OpenViking

Overview

OpenViking is a context database designed specifically for AI Agents. It replaces fragmented vector-only storage with a filesystem-like paradigm (viking://) that organizes memories, resources and skills into hierarchical directories to improve retrieval effectiveness and observability.

Key Features

• Filesystem paradigm: unify context as directories and files, enabling directory-aware retrieval.
• Tiered context loading: L0 (abstract), L1 (overview), L2 (details) to reduce token usage and load content on demand.
• Visualized retrieval trajectory: preserve and surface retrieval paths for debugging and optimization.
• SDKs and examples: quickstart guides and language clients to integrate with existing model backends.

Use Cases

• Long-running agent sessions that require durable memory and context iteration.
• RAG systems that benefit from directory positioning plus semantic search for higher precision.
• Engineering workflows that need observable, debuggable retrieval and memory extraction loops.

Technical Highlights

• Modular architecture with retrieval, storage, session and parsing modules.
• Supports multiple model providers (OpenAI, Volcengine, custom providers) for embeddings and VLM.
• Provides example scripts and configuration for local and cloud deployments.