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openviking

Manage and search structured knowledge with RAG and semantic search via MCP.

Updated Mar 12, 2026
Search Tags:#knowledge management#semantic search#RAG#vector memory#context database
One-click install
npx skills add https://github.com/Maosixian/selfused-xavier-lobsterop --skill openviking-maosixian
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openviking
Source: https://github.com/Maosixian/selfused-xavier-lobsterop/tree/main/skills/openviking
Command: npx skills add https://github.com/Maosixian/selfused-xavier-lobsterop --skill openviking-maosixian

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

OpenViking addresses the challenge of managing and retrieving large-scale, structured knowledge. It streamlines document processing and AI agent memory, making it an essential tool for efficient information retrieval and knowledge management.

Core Features & Use Cases

  • Context Database Management: Organizes large collections of documents using a filesystem paradigm.
  • Semantic Search and RAG: Implements state-of-the-art search and retrieval methods with a full RAG pipeline.
  • File-based Knowledge Base: Allows for the addition of resources to a vector memory system.
  • Use Case: Ideal for building an AI agent's memory or for indexing large document collections in organizations.

Quick Start

Trigger OpenViking by saying "openviking" or use a command like "search: machine learning papers".

Frequently Asked Questions about openviking

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

FAQPage Schema
How do I build a context database for AI agent memory?▼

OpenViking builds a context database for AI agents by organizing large-scale structured knowledge using a filesystem paradigm. It adds resources to a vector memory system, enabling efficient information retrieval.

How does semantic search work with RAG pipelines?▼

Semantic search in RAG pipelines queries a vector memory system to retrieve relevant document chunks. OpenViking integrates with MCP to handle these retrieval tasks, providing context to AI agents.

Do I need specific API keys to run RAG and semantic search tasks?▼

Yes, running RAG and semantic search tasks requires an OpenViking Context Database and Volcengine or Ark API keys. These dependencies provide the necessary infrastructure for vector memory and model processing.

What is the best way to index large document collections for knowledge management?▼

Indexing large document collections for knowledge management is best achieved through a file-based vector memory system. OpenViking adds resources to this system, streamlining document processing and structured knowledge retrieval.

How do I trigger a search query in a vector memory system?▼

Trigger a search query in a vector memory system by invoking the command 'search:' followed by your query terms, or by simply saying 'openviking'. This activates the semantic search pipeline over your indexed document collections.

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