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memory-learning

Migrate memory-learning users to memory-protocol for SQL-backed storage and vector embeddings.

7|3|Updated Mar 8, 2026
Search Tags:#recall#memory#sql#vector#postgres#embedding#memory-management
One-click install
npx skills add https://github.com/BaiGanio/aperio --skill memory-learning
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: memory-learning
Source: https://github.com/BaiGanio/aperio/tree/main/skills/memory-learning
Command: npx skills add https://github.com/BaiGanio/aperio --skill memory-learning

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

memory-learning is deprecated and merged into memory-protocol. This Skill documents the migration path and directs users to adopt memory-protocol for memory APIs, SQL access patterns, and embedding workflows.

Core Features & Use Cases

  • Migration guidance: move from memory-learning to memory-protocol with unified APIs for memory storage, retrieval, and semantic search.
  • Embedded memory workflows: leverage vector embeddings and SQL-backed storage to persist and recall memories across sessions.
  • Use Case: integrate with an AI agent that needs consistent memory across conversations and tasks by querying memory-protocol.

Quick Start

Load memory-protocol and follow its Direct Database Access guidance.

Frequently Asked Questions about memory-learning

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

FAQPage Schema
How do I persist AI agent memory across sessions using SQL?▼

Persistent agent memory across sessions is solved using SQL-backed storage and vector embeddings. You can store and recall contexts deterministically by querying a SQL database integrated with an embedding pipeline.

How does semantic search over past memories work with vector embeddings?▼

Semantic search over memories works by generating vector embeddings from stored text contexts and using vector similarity queries to retrieve relevant past interactions for an AI agent.

Can I use Postgres for AI memory management and retrieval?▼

Postgres can be used for AI memory management by leveraging its SQL-backed storage capabilities combined with vector embeddings to persist and retrieve agent memories and contexts across sessions.

How do I migrate from memory-learning to memory-protocol?▼

Migrating from memory-learning to memory-protocol involves loading the memory-protocol Skill and adopting its unified APIs for memory storage, retrieval, and semantic search workflows.

What is the best way to access stored memories deterministically in an AI agent?▼

Deterministic memory access is achieved through SQL-based access patterns over vector embedding storage, allowing AI agents to reliably query and retrieve specific memory contexts.

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