Persistent memory lets agents recall knowledge across sessions instead of starting fresh each run. LangGraph accomplishes this by externalizing state and history. Each node can store its intermediate results, conversation summaries, or retrieved documents to disk or an external database, then reload them when the graph restarts.
Developers can combine short-term in-memory variables with long-term storage to balance speed and continuity. For long-term recall, vector embeddings are ideal because they allow fuzzy matching rather than exact text lookup. Storing those embeddings in Milvus creates a persistent semantic memory: when a new session begins, the agent queries the collection for similar past cases and restores context instantly.
This design supports continuous learning without retraining. Agents accumulate structured experiences over time, and because Milvus indexes include timestamps and metadata, you can filter memory by recency or relevance. The result is a system that behaves consistently across sessions while remaining transparent and easy to maintain.