Unverified50% confidenceSolutionExact time
The agent's decision history is stored in the graph as AgentSession nodes, forming queryable structured memory rather than a messy chat log.
1
Sources
50%
Confidence
Long-term
Relevance
10/18/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe key to maintaining the conversational context of a ReAct agent is to keep the message list outside the loop and append new content to the list each round; otherwise the agent will lose its memory and be unable to reason based on historical context.74% similarUnverifiedAgentCore Memory's short-term memory is an append-only event log keyed by actor and session.73% similarUnverifiedThe proactive memory paradigm allows the system to autonomously determine which content has long-term value during a conversation and persist it, and to proactively inject relevant historical context when generating responses, without requiring the user to explicitly trigger retrieval.72% similarUnverifiedThe workflow continuously stores the conversation history of the same customer through a memory mechanism and dynamically adjusts the score accordingly.72% similarUnverifiedIn Agent systems, long-term memory is typically implemented by storing historical conversations or external knowledge into a vector database and retrieving relevant snippets to inject into the context when needed — this is the foundational logic of RAG (Retrieval-Augmented Generation) technology.71% similar
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