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arXiv · 2604.19771

Cognis: Context-Aware Memory for Conversational AI Agents

Parshva Daftari·Khush Patel·Shreyas Kapale·Jithin George·Siva Surendira
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Abstract

LLM agents lack persistent memory, causing conversations to reset each session and preventing personalization over time. We present Lyzr Cognis, a unified memory architecture for conversational AI agents that addresses this limitation through a multi-stage retrieval pipeline. Cognis combines a dual-store backend pairing OpenSearch BM25 keyword matching with Matryoshka vector similarity search, fused via Reciprocal Rank Fusion. Its context-aware ingestion pipeline retrieves existing memories before extraction, enabling intelligent version tracking that preserves full memory history while keeping the store consistent. Temporal boosting enhances time-sensitive queries, and a BGE-2 cross-encoder reranker refines final result quality. We evaluate Cognis on two independent benchmarks -- LoCoMo and LongMemEval -- across eight answer generation models, demonstrating state-of-the-art performance on both. The system is open-source and deployed in production serving conversational AI applications.

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Parshva Daftari, Khush Patel, Shreyas Kapale, Jithin George, Siva Surendira. 2026-03-27. Cognis: Context-Aware Memory for Conversational AI Agents. https://arxiv.org/abs/2604.19771

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