dsh-echo-memory
Run the following command in DeepSeek Harness:
dsh plugin install JxaMe/dsh-echo-memory
Paste the following prompt into your AI chat to install this plugin:
Open a new session in DeepSeek Harness, paste the source URL https://github.com/JxaMe/dsh-echo-memory to the agent, and it will walk you through installation and verification.
About this plugin
dsh-echo-memory tackles a very concrete pain point: every new session, the agent has no memory of your preferences, project constraints, or decisions you already agreed on, so you end up re-explaining yourself. This plugin lets the agent naturally capture that information during conversation and automatically recall it in later sessions. Only when relevant, one or two items at a time, no context flooding.
The design is deliberately minimal. All memories live in a single local memory.json file that is human-readable and atomically written. Retrieval uses BM25F scoring with title and tag weighting; ten thousand entries are searched in single-digit milliseconds. No vector database, no external API key, no local database required. On the interaction side, a draggable floating dot in the bottom-right corner lets you drag selected web text directly into memory; AI-suggested items pop up as confirmation cards; and a tombstone recycling bin guards against accidental deletions.
This plugin fits DSH users who want their agent to behave like a partner with persistent memory, but do not want to hand that memory to any external service. You prefer a purely local, auditable data model where you can open the JSON file and read it at any time. You switch between multiple projects and want project-scoped memories to stay isolated while global preferences remain shared across them.
Screenshots
Use Cases
- Remember project constraints and preferences across sessions with automatic recall
- Say "remember this" in plain conversation to save; relevant memories surface on demand
- Isolate per-project memories while sharing global preferences across workspaces
Best For
- DSH users who want a memoryful agent and are tired of re-explaining context every session
- Developers who prefer pure local JSON with no vector DB or external service
- Engineers working across multiple projects who need clean memory isolation
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