Openviking Memory logo

Openviking Memory

OrganizationPopular
volcengine
openviking-memory

Recall and persist long-term memory through the OpenViking MCP tools. Use at the start of any substantive task (coding, configuration, debugging, multi-step or tool-based work) to retrieve relevant prior knowledge with find/search/read, and during or after work to persist durable facts, preferences, decisions, and lessons with remember. Do not use for casual chat or simple factual questions the model can answer directly.

Overview

Publishervolcengine
RepositoryOpenViking
Skill nameopenviking-memory
Stars
37.9K
Forks
2.9K
Bundled files
1
LicenseAGPL-3.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by volcengine on GitHub. Read the source before you install it.

Installation

Install the Openviking Memory AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/volcengine/OpenViking.git /tmp/OpenViking
mkdir -p .claude/skills
cp -r /tmp/OpenViking/agent-plugins/skills/openviking-memory .claude/skills/openviking-memory
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Openviking Memory in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Openviking Memory on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Openviking Memory is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

OpenViking Memory

OpenViking is a long-term semantic memory store addressed by viking:// URIs. This client has no lifecycle hooks, so nothing is recalled or captured automatically — you drive both halves of the loop with the openviking MCP tools.

Core tools, available on every supported deployment:

  • Recall: find, search, read, list, grep, glob
  • Persist: remember, add_resource
  • Maintain: forget, health

Some deployments register more than the core set — tree, write, edit, list_watches, cancel_watch. These are optional: which ones exist depends on the server version and hosting mode (the managed cloud service trims some of them). Check the session's registered tool list; if any optional tool is present, read references/optional-tools.md before using it. Never call a tool that is not registered, and do not fall back to raw HTTP. If no OpenViking tools are registered at all, continue without memory.

Recall: at task start

  1. Decide whether the request warrants memory. Retrieve for executable or multi-step work, anything touching a system you may have seen before, and recovery from failures. Skip retrieval for small talk and one-off trivia.
  2. Build one concise query from the task goal, domain objects, intended operation, and constraints. After a failure, include the failed operation and the stable part of the error message.
  3. Call find (fast, ranked results with URI + abstract + score) with limit around 5-10. Use search when deeper intent analysis helps, or use search with mode="context" for a server-assembled, token-budgeted context block. In list mode, scope with target_uri when you know where to look, e.g. viking://~/memories/experiences for prior task experience. viking://~ is the home alias for your own user root; a server that predates the alias rejects every viking://~ URI with INVALID_URI. Against such a server use the explicit viking://user/<user_id>/... root taken from a URI already visible in this session, or drop target_uri and keep the hits whose URI contains /memories/experiences/. Never guess a user ID.
  4. Judge results by task and environment fit, not title similarity. read the one to three exact file URIs likely to change how you execute. Ignore sidecar files such as .abstract.md, .overview.md, and .relations.json.
  5. If nothing relevant comes back, proceed without memory. Make at most one focused follow-up search when execution fails for a materially new reason.

Treat retrieved memory as advisory. Priority order: system and developer instructions, the current user request, current environment and tool evidence, then memory. Verify commands, paths, and versions against the present task; prior success never authorizes a destructive action now.

Persist: during and after work

Because capture is not automatic here, durable information is lost unless you store it. When you encounter something worth keeping, persist it in the same session:

  • remember(messages) — the default. Pass the key exchange or a short factual summary as role-tagged messages; the server extracts and files memories (preferences, entities, events, experience) on its own. Use it when the user says "remember this", states a lasting preference or decision, or when a hard-won lesson (root cause, working procedure, environment quirk) emerges.
  • add_resource — to import external documents or URLs as searchable resources.
  • When you need an exact document at a known location (curated notes under viking://~/ — your own user root — or shared reference material under viking://resources/), the optional write / edit tools cover that — see references/optional-tools.md. If they are not registered, fall back to remember.

What to persist: stable preferences and conventions, environment facts, decisions with their rationale, and reusable procedures or fixes. What not to persist: secrets and credentials, transient state, speculation, or bulk transcript dumps — store conclusions, not scrollback.

Example

User asks to fix a failing deployment:

  1. find with query deployment image pull failure private registry, target_uri: "viking://~/memories/experiences".
  2. read the most relevant experience URI; check its assumptions against the current cluster before applying its steps.
  3. Fix the issue, verify the live result.
  4. remember a short summary of the root cause and the working fix so the next session can recall it.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Openviking Memory AI skill do?

Recall and persist long-term memory through the OpenViking MCP tools. Use at the start of any substantive task (coding, configuration, debugging, multi-step or tool-based work) to retrieve relevant prior knowledge with find/search/read, and during or after work to persist durable facts, preferences, decisions, and lessons with remember. Do not use for casual chat or simple factual questions the model can answer directly.

Why use Openviking Memory on TypingMind?

Because you install it once and use it with any model. Openviking Memory is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Openviking Memory in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/volcengine/OpenViking/tree/main/agent-plugins/skills/openviking-memory. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Openviking Memory?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Openviking Memory?

As many as you like. As long as a model supports skills, you can use Openviking Memory with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Openviking Memory AI skill free?

Yes. It is published on GitHub by volcengine under the AGPL-3.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