems: Architecture Patterns
Architecture patterns for sharing memory between AI agents. Learn namespaces, sessions, and cross-agent retrieval for multi-agent systems.
Dakera AI Team
Knowledge Graphs for AI Agents: Extraction and Traversal
Build knowledge graphs that AI agents can traverse. Learn entity extraction, relationship modeling with 4 edge types, and graph-enhanced memory retrieval.
Dakera AI Team
Vector Databases vs Agent Memory: Why They're Not the Same Thing
Vector databases store embeddings. Agent memory understands time, relationships, importance, and decay. Learn why Pinecone and Weaviate aren't enough for AI agents.
Dakera AI Team
Temporal Memory for AI Agents: Decay and Importance Scoring
Deep dive into temporal memory systems for AI agents. Learn how memory decay, importance scoring, and recency weighting create human-like recall patterns.
Dakera AI Team
Why Rust for AI Memory: Performance, Safety, and a ~101 MB Binary
Why Dakera chose Rust for AI agent memory: ~101 MB binary, zero GC pauses, no language runtime, 88.2% Recall@20 on LoCoMo. The case for Rust in always-on infrastructure.
Dakera AI Team
How Agent Memory Works: Hybrid Retrieval and Decay
A technical deep-dive into Dakera's retrieval engine — HNSW vector search, BM25 full-text, hybrid scoring, and importance decay with half-life scheduling.
Dakera AI Team
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