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openviking

Unify AI context into a searchable graph with hierarchical L0/L1/L2 loading and Viking URIs.

4|2|Updated Feb 19, 2026
Search Tags:#integration#python#memory#semantic-retrieval#openviking#viking-uri#context-database
One-click install
npx skills add https://github.com/drunkcoding/AgentSkillsArxiv --skill openviking-drunkcoding
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openviking
Source: https://github.com/drunkcoding/AgentSkillsArxiv/tree/main/skills/openviking
Command: npx skills add https://github.com/drunkcoding/AgentSkillsArxiv --skill openviking-drunkcoding

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

OpenViking provides a unified context database to store and retrieve resources, memories, and skills for AI agents, enabling scalable, hierarchical content delivery and efficient long-term context management.

Core Features & Use Cases

  • Context layers (L0/L1/L2) support fast, progressive content loading and targeted retrieval.
  • Viking URI addressing (viking://) to organize resources, memories, and skills across projects and sessions.
  • Session-based memory extraction and long-term storage to build evolving agent knowledge.
  • Seamless integration with resources and tooling for research and development workflows.

Quick Start

Initialize the OpenViking client, load a small resource, and perform a simple search.

Frequently Asked Questions about openviking

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

FAQPage Schema
How do I build a long-term memory database for my AI agent?▼

Build long-term AI memory by extracting session-based context and storing it in a unified context database. This enables agents to retain evolving knowledge across research and development workflows.

What is hierarchical content loading for AI context retrieval?▼

Hierarchical content loading uses context layers like L0, L1, and L2 to support fast progressive content delivery. It enables targeted semantic retrieval by progressively loading context depth as needed.

How do I organize AI resources and memories using a URI scheme?▼

Organize AI resources and memories using Viking URI addressing, structured as viking:// paths. This scheme systematically categorizes resources, memories, and skills across different projects and sessions.

Does OpenViking require external dependencies to manage AI context?▼

OpenViking operates with zero external dependencies, running its configurable stack with safe defaults. You can initialize the client directly to add resources and perform semantic searches without extra setup.

Can I rerank semantic retrieval results within a context graph?▼

Yes, rerank semantic retrieval results within the searchable context graph. The system performs initial retrieval and then applies reranking to refine the relevance of the extracted memories and resources.

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