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

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