I tested Sage’s Long-Term Memory and was Pleasantly Surprised by the Results!
Summary
The author tested Persistent Sage's long-term memory feature, finding it accurately recalled personal facts like colorblindness and a spouse's name from a week earlier without explicit prompting, demonstrating effective persistent memory for AI agents.
Similar Articles
From Recall to Forgetting: Benchmarking Long-Term Memory for Personalized Agents
Researchers introduce Memora, a benchmark that evaluates LLMs’ ability to retain, update, and forget long-term user memories over weeks-to-months conversations, revealing frequent reuse of obsolete memories.
SubtleMemory: A Benchmark for Fine-Grained Relational Memory Discrimination in Long-Horizon AI Agents
SubtleMemory is a benchmark for evaluating AI agents' fine-grained relational memory discrimination in long-horizon interactions, consisting of 1,522 instances over 10 long histories. It reveals limitations in current memory systems for preserving and utilizing nuanced memory relationships.
does anyone else's agent confidently "remember" stuff that's already changed?
A user describes an issue where AI agents confidently retrieve outdated facts from memory layers without flagging changes, and asks the community for solutions to invalidate old memories or track fact freshness.
For those creating personal assistants locally - how has short/long term memory impacted your experience?
A developer shares their experience building a local autonomous agent with long-term and short-term memory using Qwen 3.6 27B, noting that memory dramatically improves the agent's usefulness and realism. They invite others building similar agents to discuss memory techniques and potential agentic meetups.
Temporal Validity in Retrieval Memory: Eliminating Stale-Fact Errors for AI Agents over Evolving Knowledge
This paper introduces MemStrata, a retrieval memory system that maintains temporal validity to eliminate stale-fact errors in AI agents over evolving knowledge. It outperforms RAG on evolving benchmarks while preserving static recall, using a deterministic supersession layer without LLM calls.