How do I install the Openviking skill?

Run npx skillmds@latest add dvcrn/openviking in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Openviking safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: executes scripts, makes network calls, reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Openviking?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Openviking free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Openviking?

dvcrn (@dvcrn) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Openviking by dvcrn — Agent Skill

Name: Openviking
Rating: 4.2 (5 reviews)
Author: dvcrn

Openviking

RAG and semantic search via OpenViking Context Database MCP server. Query documents, search knowledge base, add files/URLs to vector memory. Use for document Q&A, knowledge management, AI agent memory, file search, semantic retrieval. Triggers on "openviking", "search documents", "semantic search", "knowledge base", "vector database", "RAG", "query pdf", "document query", "add resource".

dvcrn Updated Sep 8, 2026 32 repo stars

File contents

OpenViking - Context Database for AI Agents

OpenViking is ByteDance's open-source Context Database designed for AI Agents — a next-generation RAG system that replaces flat vector storage with a filesystem paradigm for managing memories, resources, and skills.

Key Features:

Filesystem paradigm: Organize context like files with URIs (viking://resources/...)
Tiered context (L0/L1/L2): Abstract → Overview → Full content, loaded on demand
Directory recursive retrieval: Better accuracy than flat vector search
MCP server included: Full RAG pipeline via Model Context Protocol

Quick Check: Is It Set Up?

test -f ~/code/openviking/examples/mcp-query/ov.conf && echo "Ready" || echo "Needs setup"
curl -s http://localhost:2033/mcp && echo "Running" || echo "Not running"

If Not Set Up → Initialize

Run the init script (one-time):

bash ~/.openclaw/skills/openviking-mcp/scripts/init.sh

This will:

Clone OpenViking from https://github.com/volcengine/OpenViking
Install dependencies with uv sync
Create ov.conf template
Pause for you to add API keys (embedding.dense.api_key, vlm.api_key)

Required: Volcengine/Ark API Keys

Config Key	Purpose
`embedding.dense.api_key`	Semantic search embeddings
`vlm.api_key`	LLM for answer generation

Get keys from: https://console.volcengine.com/ark

Start the Server

cd ~/code/openviking/examples/mcp-query
uv run server.py

Options:

--port 2033 - Listen port
--host 127.0.0.1 - Bind address
--data ./data - Data directory

Server will be at: http://127.0.0.1:2033/mcp

Connect to Claude

claude mcp add --transport http openviking http://localhost:2033/mcp

Or add to ~/.mcp.json:

{
  "mcpServers": {
    "openviking": {
      "type": "http",
      "url": "http://localhost:2033/mcp"
    }
  }
}

Tools Available

Tool	Description
`query`	Full RAG pipeline — search + LLM answer
`search`	Semantic search only, returns docs
`add_resource`	Add files, directories, or URLs

Example Usage

Once connected via MCP:

"Query: What is OpenViking?"
"Search: machine learning papers"
"Add https://example.com/article to knowledge base"
"Add ~/documents/report.pdf"

Troubleshooting

Issue	Fix
Port in use	`uv run server.py --port 2034`
Auth errors	Check API keys in ov.conf
Server not found	Ensure it's running: `curl localhost:2033/mcp`

Files

ov.conf - Configuration (API keys, models)
data/ - Vector database storage
server.py - MCP server implementation

---name: openvikingdescription: RAG and semantic search via OpenViking Context Database MCP server. Query documents, search knowledge base, add files/URLs to vector memory. Use for document Q&A, knowledge management, AI agent memory, file search, semantic retrieval. Triggers on "openviking", "search documents", "semantic search", "knowledge base", "vector database", "RAG", "query pdf", "document query", "add resource".---# OpenViking - Context Database for AI AgentsOpenViking is ByteDance's open-source **Context Database** designed for AI Agents — a next-generation RAG system that replaces flat vector storage with a filesystem paradigm for managing memories, resources, and skills.**Key Features:**- **Filesystem paradigm**: Organize context like files with URIs (`viking://resources/...`)- **Tiered context (L0/L1/L2)**: Abstract → Overview → Full content, loaded on demand- **Directory recursive retrieval**: Better accuracy than flat vector search- **MCP server included**: Full RAG pipeline via Model Context Protocol---## Quick Check: Is It Set Up?```bashtest -f ~/code/openviking/examples/mcp-query/ov.conf && echo "Ready" || echo "Needs setup"curl -s http://localhost:2033/mcp && echo "Running" || echo "Not running"```## If Not Set Up → InitializeRun the init script (one-time):```bashbash ~/.openclaw/skills/openviking-mcp/scripts/init.sh```This will:1. Clone OpenViking from `https://github.com/volcengine/OpenViking`2. Install dependencies with `uv sync`3. Create `ov.conf` template4. **Pause for you to add API keys** (embedding.dense.api_key, vlm.api_key)**Required: Volcengine/Ark API Keys**| Config Key | Purpose ||------------|---------|| `embedding.dense.api_key` | Semantic search embeddings || `vlm.api_key` | LLM for answer generation |Get keys from: https://console.volcengine.com/ark## Start the Server```bashcd ~/code/openviking/examples/mcp-queryuv run server.py```Options:- `--port 2033` - Listen port- `--host 127.0.0.1` - Bind address- `--data ./data` - Data directoryServer will be at: `http://127.0.0.1:2033/mcp`## Connect to Claude```bashclaude mcp add --transport http openviking http://localhost:2033/mcp```Or add to `~/.mcp.json`:```json{ "mcpServers": { "openviking": { "type": "http", "url": "http://localhost:2033/mcp" } }}```## Tools Available| Tool | Description ||------|-------------|| `query` | Full RAG pipeline — search + LLM answer || `search` | Semantic search only, returns docs || `add_resource` | Add files, directories, or URLs |## Example UsageOnce connected via MCP:```"Query: What is OpenViking?""Search: machine learning papers""Add https://example.com/article to knowledge base""Add ~/documents/report.pdf"```## Troubleshooting| Issue | Fix ||-------|-----|| Port in use | `uv run server.py --port 2034` || Auth errors | Check API keys in ov.conf || Server not found | Ensure it's running: `curl localhost:2033/mcp` |## Files- `ov.conf` - Configuration (API keys, models)- `data/` - Vector database storage- `server.py` - MCP server implementation

Frequently asked questions