Developer details pgvector memory patterns for ByteDance's deer-flow long-running agents

A developer writing on DEV Community describes integrating ByteDance's deer-flow agent framework into a multi-hour autonomous research pipeline, where memory persistence proved harder than task orchestration. The framework separates agent memory into a session buffer, a vector store for semantic retrieval, and relational metadata such as timestamps and task lineage. The author shares a PostgreSQL and pgvector schema using an HNSW index, reporting that tuned settings cut insert latency from multi-second stalls to about 12ms at p99 under 500 concurrent inserts. Pure vector search surfaced tangential older memories over recent decisions, so the author fused vector similarity with recency and task-ancestry filters using reciprocal rank fusion.