> ## Documentation Index > Fetch the complete documentation index at: https://mintlify.com/volcengine/OpenViking/llms.txt > Use this file to discover all available pages before exploring further. # Introduction > OpenViking is an open-source context database designed specifically for AI Agents ## What is OpenViking? **OpenViking** is an open-source **Context Database** designed specifically for AI Agents. It revolutionizes how AI agents manage and retrieve context by introducing a **file system paradigm** that unifies memories, resources, and skills into a coherent, hierarchical structure. Unified storage for memories, resources, and skills using a filesystem paradigm Directory recursive retrieval for better context understanding and accuracy L0/L1/L2 architecture reduces token consumption by loading context on-demand Automatic session management enables agents to become smarter with use ## Why OpenViking? In the AI era, data is abundant, but high-quality context is scarce. When building AI Agents, developers often face critical challenges: ### The Problems Memory scattered in code, resources distributed across vector databases, and skills stored in various locations make unified management nearly impossible. Long-running agent tasks generate massive amounts of context with each execution. Simple truncation or compression leads to critical information loss. Traditional RAG systems use flat storage, lacking global perspective and struggling to understand complete context relationships. Traditional RAG's implicit retrieval pipeline is like a black box, making debugging and optimization extremely difficult. Current memory systems only record user interactions, lacking agent-related task memories and experience accumulation. ### The OpenViking Solution OpenViking addresses these challenges with five innovative approaches: Unified context management of memories, resources, and skills based on a filesystem paradigm. All context is organized under the `viking://` protocol with unique URIs. ``` viking:// ├── resources/ # Project docs, repos, web pages │ └── my_project/ │ ├── docs/ │ └── src/ ├── user/ # User preferences, habits │ └── memories/ └── agent/ # Skills, instructions, task memories ├── skills/ └── memories/ ``` L0/L1/L2 three-tier structure loaded on demand significantly reduces token consumption: * **L0 (Abstract)**: \~100 tokens - Quick relevance check * **L1 (Overview)**: \~2k tokens - Understand structure and key points * **L2 (Detail)**: Unlimited - Full content loaded when needed Combines directory positioning with semantic search for recursive and precise context acquisition. This "lock high-score directory first, then refine content exploration" strategy improves both accuracy and completeness. Complete preservation of directory browsing and file positioning for each retrieval allows clear observation of problem sources and guides retrieval logic optimization. Built-in memory self-iteration loop automatically analyzes task execution and user feedback, updating User and Agent memory directories to enable continuous learning. ## Core Features ### File System Management Paradigm OpenViking organizes all context as a virtual file system, enabling agents to manipulate information through standard filesystem commands: ```python theme={null} import openviking as ov client = ov.OpenViking(path="./data") client.initialize() # Unix-like filesystem operations client.ls("viking://resources/") # List directory client.read("viking://resources/doc") # Read content client.mkdir("viking://agent/skills/") # Create directory client.find("user authentication") # Semantic search ``` ### Context Types OpenViking manages three types of context: | Type | Purpose | Lifecycle | | ------------ | --------------------------------------------------------- | ------------------------------ | | **Resource** | Knowledge and rules (docs, code, FAQ) | Long-term, relatively static | | **Memory** | Agent's cognition (user preferences, learned experiences) | Long-term, dynamically updated | | **Skill** | Callable capabilities (tools, MCP) | Long-term, static | ### Hierarchical Context Architecture Every resource is automatically processed into three layers: ``` viking://resources/my_project/ ├── .abstract.md # L0: One-sentence summary ├── .overview.md # L1: Core information ├── docs/ │ ├── .abstract.md # Each directory has L0/L1 │ ├── .overview.md │ └── api.md # L2: Full original content └── src/ ``` This tiered approach enables: * **Fast retrieval** using L0 abstracts * **Informed decisions** using L1 overviews * **Deep analysis** using L2 details only when needed OpenViking's hierarchical architecture can reduce token consumption by **83-96%** compared to traditional RAG systems while improving task completion rates by **15-49%** (based on LoCoMo10 benchmark testing). ## OpenViking vs Traditional RAG | Feature | Traditional RAG | OpenViking | | ---------------------- | ------------------------- | ------------------------------------- | | **Storage Model** | Flat vector slices | Hierarchical filesystem | | **Context Access** | Semantic search only | Deterministic paths + semantic search | | **Token Efficiency** | Load all retrieved chunks | Tiered loading (L0/L1/L2) | | **Retrieval Strategy** | Single-pass vector search | Directory recursive retrieval | | **Observability** | Black box | Full retrieval trajectory | | **Memory Evolution** | Static snapshots | Automatic self-iteration | ## Use Cases Maintain comprehensive knowledge bases with automatic updates and hierarchical organization Navigate large codebases efficiently with filesystem-based context retrieval Accumulate experience and improve performance through automatic memory iteration Handle text, images, and documents with unified VLM-powered processing ## Performance Based on LoCoMo10 long-range dialogue benchmark (1,540 test cases): **Task Completion Rate Improvements:** * **+43-49%** vs original baseline * **+15-17%** vs traditional vector database (LanceDB) **Token Cost Reduction:** * **83-96%** reduction in input token consumption Test configuration: OpenViking v0.1.18, seed-2.0-code model ## Next Steps Get OpenViking running in 5 minutes Deploy OpenViking as a production service Explore the complete API documentation View source code and contribute