memory-recall

Name: memory-recall
Availability: InStock
Author: toperythroblast876

Search the ourmem shared memory store for relevant stored memories via semantic search.

Updated Apr 20, 2026

Search Tags:#claude-code #semantic-search #memory-retrieval #agent-context #memory-recall #shared-memory #ourmem

Ready to run

npx skills add https://github.com/Toperythroblast876/omem --skill memory-recall-toperythroblast876

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: memory-recall
Source: https://github.com/Toperythroblast876/omem/tree/main/plugins/claude-code/skills/memory-recall
Command: npx skills add https://github.com/Toperythroblast876/omem --skill memory-recall-toperythroblast876

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill solves the common issue of AI agents losing access to previously stored context, notes, and team knowledge across sessions, removing the need to manually sort through unstructured memory data to find relevant information.

Core Features & Use Cases

Semantic Memory Search: Queries the ourmem shared memory store using natural language to return the most relevant stored memories, ranked by relevance score.
Cross-Session Context Retrieval: Enables agents to pull up past task details, user preferences, or team knowledge saved in previous interactions without requiring the user to repeat information.
Use Case: If a user asks an agent what their preferred code review workflow is, the agent can use this Skill to quickly locate the stored note about their workflow instead of asking the user to restate it.

Quick Start

Use the memory-recall skill to search for all stored memories related to your team's Q3 project deadlines.

Frequently Asked Questions about memory-recall

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

FAQPage Schema

How do I retrieve shared agent memories across different sessions?▼

Semantic memory search retrieves shared agent memories across sessions by querying the ourmem persistent memory layer with natural language. It returns relevance-ranked results, eliminating manual context lookup and the need to restate past task notes or user preferences.

How do agents recall historical context and team knowledge without manual lookup?▼

Agents recall historical context by querying the ourmem shared memory store using natural language. This semantic search mechanism retrieves relevant stored memories ranked by relevance score, directly eliminating manual context lookup across unstructured memory data.

Can I use natural language to search persistent AI agent memory?▼

Yes, you can use natural language to search persistent AI agent memory. The memory-recall capability queries the ourmem v1 memories search API to find and rank relevant past task notes, team knowledge, and historical context based on semantic relevance.

Do I need an API key to access the ourmem shared memory store?▼

Yes, you need an API key for authenticated access to the ourmem shared memory store. The memory retrieval process uses this key to securely query the ourmem v1 memories search API and return relevance-ranked stored memories.

What's the best way to find user preferences saved in previous agent interactions?▼

The best way to find user preferences saved previously is to perform a semantic memory search across the ourmem shared memory store. This retrieves specific stored notes from past interactions without requiring the user to repeat information.

What limitations exist when relying on semantic search for agent context retrieval?▼

A limitation of semantic search for agent context retrieval is that it depends entirely on the data already stored in the ourmem persistent memory layer. It cannot retrieve external context or memories that were not explicitly saved during previous agent sessions.