OpenViking Agent工作流

Deploy in production

For production, run OpenViking as a standalone HTTP service — see Server deployment and the Deployment guide.

Quick start

💡 Want to see it in action first? Try OpenViking Studio — a live hosted instance with a context playground, semantic search, and a multi-agent hub. No installation required.

Requires Python 3.10 or higher.

pip install openviking --upgrade
openviking-server init      # interactive wizard: providers, models, ov.conf
openviking-server doctor    # validate setup
openviking-server           # start (background: nohup openviking-server > openviking.log 2>&1 &)

init walks you through provider setup and writes ~/.openviking/ov.conf. It supports Volcengine, OpenAI, Codex OAuth, Kimi, GLM, and local Ollama — for Ollama it can detect and install the runtime and pull models suited to your hardware. doctor checks the config file, Python version, provider connectivity, and disk space without a running server. Manual ov.conf templates, per-provider examples, environment variables, and Windows setup: Configuration guide · Quick start docs.

The install already includes the ov client CLI. With the server running:

```bash ov status ov add-resource https://github.com/volcengine/OpenViking # --wait ov ls viking://resources/ ov tree viking://resources/volcengine -L 2

OpenViking: The Context Database for AI Agents

English / 中文 / 日本語

<a href="https://www.openviking.ai">Website</a> · <a href="https://openviking.ai/studio">Live Demo</a> · <a href="https://github.com/volcengine/OpenViking">GitHub</a> · <a href="https://github.com/volcengine/OpenViking/issues">Issues</a> · <a href="https://docs.openviking.ai/">Docs</a>

👋 Join our Community

📱 <a href="https://docs.openviking.ai/en/about/01-about-us#lark-group">Lark Group</a> · <a href="https://docs.openviking.ai/en/about/01-about-us#wechat-group">WeChat</a> · <a href="https://discord.com/invite/eHvx8E9XF3">Discord</a> · <a href="https://x.com/openvikingai">X</a>

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What is OpenViking

OpenViking is an open-source context database for AI agents. It stores memories, resources, and skills as one virtual filesystem under the viking:// protocol, so an agent browses its own context with ls, tree, and find instead of querying a black-box vector store. Content is processed into three tiers — L0 abstract, L1 overview, L2 details — and loaded on demand. Every retrieval leaves a trajectory you can watch and debug. Full introduction: Getting started.

The OpenViking Studio playground — a live demo you can open in the browser, no installation required.

Why OpenViking

• One filesystem for all context. Memories, resources, and skills each get a viking:// URI. Agents locate and manipulate context deterministically, like a developer working with files. → Viking URI · Context types
• Tiered loading cuts token spend. Every entry is processed into L0 (abstract), L1 (overview), and L2 (details) on write, then loaded only as deep as the task requires. → Context layers
• Directory recursive retrieval. Vector search first locates the highest-scoring directory, then drills down layer by layer, so results arrive with their surrounding context intact. → Retrieval
• Observable retrieval. Each query preserves its directory-browsing trajectory. When a result looks wrong, you can see exactly which path produced it. → Retrieval
• Sessions become memory. After a session commits, OpenViking asynchronously extracts user preferences and agent experience into long-term memory. → Session

How the pieces fit together: Architecture. The thinking behind the design: The Database Paradigm for Context Engineering.

viking://
├── resources/              # Resources: project docs, repos, web pages, etc.
│   └── my_project/
│       ├── docs/
│       │   ├── api/
│       │   └── tutorials/
│       └── src/
└── user/
    └── {user_id}/
        ├── memories/
        │   └── preferences/
        │       ├── writing_style
        │       └── coding_habits
        ├── resources/
        │   └── private_project/
        ├── skills/
        │   ├── search_code
        │   └── analyze_data
        └── peers/
            └── web-visitor-alice/

The three loading tiers:

• L0 (Abstract): a one-sentence summary for quick relevance checks.
• L1 (Overview): core information and usage scenarios for planning.
• L2 (Details): the full original data, read only when needed.

Each directory carries its own L0/L1 layers, so relevance can be judged before any full file is read:

viking://resources/my_project/
├── .abstract               # L0: ~100 tokens - quick relevance check
├── .overview               # L1: ~2k tokens - structure and key points
└── docs/
    ├── .abstract
    ├── .overview
    └── api/
        ├── auth.md         # L2: full content, loaded on demand
        └── endpoints.md

OpenViking Helper (Beta)

OpenViking Helper is a desktop console, currently in beta for macOS and Windows x64:

• Visual local agent setup: detects OpenViking CLI, Claude Code, Codex, Cursor, Trae, and OpenCode, then configures supported plugin, MCP, Hook, and CLI integrations.
• Session trace inspection: parses Claude Code, Codex, and Trae sessions to show OpenViking recall, prompt injection, MCP calls, capture, and commit events.
• Local memory and skill management: views local memory / rule files and SKILL.md skills, then syncs them to OpenViking.

Download:

• macOS Apple Silicon (arm64)
• macOS Intel (x64)
• Windows (x64)