AgentDB Memory Patterns

Session memory in AgentDB stores conversational history between interactions, enabling agents to recall context and maintain continuity. This requires Node.js 18+, vector embedding support, and integration with ReasoningBank to retrieve past conversations for follow-up sessions.

Session memory tracks conversational history within a single interaction, while long-term memory persists facts and preferences across multiple sessions. AgentDB patterns enable both: session memory maintains immediate context, and long-term memory stores learned facts for personalization in future interactions.

Yes. AgentDB's pattern learning feature analyzes successful interactions and applies that knowledge to new tasks. Combined with session and long-term memory, agents extract recurring patterns and improve decision-making across interactions.

AgentDB memory patterns require Node.js 18 or higher, vector embedding support for semantic search, and ReasoningBank integration. These components enable efficient storage and recall of agent memories across stateful systems like chat applications and intelligent assistants.

AgentDB memory patterns manage context by combining session memory for immediate conversation flow with long-term memory for accumulated knowledge. Pattern learning then applies learned insights to new tasks, maintaining coherent behavior across interactions.