agent-memory

Name: agent-memory
Availability: InStock
Author: ananyadbj

Store and retrieve user preferences and conversation history with semantic search.

6|10|Updated Mar 23, 2026

Search Tags:#semantic search #AI agent #memory management #conversation history #user preferences

Ready to run

npx skills add https://github.com/AnanyaDBJ/databricks-ai-workshops --skill agent-memory-ananyadbj

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agent-memory
Source: https://github.com/AnanyaDBJ/databricks-ai-workshops/tree/main/advanced/.claude/skills/agent-memory
Command: npx skills add https://github.com/AnanyaDBJ/databricks-ai-workshops --skill agent-memory-ananyadbj

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a solution for persistently storing user preferences and conversation history, enabling the AI agent to maintain context over extended interactions.

Core Features & Use Cases

Persistent User Memory: Store and retrieve user preferences and history for consistent interactions.
Semantic Search: Implement a search feature that allows users to query their stored data using natural language.
Task and Conversation Summarization: Automatically log and retrieve summaries of completed tasks and conversation history.
Use Case: Imagine you have a customer service agent that needs to remember preferences and past interactions with a user across multiple sessions.

Quick Start

Use the 'get_user_memory' tool to retrieve the preferences of a user with the ID '[email protected]'.

Frequently Asked Questions about agent-memory

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I add long-term memory to an AI agent for persistent conversation history?▼

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.

How does semantic search work for retrieving stored user preferences?▼

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.

Do I need Lakebase to manage persistent memory for AI agents?▼

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.

Can I automatically summarize completed tasks and past conversations for an AI agent?▼

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.

What is the best way to retrieve a user's past preferences in a customer service agent?▼

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.