How Memory Works In DeerFlow?

DeerFlow, ByteDance’s open-source superagent harness, uses a local JSON-based memory system that distills conversations into confidence-scored user facts, summaries, and current priorities rather than replaying full chat histories. Its LangGraph-based MemoryMiddleware processes exchanges asynchronously after responses are delivered, applying a 30-second debounce, extracting additions and removals through an LLM, and storing up to 100 facts while prioritizing higher-confidence entries. At the start of future sessions, DeerFlow injects stored summaries and the highest-confidence facts that fit a 2,000-token prompt budget, enabling persistent personalization without a vector database or synchronous retrieval overhead. The approach offers local control, inspectable storage, atomic writes, and low latency, but testing identified limitations including failed deletion requests, occasional irrelevant language recommendations, text-only rather than semantic deduplication, and retrieval based on confidence rather than relevance to the current query.

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