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dsh-layered-memory

Layered long-term memory plugin for DeepSeek Harness with automatic distillation (L0-L3) and contextual recall.

UI & ProductivityDeveloper ToolsSkills & Workflows
20GitHub Stars5ForksUpdated2026-09-07

Install

$ dsh plugin --profile web add dsh-layered-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
dsh-layered-memory brings hierarchical long-term memory (L0 conversation capture -> L1 atomic memories -> L2 scene blocks -> L3 user persona distillation) to DeepSeek Harness. It automatically recalls and injects relevant context before agent steps, supports BM25 keyword search as well as local/remote vector embeddings, and provides a full memory workbench UI.

Key Capabilities

  • Automatic L0-L3 conversation capture and background distillation pipeline
  • Pre-step synthetic message recall injection and system prompt persona integration
  • Hybrid retrieval combining BM25 full-text search with optional local (ONNX CPU) or remote vector embeddings
  • Session-level memory scope gears (Auto, Work, Chat, Read-Only, Paused)
  • Built-in agent memory tools: memory_search, conversation_search, and memory_read_scene
  • Interactive Memory Workbench UI in settings with cost analytics and asset maintenance

Useful For

  • Maintaining user preferences, project background, and team methodologies across distinct sessions
  • Reducing token consumption and exploratory steps in long, multi-step engineering tasks
  • Separating personal and work contexts automatically without manual tagging
  • Enabling local-first, privacy-conscious semantic memory indexing without external vector DB dependencies

Who It Fits

  • DeepSeek Harness web profile users seeking persistent cross-session memory
  • Developers working on multi-session software projects requiring continuous context
  • Power users desiring fine-grained control over agent recall scopes and LLM distillation budgets

Documented Limitations

  • Requires DeepSeek Harness host version >= 0.1.2-alpha.1 and Node.js >= 22.16.0
  • Vector embedding capabilities require either configuring remote embedding endpoints or downloading local ONNX models into the data directory
  • Distillation consumes background LLM tokens using the host ctx.llm or configured layer routes

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 4 runtime, 15 development, 8 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 MIT.

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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
JunNanLYS/dsh-layered-memory
Registry source
GitHub · dsh-plugin topic
Registry classification
Plugin
Source checked
69028fb · 2026-10-12

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
20GitHub stars
Forks
5
Open issues
9
Last commit
2026-09-07
Last release
2026-09-07
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