DSH Marketplace

OpenViking Memory Plugin

volcengine/OpenViking#examples/dsh-memory-plugin

OpenViking memory and context bundle for DeepSeek Harness: pre-step auto-recall and profile injection, session capture, `viking://` URI guarding, and recall/write memory tools backed by an OpenViking server.

38,9763,054PythonAGPL-3.0Source

Install

Add dsh-memory-plugin to DeepSeek Harness

via npm

Resolves a published tarball rather than cloning the repository, and installs without any extra setup. Swap `web` for your profile name if you run another one.

What happened when we ran it

Installed cleanly when we ran it

Every command here is run in a throwaway container against a clean profile, and the result is whatever the harness recorded — not a guess from the source. Last run 24d ago.

Show it in your README

install verified — dshmarketplace

For maintainers: the badge serves this listing's latest sandbox verdict, so a re-run updates it on its own — and it links readers to the full result here.

Due diligence

Before you install dsh-memory-plugin

  • Source of record: volcengine/OpenViking — present in the community registry that DSH's own plugin market installs from.
  • Licensed under AGPL-3.0.
  • Detected: terminal surface. Read the source before granting these.
  • A listing here is not a security review. Plugins run with your agent's permissions.

The AI take

What it is — dsh-memory-plugin is an OpenViking memory and context bundle plugin.

Who it is for — When you use the DSH plugin system, people who need OpenViking memory and context bundle are suitable to install dsh-memory-plugin. For people who do not need OpenViking as context database, they can use other plugins.

Watch out — Sandbox test passed, installed in a fresh profile and registered into profile by harness. It will execute shell commands. No obvious pitfalls found.

The verdict — I would install it because it can be registered by harness after installation in a fresh profile.

Generated by grok-4.6, and a starting point rather than a verdict. Where it says a plugin installs or does not, that is from a real run in a clean profile — everything else is read off the repository. Trust the source over this.

What dsh-memory-plugin does

Session fragments pass through a guarded filesystem and converge into layered memory blocks.

dsh-memory-plugin is a DeepSeek Harness plugin that connects an agent to an OpenViking server for persistent memory and context retrieval. It adds pre-step processing that automatically recalls relevant context and injects a user profile, captures session data, guards viking:// URIs, and exposes memory recall and write tools.

OpenViking stores memories, resources and skills in a virtual viking:// filesystem. Its retrieval model can browse with ls, tree and find, inspect directory-level L0 abstracts and L1 overviews, then load L2 details when needed. After a session is committed, OpenViking can asynchronously extract user preferences and agent experience into long-term memory. The plugin therefore reads context from the OpenViking server and may write captured session information and extracted memories there.

dsh-memory-plugin is intended for agents that need cross-session user memory or OpenViking’s filesystem-style context model. It is not a self-contained memory store: an OpenViking server is required, and the README excerpt does not document plugin-specific configuration or setup values. The plugin also has a terminal surface, so it runs with the agent’s available terminal permissions; this matters when installing it in an agent that can access local files or execute commands.

dsh-memory-plugin documentation

How it behaves

The plugin integrates OpenViking at the DeepSeek Harness pre-step stage. Before a step runs, it recalls relevant context and injects a profile. It also captures session activity and provides tools for recalling and writing memory. The upstream summary identifies URI guarding for viking:// resources.

OpenViking represents memories, resources and skills as paths below viking://. Agents can browse this context with ls, tree and find. Retrieval starts with directory context and can move through three content layers:

  • L0: a short abstract for relevance checks
  • L1: an overview for planning
  • L2: the full content, loaded when needed

After a session commits, OpenViking asynchronously extracts user preferences and agent experience into long-term memory.

Data and server boundary

The plugin reads recalled context and profile information from an OpenViking server. Its session-capture and memory-write paths can send session information and memories back to that server. OpenViking organises data under viking://, including user memories, resources and skills. The plugin has a terminal surface and therefore should be evaluated with the terminal permissions available to the agent.

Requirements

An OpenViking server is required as the backend for the recall and write tools. The provided README excerpt does not specify the plugin’s configuration keys, default values, environment variables, server endpoint format, commands for starting the server, or supported platform and version requirements.

Written from the project's own documentation and kept in sync with it. Where the two disagree, the source is authoritative — read the README on GitHub

Same category

Alternatives to dsh-memory-plugin

coding-agents

vectorize-io

41k

Hindsight, agent memory that learns: long-term project memory with auto recall and retain, knowledge pages, deep reflection, and per-repo memory banks.

No one-line install — this plugin lives inside a larger repository and publishes no npm package.

GitHub sourcePython2d ago

AI review

coding-agents

What it is — Hindsight provides long-term project memory management for coding agents.

Who it is for — Developers using AI coding assistants such as Claude Code for code development should install it. If you use a non-Python language for the agent, you can choose other implementations.

Watch out — The plugin requires an API key or token and will execute shell commands. Sandbox testing has not been performed. No obvious pitfalls found.

The verdict — Without sandbox testing results, I would not install it.

Generated by grok-4.6, and a starting point rather than a verdict. Where it says a plugin installs or does not, that is from a real run in a clean profile — everything else is read off the repository. Trust the source over this.Read the source
2Details

typescript

agentscope-ai

3.5k

Connects DeepSeek Harness to ReMe's local-first, self-evolving personal knowledge base: automatically captures completed main-agent conversations as user-owned Markdown memory, searches conversations and source material through reme_search with BM25, optional embeddings, and wikilink expansion, and schedules daily memory consolidation.

