DeepSeek Harness 插件

dsh-moa-morphlin

Mixture of Agents (MoA) tool plugin for DeepSeek Harness — run several models in parallel on the same prompt, then have a stronger aggregator model synthesize a final answer. On-demand tool, zero(英文原文)

跳到安装方式

来源信息

GitHub 仓库
morphlinglan/dsh-moa
最近更新
2026年8月16日
分类
工具与能力
GitHub stars
0
载体类型
plugin
目录证据
上游声明已找到 dsh.bundle
证据路径
package.json#dsh.bundle
核对版本
0.1.0-rc.8
上游核对日期
2026-08-20

该证据由上游目录提供。本站没有安装、运行或安全审核这个插件。

安装

默认先复制一段 Prompt,让 Agent 读 GitHub 仓库和源码;需要自己装时再切到命令。

复制这段 Prompt,发给 DSH、Codex 或其他 Agent,让它先读 GitHub 仓库和源码。

请先不要安装或执行任何命令。阅读这个插件的 GitHub 仓库、README 和关键源码,然后用清楚、直接的方式回答以下问题,帮助我判断它是否适合我的需求:

1. 这个插件是什么,解决什么问题;
2. 适合哪些用户和典型使用场景;
3. 安装后如何使用,并给出一个最小使用示例;
4. 有哪些已知限制,以及隐私、安全、兼容性或维护风险;
5. 给出“推荐 / 有条件推荐 / 不推荐”的明确建议和理由。

请区分仓库明确说明、根据源码推断和未知信息。证据不足时请明确说明,不要猜测或照抄 README。

GitHub:https://github.com/morphlinglan/dsh-moa
插件名:dsh-moa-morphlin
作者:morphlinglan

检查来源文件

安装前先看这个插件目录里的 README 和其他文件。

文件资源管理器2 个文件
README.md来源说明 · 只读预览

dsh-moa

A DeepSeek Harness plugin that adds Mixture of Agents (MoA) as an on-demand tool: run_moa.

Instead of using one model, MoA sends the same prompt to several proposer models in parallel, then has a stronger aggregator model synthesize the best final answer from all their outputs. It is not on the normal request path — the agent calls the tool only when multi-model synthesis is worth the extra tokens/latency.

Install

dsh plugin --profile web add github:morphlinglan/dsh-moa

Then restart dsh web if it is already running.

Usage

Configure the proposer pool and aggregator in your profile's cordis.patch.yml:

- insert:
    - id: dsh-moa
      name: dsh-moa
      config:
        toolName: run_moa
        # Replace with your own providers/models.
        proposers:
          - provider: proposer-provider-a
            model: proposer-model-a
          - provider: proposer-provider-b
            model: proposer-model-b
        aggregator:
          provider: aggregator-provider
          model: aggregator-model
        minProposers: 2
        proposerMaxTokens: 1500
        aggregatorMaxTokens: 2500
        fallbackLongest: true
        maxRetries: 2

The providers/models above are placeholders only. Replace them with providers and models you actually have access to.

Then ask the agent to use run_moa for complex analysis, synthesis, translation, or review tasks.

Config

FieldTypeDefaultDescription
toolNamestring"run_moa"Tool name registered in DSH.
proposers{ provider, model }[][]Models that independently answer the prompt in parallel.
aggregator{ provider, model }requiredStronger model that synthesizes the final answer.
minProposersnumber2Minimum successful proposers required to run aggregation.
proposerMaxTokensnumber1500Output cap for each proposer.
aggregatorMaxTokensnumber2500Output cap for the aggregator.
fallbackLongestbooleantrueIf the aggregator fails, fall back to the longest proposer output.
temperaturenumberOptional sampling temperature passed to every call.
reasoningEffortstringOptional adapter-owned reasoning effort id.
maxRetriesnumber0Retries per proposer/aggregator on transient errors, with exponential backoff.
iterationsnumber1Iterative MoA rounds (1 = single layer + aggregator).

License

MIT