If you are building agents, you already know the failure mode: long sessions get expensive, slow, and brittle because you keep paying to restate the past. This persistence engine fixes that by moving memory out of the prompt and into a durable store, then retrieving only what is relevant per turn. …

Beta invite: Persistence engine for agents, cut token usage up to 95 percent as sessions age

Topic		Replies	Views
Offline Autonomous AI Engineer: Phase 1–2 Complete — Local LLM + Memory + Eval Loop (Architecture Inside) Spaces	1	77	March 14, 2026
Designing multi-agent pipelines with shared state — how are you approaching it? Models	2	94	December 25, 2025
Can Proactive Recall Replace Query-Based retrieval in persistent AI memory? Research	1	26	February 24, 2026
widemem: open-source memory layer for LLMs with importance scoring, decay, and conflict resolution Show and Tell	0	14	March 15, 2026
Securing Large Vision-Language Models via Deterministic Orchestration Layers Awesome paper	2	89	December 30, 2025