No one-line install — this plugin lives inside a larger repository and publishes no npm package.

GitHub sourcePython15d ago

AI review

typescript

What it is — The plugin integrates DeepSeek Harness with ReMe's knowledge base to automatically capture main-agent conversations as user Markdown memory and schedule consolidation.

Who it is for — When managing main-agent conversations with DeepSeek Harness and wanting to capture completed dialogues as user-owned Markdown memory, this plugin provides integration. For users who do not want to share the workspace with ReMe, independent knowledge base tools offer a different perspective.

Watch out — The plugin requires manually allowing the build script when installing from source. Sandbox testing has not been completed. The plugin executes shell commands.

The verdict — If I am willing to manually allow the build script from source, I would install it because it connects conversations to persistent Markdown memory.

Generated by grok-4.6, and a starting point rather than a verdict. Where it says a plugin installs or does not, that is from a real run in a clean profile — everything else is read off the repository. Trust the source over this.Read the source
1Source

ReMe

agentscope-ai

3.4k

Connects DeepSeek Harness to ReMe's local-first, self-evolving personal knowledge base: automatically captures completed main-agent conversations as user-owned Markdown memory, searches conversations and source material through reme_search with BM25, optional embeddings, and wikilink expansion, and schedules daily memory consolidation.

No one-line install — this plugin lives inside a larger repository and publishes no npm package.

GitHub sourcePython20d ago

AI review

ReMe

What it is — Integrates ReMe's local-first self-evolving personal knowledge base with DeepSeek Harness.

Who it is for — When running agents using DeepSeek Harness that require automatic capture of conversations as Markdown memory, this is suitable. If you do not need shared local workspace across agents, you can skip this plugin.

Watch out — No obvious pitfalls found. The plugin will execute shell commands.

The verdict — I would install it, but only if shell command execution is permitted.

Generated by grok-4.6, and a starting point rather than a verdict. Where it says a plugin installs or does not, that is from a real run in a clean profile — everything else is read off the repository. Trust the source over this.Read the source
1Source

MemSearch

zilliztech

2.6k

Shared Markdown memory for DSH and other coding agents, with automatic capture, pre-step context injection, searchable recall, and memory-to-skill self-evolution through a review panel.

No one-line install — this plugin lives inside a larger repository and publishes no npm package.

GitHub sourcePython19d ago

AI review

MemSearch

What it is — MemSearch provides shared Markdown memory for DeepSeek Harness coding agents. It supports automatic capture, pre-step context injection, searchable recall, and memory-to-skill self-evolution through a review panel.

Who it is for — When building complex coding agents with DeepSeek Harness that require persistent context across sessions, MemSearch offers value. If your work involves only short-lived conversations without cross-platform memory needs, you can skip it.

Watch out — It will execute shell commands. Sandbox testing has not been done yet; no obvious issues were identified.

The verdict — I would install it because it enables memory to evolve into reusable skills, but only if you are willing to use it via subdirectory installation and accept shell command execution as a trade-off.

Generated by grok-4.6, and a starting point rather than a verdict. Where it says a plugin installs or does not, that is from a real run in a clean profile — everything else is read off the repository. Trust the source over this.Read the source
1Source

deja-vu

vshulcz

975

Memory for coding agents — Claude Code, Codex, Cursor and 17 others. Indexes the sessions they already wrote to disk, including months from before you installed it, and recalls them in any of them. No LLM, no embeddings, one local Go binary.

Installed cleanly when we ran it

GitHub sourceGo6d ago

AI review

deja-vu

What it is — Indexes the sessions coding agents have written to disk for recall in Claude Code and others.

Who it is for — Users working in Claude Code who need to recall solutions from months ago. Users whose work does not involve multi-session coding workflows also do not need it.

Watch out — Requires manual approval of the build script when installing from source. Sandbox test passed. No obvious issues found.

The verdict — I would install it because it recalls existing session history from disk without needing additional model calls.

Generated by grok-4.6, and a starting point rather than a verdict. Where it says a plugin installs or does not, that is from a real run in a clean profile — everything else is read off the repository. Trust the source over this.Read the source
2Source

deja-vu

vshulcz

821

Reads the session files thirty-three other coding agents on this machine already wrote — Claude Code, Codex, Cursor, VS Code Copilot Chat, opencode, OpenClaw, Hermes, Kimi, Cline, Zed and more — including sessions from before it was installed: six tools (deja_recall, deja_session, deja_blame, deja_fix, deja_how, deja_remember), a /deja command, and optional automatic recall added to the runtime context. Local BM25 index, no LLM, no embeddings, no network (dsh plugin --profile web add dsh-deja).

Installed cleanly when we ran it

npm packageGo16d ago

AI review

deja-vu

What it is — deja-vu indexes session files from other coding agents to provide memory recall for DeepSeek Harness.

Who it is for — When using DeepSeek Harness with agents like Claude Code for code debugging tasks referencing historical solutions, users missing cross-agent recall benefit from it. If your workflow is limited to a single local agent with no cross-agent session sharing, this plugin is irrelevant.

Watch out — Sandbox testing passed in a new profile with registration in DeepSeek Harness. Index build takes about ten seconds. No obvious issues found.

The verdict — I would install it because it uses local BM25 index with no LLM, no embeddings, and no network.

Generated by grok-4.6, and a starting point rather than a verdict. Where it says a plugin installs or does not, that is from a real run in a clean profile — everything else is read off the repository. Trust the source over this.Read the source
1Source