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gbrain

Manage long-term memory and knowledge retrieval through a hybrid RAG-based CLI.

45|11|Updated Mar 17, 2026
Search Tags:#knowledge management#rag#search#ai agent#memory#second brain
One-click install
npx skills add https://github.com/beyonai/ByClaw --skill gbrain-beyonai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gbrain
Source: https://github.com/beyonai/ByClaw/tree/main/middleware/openclaw/skills/gbrain
Command: npx skills add https://github.com/beyonai/ByClaw --skill gbrain-beyonai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill solves the problem of fragmented knowledge and lost context by providing a unified, AI-managed memory layer that captures, organizes, and retrieves information across your entire digital workspace.

Core Features & Use Cases

  • Brain-First Retrieval: Prioritizes your own knowledge base over external web searches for personalized, accurate answers.
  • Intelligent Ingestion: Automatically routes and files diverse inputs like voice notes, articles, and meeting transcripts into the correct knowledge categories.
  • Use Case: When you need to recall a specific decision made in a meeting three months ago, this Skill queries your internal graph and timeline to provide the exact context, participants, and outcome without you needing to manually search through files.

Quick Start

Use the gbrain skill to query your brain for the latest project updates on the current initiative.

Frequently Asked Questions about gbrain

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

FAQPage Schema
How does an AI agent manage long-term memory for personal knowledge management?▼

An AI agent manages long-term memory by using a hybrid RAG interface to ingest diverse inputs like meeting transcripts and notes, maintaining a structured, queryable knowledge graph with strict citation tracking for provenance.

How do I retrieve specific decisions from past meeting transcripts using a second brain?▼

You retrieve past decisions by querying your internal knowledge graph and timeline, which prioritizes your own knowledge base over external web searches to provide exact context, participants, and outcomes.

What is the best way to organize fragmented notes and research archives into a unified memory layer?▼

The best way to unify fragmented notes is through intelligent ingestion, which automatically routes and files diverse inputs like voice notes and articles into the correct knowledge categories within a structured graph.

Can I use RAG search to query my personal notes instead of external web searches?▼

Yes, you can use brain-first retrieval to prioritize your personal knowledge base over external web searches, ensuring personalized, accurate answers directly from your managed memory layer.

Does a hybrid RAG knowledge graph maintain data integrity and provenance for ingested research?▼

A hybrid RAG knowledge graph maintains data integrity by implementing strict filing protocols, citation tracking, and entity propagation across diverse data sources to ensure provenance.

What limitations exist when using a CLI interface for knowledge management and memory retrieval?▼

Using a CLI interface for knowledge management requires navigating text-based commands to query your knowledge graph, meaning you must rely on strict filing protocols rather than visual interfaces for entity propagation.

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