From the Blog
Insights on AI Memory
Research, engineering deep-dives, and product updates from the Hypermemory team.
Why Your AI Agent Forgets Everything — And How to Fix It
Long-running agents break down not because of bad reasoning, but because they can't remember. We explore the root causes of context degradation and the architecture that solves it.
Hybrid Retrieval: Why One Search Strategy Is Never Enough
Semantic search alone misses keywords. BM25 alone misses meaning. Temporal search alone misses context. Here's how fusing all five retrieval modes with RRF produces SOTA results.
One-Line Memory for Any LLM Framework
Whether you're on LangChain, LlamaIndex, CrewAI, or raw OpenAI — adding persistent memory to your agent should take minutes, not weeks. Here's how we built that.
SOTA on LoCoMo: Breaking Down the Benchmark Results
Hypermemory achieves state-of-the-art across all 5 LoCoMo domains. We walk through what each domain tests, where other systems fail, and why our temporal fact engine makes the difference.
Temporal Supersession: Tracking Facts That Change Over Time
"My meeting with Sarah is on Thursday" becomes stale the moment the meeting passes. Here's how Hypermemory's fact graph tracks current state vs. historical state without explicit updates.
Self-Hosting Hypermemory: A Complete Guide
Run Hypermemory entirely on your own infrastructure — on-prem, private cloud, or air-gapped. This guide covers deployment, Qdrant configuration, and production hardening.
City-Scale Surveillance: Correlating Threat Patterns Across Thousands of Cameras
Surveillance systems drown in data but starve for insight. We explore how unified memory across distributed camera networks enables real-time threat correlation, faster incident response, and 70% fewer false positives.