agent-memory

To add long-term memory to an AI agent, you need a system that stores user preferences and conversation history persistently. This Skill provides that functionality by logging interactions and enabling context retrieval across multiple sessions.

Semantic search for user preferences works by utilizing embeddings to match natural language queries with stored data. This allows an AI agent to retrieve relevant historical context based on meaning rather than exact keyword matches.

Yes, you need Lakebase to manage persistent memory for AI agents. It serves as the required storage backend where the conversation history and user preferences are saved and retrieved.

You can automatically summarize completed tasks and past conversations for an AI agent. The Skill includes built-in task and conversation summarization features to log and retrieve historical interaction summaries.

The best way to retrieve a user's past preferences in a customer service agent is using the 'get_user_memory' tool. It fetches stored preferences by user ID, such as '[email protected]', ensuring consistent interactions.