RAG

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:

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

Required: Volcengine/Ark API Keys

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

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

Files

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