@agentscope-ai/reme-dsh-plugin

memorytested: passnpmApache-2.0 condition deviation

ReMe memory and context integration for DeepSeek Harness

📦 体积
2.91 MB · small
🔌 Network
undeclared
✅ Sandbox
pass
🏷️ Versions
3

Who this is for

  • anyone who wants the agent to remember long-running context
下载量 / Downloads
0
依赖体积 / node_modules
2.91 MB
测试时间 / Tested
2026-11-06
DSH 版本 / Version
0.2.2-rc.1

Install

dsh plugin --profile web add @agentscope-ai/reme-dsh-plugin

Sandbox test results

條件偏離 —— 本結果在 DSHIO/T 21 v0.9 條件下產生(內存 1g、CPU 1.5、T4 超時 60s,未記鏡像 digest)。偏離條款:5.1.3(内存 768MB)、5.1.3(CPU 1 核)、5.1.6(T4 超时 90s)、5.1.2(镜像 digest)。按 DSHIO/T 21 §8.2,該判定不得與標準條件結果比較,不得用作認證依據。
StageResult
T0 static check—
T1 install✓
T2 list✓
T3 mount✓
T4 boot—

Four stages run in an isolated sandbox: install (network on) → list → mount → boot (last three with no network).

README

ReMe plugin guide for DeepSeek Harness

[中文说明](./README_ZH.md)

This guide explains how to install, configure, and use @agentscope-ai/reme-dsh-plugin with DeepSeek Harness (DSH), including memory guidance injection, the reme_search tool, automatic memory, daily consolidation, and the ReMe Status page.

The screenshots record an earlier local integration test against DSH 0.1.5-rc.2; the supported releases are 0.1.7-rc.2, 0.2.0-rc.2, 0.2.1-alpha.2, 0.2.1-alpha.4, 0.2.1-rc.2, and 0.2.2-alpha.2. Both the interface and ReMe guidance are set to English, and the isolated DSH and ReMe workspaces contain only fictional Project Aurora data. No .env values, API keys, access tokens, or personal memories appear in the screenshots.

1. How the plugin works

When a DSH session starts, the plugin injects guidance that tells the root agent when and how to use long-term memory. It also registers the read-only reme_search tool. After a turn completes, the plugin can submit user and assistant messages to ReMe auto_memory; a daily schedule can run auto_dream to consolidate journal entries into durable personal knowledge.

New session
  └─ Inject long-term-memory guidance
       └─ Agent decides whether the request depends on history
            └─ reme_search → ReMe search → daily / digest files

Completed conversation
  └─ Automatic-memory batch → ReMe auto_memory → daily files
       └─ Scheduled consolidation → ReMe auto_dream → digest files

The DSH adapter injects usage guidance, not every historical memory. Relevant memories enter the conversation only when the agent calls reme_search. This keeps unrelated history out of the prompt and helps prevent historical content from being treated as new instructions.

2. Requirements

  • ReMe is installed and its configuration exposes the search, auto_memory, and auto_dream jobs.
  • DeepSeek Harness 0.1.7-rc.2, 0.2.0-rc.2, 0.2.1-alpha.2, 0.2.1-alpha.4, 0.2.1-rc.2, or 0.2.2-alpha.2.
  • Node.js ^22.19.0 or >=24.0.0, matching the current DSH engine range (DSH 0.2.2 needs Node 24.9 or newer).
  • The browser running DSH can reach the configured ReMe HTTP endpoint. Cross-machine deployments must also allow the DSH browser origin.

The default ReMe endpoint is http://127.0.0.1:2333. ReMe HTTP does not use API-key authentication, so do not expose it directly to an untrusted network.

Peer ranges cover the tested prerelease lines and compatible stable 0.1.x / 0.2.x releases. Other prerelease lines require validation before being added; 0.3.x is excluded.

3. Install and start

This package replaces the former @agentscope-ai/reme/dsh entry. Remove the combined package before installing the new

host-specific plugin.

