This article describes an Elasticsearch-based system for long-term agent memory, dividing data into episodic, semantic, and procedural indices with tailored aging and update rules. It uses hybrid retrieval—BM25 plus dense vectors fused by RRF and refined with a cross-encoder reranker—alongside per-user DLS, supersession, decay, and an LLM-driven consolidation process to keep memory accurate and scalable.

• Splitting agent memory into episodic, semantic, and procedural indices (each with its own decay/update rules) avoids one-size-fits-all tradeoffs in write rate and aging logic.
• Hybrid retrieval (BM25 + Jina v5 embeddings fused via RRF, then reranked with a Jina v2 cross-encoder over 160 candidates) hit 0.89 recall@10 across 168 test queries.
• Contradictions are resolved via supersession with time-decay ranking rather than deletion, preserving an audit trail while surfacing current facts.
• Document-level security enforced per-user isolation with zero cross-tenant leaks, all within a single Elasticsearch cluster exposed as an MCP-compatible tool.