What is Conversation Memory?
Conversation memory refers to the system that tracks and maintains the history of dialogue between a user and an AI agent, enabling the agent to understand context, maintain coherence, and reference previous exchanges during ongoing interactions. This type of memory is fundamental to creating conversational agents that feel natural and contextually aware rather than treating each input as an isolated query.
The primary function of conversation memory is to provide the agent with awareness of what has been discussed, what questions have been asked, what answers have been given, and what preferences or information the user has shared. This allows the agent to avoid repeating itself, build upon previous statements, resolve pronouns and references correctly, and maintain a consistent persona and tone throughout the interaction.
Different implementations of conversation memory offer various trade-offs between completeness and efficiency. Simple buffer-based approaches store the raw conversation history, while more sophisticated approaches may summarize older exchanges, extract key facts, or use vector storage to retrieve relevant past interactions. The choice of conversation memory strategy depends on factors like expected conversation length, the importance of exact recall versus general context, and constraints on model context window usage.