core-memory

Recalls, stores, and searches persistent memories across agent sessions.

609|81|Updated Nov 15, 2024
One-click install
npx skills add https://github.com/QuixiAI/Hexis --skill core-memory
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: core-memory
Source: https://github.com/QuixiAI/Hexis/tree/main/skills/installed/core-memory
Command: npx skills add https://github.com/QuixiAI/Hexis --skill core-memory

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

LLM agents lose all context between sessions, forcing users to repeat themselves and preventing any real continuity. This Skill gives the agent durable memory operations so it can recall past conversations, store important facts, and maintain goals and commitments over time.

Core Features & Use Cases

  • Semantic and Exact Recall: Use recall for targeted semantic retrieval and search_history for exact names, phrases, or details from earlier sessions, with recall_at_time and diff_memory_history for point-in-time and change-over-time queries.
  • Durable Storage and Commitments: Persist facts, events, preferences, and decisions with remember, and manage goals, schedules, backlogs, and watch-and-notify responsibilities with dedicated management tools.
  • Source Document Access: Climb the cabinet ladder from search_documents and search_document_chunks to open_document and desk tools to cite ingested source material directly.
  • Use Case: A user asks "what did we decide about the database migration last month?" The agent recalls the relevant memory, opens the source document for the exact wording, and cites the decision with its date.

Quick Start

Ask the agent to recall what was discussed in previous sessions about a specific topic and store any new decisions from the current conversation.

Frequently Asked Questions about core-memory

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

FAQPage Schema
How do I give an LLM agent persistent memory across sessions?

Use the remember tool to store durable facts, events, preferences, and decisions, then retrieve them later with recall for semantic queries or search_history for exact phrases. The agent checks memory before claiming it does not know something.

How to search past conversations for exact phrases or names?

Use search_history with the exact name, phrase, or detail you need from earlier sessions. It works even when semantic recall is weak or embeddings are unavailable, making it the fallback for precise lookups.

What is the difference between semantic recall and exact history search?

Recall performs targeted semantic retrieval and works best with specific queries about topics or concepts. Search_history finds exact strings from prior sessions, so use it for names, quotes, and precise details.

Can the agent answer what it knew at a specific past time?

Yes, recall_at_time answers what was known as of a past instant, and diff_memory_history shows what changed between two points in time. This supports auditing how beliefs and memories evolved.

When should information not be stored in agent memory?

Do not store secrets unless the user explicitly asks, and avoid persisting every minor sentence. Memory should hold facts, promises, and decisions that will matter later, keeping tool use proportional to the task.