Cognis: Context-Aware Memory for Conversational AI Agents
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-enco...
| Authors | Parshva Daftari, Khush Patel, Shreyas Kapale et al. |
|---|---|
| Primary category | cs.CL |
| Published | 27 March 2026 |
| Last updated at source | 27 March 2026 |
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