OpenViking: The Context Database for AI Agents

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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.

• 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 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.

• User memory (LoCoMo): with OpenViking, all three agent integrations land at 80–83% accuracy — up from 24–57% on their native memory — while input tokens drop by 34.3–91.0% and query latency by 58.45–66.10%.
• Agent experience (tau2-bench): experience memory lifts task success by +6.87pp (retail) and +11.87pp (airline) over the same LLM without memory.

💡 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:

ov status
ov add-resource https://github.com/volcengine/OpenViking # --wait
ov ls viking://resources/
ov tree viking://resources/volcengine -L 2
# wait some time for semantic processing if not --wait
ov find "what is openviking"
ov grep "openviking" --uri viking://resources/volcengine/OpenViking/docs/en

Next steps:

• Client configuration (ov config), standalone CLI installs (npm / cargo), and advanced usage such as index rebuilding: CLI setup
• Docker and production deployment: Deployment guide

Integrations inject OpenViking recall into your agent's context and auto-commit session memory:

• Claude Code
• Codex
• OpenClaw
• Hermes
• Cursor
• Trae
• OpenCode
• pi
• MCP clients
• LangChain / LangGraph

Setup instructions for each agent: Agent integrations overview.

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)

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.

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

The open-source edition is not crippled. OpenViking in this repo is fully open source under AGPLv3: no feature gates, no account required, no activation key. Follow Deploy in production above and run it in production yourself — and that will stay true.

The two editions below answer "who operates it and where it runs", not "can I use it".

☁️ Managed SaaS Officially hosted on Volcano Engine. Nothing to set up, nothing to operate. Personal — for individual developers. Free trial for up to 50 files, and scales far beyond local hardware with VikingDB. Enterprise — multi-user context management, team collaboration and permissions, enterprise SLA and support. Existing open-source users can move over with the migration tool. → Volcano Engine product page · Documentation Global hosting for regions outside China is coming to BytePlus.

🏢 Self-Managed Runs inside your own environment. Data never leaves it. Online — deployed into your own cloud account / VPC, BYOC supported, with outbound access for updates and licensing. Offline — fully air-gapped environments with no internet access, for regulated industries. Adds distributed deployment and official support on top of the open-source edition, activated by license key. → Talk to us about self-managed deployment

Just want to run the open-source edition? Go ahead — you don't need to contact anyone. Head to Quick start.

OpenViking open-sources a subset of the core capabilities described in the VikingMem paper:

VikingMem: A Memory Base Management System for Stateful LLM-based Applications Jiajie Fu, Junwen Chen, Mengzhao Wang, Aoxiang He, Maojia Sheng, Xiangyu Ke, Yifan Zhu, and Yunjun Gao. arXiv:2605.29640, 2026. Accepted by VLDB 2026. 📄 Read the paper on arXiv

OpenViking welcomes collaboration with other open-source projects to build the context data ecosystem. Our confirmed partners include:

• deer-flow - Open-source long-horizon SuperAgent harness
• NoKV - AI native distributed file system
• loopx - Lightweight loop engineering state kernel
• Hermes Agent - The agent that grows with you

Interested in joining our partner list? Please submit an issue to our community to apply.

OpenViking is still in its early stages, and there is plenty left to build.

• Docs: docs.openviking.ai · FAQ
• Blog: blog.openviking.ai
• Team: About us
• Chat: 📱 Lark Group · 💬 WeChat · 🎮 Discord · 🐦 X
• Contribute: bug fixes and new features are both welcome — see CONTRIBUTING.md

This project takes security seriously. For vulnerability reporting and supported versions, see SECURITY.md

The OpenViking project uses different licenses for different components:

• Main Project: AGPLv3 - see the LICENSE file for details
• crates/ov_cli: Apache 2.0 - see the LICENSE for details
• examples: Apache 2.0 - see the LICENSE for details
• third_party: Respective original licenses of third-party projects