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A developer shares their experience building an AI agent with memory using the Anthropic SDK and TypeScript, explaining the differences between working, episodic, semantic, and procedural memory and the challenges of scaling memory for production.
An ex-Google engineer explains AI agent memory architecture in 12 minutes, covering working memory and three memory layers (procedural, semantic, episodic) with a summarizer to prevent token bloat, as used by Claude.
ActiveGraph introduces a deterministic non-generative approach for evidence compilation before semantic memory, achieving 85.6% QA accuracy and 86.2% turn answer-in-context on LongMemEval-S.
Memanto introduces a typed semantic memory system using a schema, conflict resolution, and Moorcheh's information-theoretic retrieval engine, achieving state-of-the-art results on LongMemEval and LoCoMo benchmarks with zero ingestion cost and sub-90ms latency.
This paper introduces CIG (Conversational Information Gain), a framework for measuring how utterances advance collective understanding in deliberative dialogues by tracking evolving semantic memory and scoring utterances on novelty, relevance, and implication scope. The authors demonstrate that memory-derived dynamics correlate better with human-perceived dialogue quality than traditional heuristics and develop LLM-based predictors for information-focused conversation analysis.