3.1 Start ReMe

reme start workspace_dir=/absolute/path/to/your/reme-workspace \
  service.host=127.0.0.1 service.port=3457 \
  jobs.dream_cron.enabled=false

The plugin owns the daily Dream schedule by default, so this disables ReMe's dream_cron while keeping the

auto_dream API available. To use ReMe scheduling instead, omit this override and set the plugin's

autoDreamEnabled to false. Shared services should have only one scheduler.

For development and screenshots, use an isolated directory outside the repository, such as /tmp/reme-dsh-demo. Do not write runtime memory into the repository's .reme/ directory.

3.2 Install the DSH bundle

Install the published package. See its listings on

Awesome DSH Plugin and

npm:

dsh plugin --profile web add @agentscope-ai/reme-dsh-plugin

For local package development, pass the package directory to DSH so the profile records a local link:

cd /path/to/deepseek-harness
pnpm link /path/to/ReMe/integrations/dsh --workspace-root
dsh plugin --profile web add /path/to/ReMe/integrations/dsh

The first command makes a source checkout's package resolver see the local plugin; the second installs its bundle into the web profile. A published DSH installation normally needs only dsh plugin ... add. Do not commit a machine-specific link: dependency to DSH.

The package declares cordis.patch.yml through package.json#dsh.bundle.patch. The patch mounts exactly one Host runtime in an isolated remeMemory realm. DSH discovers the Web entry separately through package.json#dsh.client; mounting the package twice causes a remeMemory service collision in current DSH releases.

3.3 Start DSH Web

dsh web --no-open --port 3090

Open the local URL printed by DSH and select a workspace. If DSH enables an access token, use the authenticated URL from its startup output and do not copy the token into documentation or screenshots.

3.4 Real OpenAI-compatible verification

ReMe and DSH can share an OpenAI-compatible model endpoint during local verification without copying a secret into YAML. Load the ReMe repository's .env in the shell, then reference the environment variable from the DSH llm-pi-ai route:

set -a
source /path/to/ReMe/.env
set +a

# The patch/settings document contains only these references, never the value.
# apiKeyEnv: LLM_API_KEY
# baseURL: !!js process.env.LLM_BASE_URL
dsh web --no-open --port 3090

Declare the route with api: openai-completions, select LLM_MODEL_NAME (or an explicitly configured model id), and use DSH's generic @deepseek-ai/dsh-llm-pi-ai adapter. The direct llm-deepseek adapter adds DeepSeek-specific request extensions and is not the right compatibility layer for an arbitrary OpenAI-compatible gateway. Never print, screenshot, or commit the resolved key.

4. Configure ReMe Memory

Open Plugins → ReMe bundle → reme-memory → Configure. Save submits all edits together as one revision-checked atomic update. Save changes before starting the next session. Settings are stored in the ac

…(truncated)

Rendered from the README published on npm. Formatted only, not edited.

Embed the badge

Two badges, each using its own site's identity: left is the **standard** (published by DSHIO), right is the **testing organization** (deepseekbase, which ran the inspection). Each links to the standard text and the test report.

DSHIO/T 10deepseekbase

Markdown(推荐,两个一起)

[![DSHIO/T 10](https://deepseekbase.com/badge/std.svg)](https://dshio.org/10/) [![deepseekbase](https://deepseekbase.com/badge/agentscope-ai-reme-dsh-plugin.svg)](https://deepseekbase.com/zh/plugins/agentscope-ai-reme-dsh-plugin/)

Note: this is not a certification. DSHIO/T 32 is not in effect.

Versions

Published on npm::3,最后更新 2026-11-06

Links

GitHub · Homepage · npm

Frequently asked

How do I install @agentscope-ai/reme-dsh-plugin?
Run dsh plugin --profile web add @agentscope-ai/reme-dsh-plugin in DSH, or paste the package name into the DSH Desktop plugin page.
What if it fails to install?
Check your DSH version first (dsh --version), then look at the first error in the test-results section. If it still fails, open an issue on the repository.
How do I uninstall it?
Run dsh plugin --profile web remove @agentscope-ai/reme-dsh-plugin.

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