DeepSeek Harness Plugins
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dsh-semantic-memory

Persistent semantic long-term memory plugin for DeepSeek Harness.

Developer ToolsRemote Execution
0GitHub Stars0ForksUpdated2026-08-17

Install

$ pnpm dsh plugin --profile web add dsh-plugin-semantic-memory

Plugin Overview & Capabilities

AI-assisted organization based on the public repository snapshot. The content must be grounded in source evidence and does not replace compatibility or security verification.

source-grounded
A Cordis/DeepSeek Harness plugin that stores cross-session memories in JSONL and retrieves them by embedding similarity. It adds memory tools, prompt-time recall, workspace-aware filtering, and optional automatic conversation summarization.

Key Capabilities

  • Persists facts, decisions, preferences, and notes across sessions.
  • Performs embedding-based semantic retrieval using local ONNX embeddings or an OpenAI-compatible embeddings API.
  • Provides memory_write, memory_search, memory_forget, and memory_stats model-facing tools.
  • Injects recalled or fallback memories into the system prompt.
  • Tags memories with workspace context and supports workspace-scoped recall.
  • Applies memory importance, access refreshing, and time-based strength decay.
  • Can summarize recent conversation messages into automatically tagged memory entries.

Useful For

  • Remember user preferences and durable project decisions between DeepSeek Harness sessions.
  • Retrieve relevant prior context by meaning rather than literal full-text search.
  • Maintain workspace-specific notes while allowing optional cross-workspace recall.
  • Let an agent automatically preserve salient conversation facts and decisions.

Who It Fits

  • DeepSeek Harness users who need persistent personal or project memory.
  • Agent workflow builders using Cordis-based DSH profiles.
  • Users who want local embedding retrieval or OpenAI-compatible embedding endpoints.

Documented Limitations

  • The first local embedding downloads an approximately 100 MB model.
  • Recall is asynchronous and may arrive one turn late during cold starts or slow API responses.
  • A new process may have empty prompt injection before the memory store is lazily loaded.
  • Search is brute-force O(n) cosine similarity and is described as suitable for personal-scale stores rather than millions of entries.
  • Auto-summarization requires llm and agentDefaultModel services and is silent on failure.
  • For file: dependencies, local patch or build changes require reinstalling the profile package and restarting.

DSH Compatibility

Version-specific runtime evidence collected by DSH Plugin. A missing result means we have not tested that combination yet.

Not tested yetNo runtime compatibility tests have been published yet.

Security Signals

Objective signals discovered from package metadata and source inspection. These are not a guarantee that a plugin is safe.

Dependency Counts

package.json declares 1 runtime, 5 development, 3 peer, and 0 optional dependencies.

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Dsh Bundle Declared

package.json declares DSH bundle metadata.

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License Declared

GitHub reports the repository license as Apache-2.0.

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Package Manifest Available

A root package.json was captured and can be inspected by the registry.

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Source Available

Public GitHub source metadata is available for this registry snapshot.

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Source & Registry Notes

Public provenance, Registry classification, and the latest source check for this entry, kept separate from runtime verification.

Source repository
chenkezhen480/dsh-semantic-memory
Registry source
GitHub · dsh-plugin topic
Registry classification
Plugin
Source checked
0ab4575 · 2026-09-15

This project is independently indexed from public source information. DSH Plugin is not affiliated with DeepSeek or the plugin author. Always check the author repository before installation.

Repository Activity

GitHub stars
0GitHub stars
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0
Open issues
0
Last commit
2026-08-17
Last release
No release detected
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