Memory Context Recall

You can retrieve past conversation history by searching the agent's memory with configurable parameters like query text and time window. This enables context-aware responses by summarizing previous discussions and fetching specific facts.

Retrieving specific facts from previous interactions relies on a memory search tool using a targeted query text. It filters past agent memory by configurable grain and time window to find exact decisions or data points discussed earlier.

Memory recall for follow-up questions works by searching past agent memory to extract relevant prior context. The agent applies this retrieved history to understand references to previous decisions, enabling personalized and continuous interactions.

Yes, you can configure the time window when searching conversation memory. The memory search tool accepts time window parameters alongside query text and grain settings to scope retrieved past interactions accurately.

Yes, memory context retrieval requires a built-in search_memory tool to function. This tool queries the agent's internal memory using configurable grain, queryText, and time window parameters to synthesize past interaction data.

Use memory recall instead of current context when users reference previous decisions or past discussions not present in the immediate session. It retrieves historical facts and conversation history to maintain contextual awareness across interactions.