volcengine/OpenViking

What is OpenViking

OpenViking is an open-source context database for AI agents — one filesystem for everything an agent knows: knowledge, memory, and skills.

Most agent memory is a black box: text goes in, embeddings come out, and nobody can see what was actually stored. OpenViking organizes context as a virtual filesystem under viking:// instead. Agents navigate it like files — ls, tree, read, write, grep — and you can open any directory to inspect and edit what your agent knows. Every directory carries a generated summary, so agents can scan summaries first and decide what to read.

Try OpenViking Studio in your browser, no installation required. Self-host Web Studio.

Why OpenViking

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:

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

Proof it works

OpenViking 0.3.22 has been evaluated on long-conversation user memory (LoCoMo) and multi-turn agent tasks (tau2-bench). Full results and setup details, including knowledge-base QA, are in the benchmark report; reproduction scripts live in ./benchmark.

To run your own experiments, follow the RAG and Memory evaluation guide for routine VikingBot memory evaluation, published LoCoMo reproduction, separate mem0 prompt comparisons, and RAG evaluation.

The memory evaluation used Doubao 2.0 Pro as the VLM and Doubao-embedding-vision-251215 as the embedding model.

Quick start

Set up an OpenViking server first; if you already have one, skip to Use it with your agent. Deploying your own needs uv, Python 3.10+ and a model provider with an embedding model and a VLM.

Follow this guide to install and start an OpenViking Server for me:
https://docs.openviking.ai/en/getting-started/04-setup-for-agent
Ask me for the model provider, the models, the workspace directory and whether
other machines need to reach the server; don't guess. When you ask for the model
API key, tell me how to hand it over if I'd rather not paste it into this chat,
and never repeat it back.
When it's running, tell me the server's address and whether auth is turned on.

uv tool install openviking --upgrade && openviking-server init

Explore with the CLI

The openviking package includes the ov CLI. With the server running, import a repository and search it:

ov status
ov add-resource https://github.com/volcengine/OpenViking
Replace TASK_ID with the returned task_id; repeat until status is completed
ov task status TASK_ID
ov ls viking://resources/
ov tree viking://resources/volcengine -L 2
ov find "what is openviking"
ov grep "openviking" --uri viking://resources/volcengine/OpenViking/docs/en

ov find returns matching context with URIs you can inspect. For client configuration (ov config), standalone CLI installs, and index maintenance, see CLI setup.

Build your own integration with the Python, Go, or TypeScript SDK, or the HTTP API.

Use it with your agent

Connect your coding agent to OpenViking for cross-session memory. The memory plugin installer covers Claude Code, Codex, Cursor, TRAE, OpenCode and more, and detects which ones you have.

Install the OpenViking memory plugin and help me start using it. OpenViking is the open-source context database for AI agents from Volcengine (https://github.com/volcengine/OpenViking, AGPL-3.0); the command and link below are from its official docs.
1. Run the official installer. It needs network access and writes to my home directory; if your sandbox blocks that, ask me for approval:
   curl -fsSL https://openviking.ai/install | bash -s -- --yes --lang en --url <SERVER_URL> --api-key <API_KEY>
2. Once it's installed, read the OpenViking onboarding guide and use it to walk me through setup. The guide has you ask me before choices like importing history or adding a working rule. docs.openviking.ai and docs.openviking.net are the same docs site; use whichever loads:
   https://docs.openviking.ai/agents/en/onboarding.md
The API key is a user key I created for myself on that server; the installer stores it in ~/.openviking/ovcli.conf on this machine. Use it in the command as it is; there's no need to show it in your replies, and don't write it into any other file or rule. Talk to me in English.

curl -fsSL https://openviking.ai/install | bash
AI agents: not sure about running this? The script's header says what it does and how to verify it.

Then restart your agent. The installer ends with the next steps for each tool, for example:

  Claude Code
    Next: restart Claude Code
    ⋮
    Verify: run /openviking-memory:ov in a session

On its first start Codex stops at Hooks need review; choose Trust all and continue.

Try it: ask it to remember one of your preferences, then ask about it in a new session a little later. Memories are processed in the background, so it's normal not to find one right after you say it.

The installer needs macOS or Linux, Node.js 18+ and curl, no sudo. On Windows, use the desktop app.

Setup guides for each integration:

Claude Hooks + MCP	Codex Hooks + MCP	Cursor Hooks + MCP	TRAE Hooks + MCP	OpenClaw Context engine	Hermes Built-in
OpenCode Plugin + MCP	pi Native extension	DeerFlow Plugin + MCP	DSH Plugin + MCP	Doubao Work Connector	LangChain Tools + store

Agent Plugins 1.0

MCP clients

For setup instructions and integration details, see Integrations.

For framework applications, see the LangChain/LangGraph and Microsoft Agent Framework packages, with a guide to adding other adapters.

Desktop App (Beta)

The desktop app is a console for macOS and Windows x64 (beta). It configures supported local agent integrations, inspects recall and capture events in sessions, and syncs local memories and skills to OpenViking.

Download:

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

VikingBot

VikingBot is an AI agent framework built on top of OpenViking:

pip install "openviking[bot]"
openviking-server --with-bot
ov chat   # in another terminal

The official Docker image bundles VikingBot and starts it by default alongside the server and console UI. Details: VikingBot guide.

Deploy in production

Run the open-source server in your own environment under AGPLv3. It requires no activation key. Start with server setup or the Docker and deployment guide.

The server supports accounts and user isolation and opt-in resource ACLs. Configure authentication before exposing it beyond localhost.

Commercial editions

☁️ Managed SaaS Volcano Engine hosts and operates OpenViking. Personal and Enterprise plans cover individual and team use, with migration tooling for open-source deployments. See the service documentation for plans and limits. Hosting outside China is planned on BytePlus.

🏢 Self-Managed Deploy in your own cloud account / VPC (BYOC) or an offline environment. This edition adds distributed deployment and official support, activated by a license key. Contact the team.

Research

Jiajie Fu, Junwen Chen, Mengzhao Wang, Aoxiang He, Maojia Sheng, Xiangyu Ke, Yifan Zhu, and Yunjun Gao.

arXiv:2605.29640, 2026. Presented at VLDB 2026 in September.

📄 Read the paper on arXiv · Read PDF

Mengzhao Wang, Zheng Gong, Jingpei Hu, Jiajie Fu, Maojia Sheng, Junwen Chen, and Yifan Zhu.

arXiv:2606.16903, 2026. Accepted by ICDE.

📄 Read the paper on arXiv · Read PDF