Openviking Context Database

OpenViking replaces fragmented vector stores with a filesystem paradigm for agent context. You organize memories, resources, and skills in directories, then query them with tiered loading: L0 for always-loaded identity, L1 for on-demand resources, L2 for semantic skill retrieval. The session compression feature automatically extracts long-term memories from conversation turns, which is cleaner than manually managing embeddings. Setup requires wiring your VLM and embedding providers through a config file, with support for OpenAI, LiteLLM, and local models. The observable trajectory tracking is genuinely useful for debugging why your agent pulled the wrong context. Best for agents that need persistent memory across tasks without blowing your token budget.

skills.sh installs: 1.3K
GitHub stars: 61
last updated: Jul 14, 2026

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Install

$ npx -y skills add aradotso/trending-skills --skill openviking-context-database --agent claude-code

Installs into .claude/skills of the current project.

Files

OpenViking Context Database

Skill by ara.so — Daily 2026 Skills collection.

OpenViking is an open-source context database for AI Agents that replaces fragmented vector stores with a unified filesystem paradigm. It manages agent memory, resources, and skills in a tiered L0/L1/L2 structure, enabling hierarchical context delivery, observable retrieval trajectories, and self-evolving session memory.

Installation

Python Package

pip install openviking --upgrade --force-reinstall

Optional Rust CLI

# Install via script
curl -fsSL https://raw.githubusercontent.com/volcengine/OpenViking/main/crates/ov_cli/install.sh | bash

# Or build from source (requires Rust toolchain)
cargo install --git https://github.com/volcengine/OpenViking ov_cli