Workflow & Automation

dsh-moa

morphlinglan/dsh-moa · ★ 0

morphlinglan

DeepSeek Harness plugin: Mixture of Agents (MoA) on-demand tool.

Repository Source

License: Unknown
Added: 2026-08-16
Maintenance: active
Install kind: github

Manifest details

Manifest status: Declared
Manifest path: package.json#dsh
Manifest checked: 2026-08-19
Lifecycle scripts: None found

Static manifest checks report declarations only; they are not compatibility or security review.

安装

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

GitHub 源码安装：首次需按提示配置 allowBuilds 构建授权后重试

For install and environment setup, plus plugin development tutorials, see DSH community docs ↗

README

Source README · body: en · Read Chinese README

Table of contents

Install
Usage
Config
License

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

Field	Type	Default	Description
`toolName`	`string`	`"run_moa"`	Tool name registered in DSH.
`proposers`	`{ provider, model }[]`	`[]`	Models that independently answer the prompt in parallel.
`aggregator`	`{ provider, model }`	required	Stronger model that synthesizes the final answer.
`minProposers`	`number`	`2`	Minimum successful proposers required to run aggregation.
`proposerMaxTokens`	`number`	`1500`	Output cap for each proposer.
`aggregatorMaxTokens`	`number`	`2500`	Output cap for the aggregator.
`fallbackLongest`	`boolean`	`true`	If the aggregator fails, fall back to the longest proposer output.
`temperature`	`number`	—	Optional sampling temperature passed to every call.
`reasoningEffort`	`string`	—	Optional adapter-owned reasoning effort id.
`maxRetries`	`number`	`0`	Retries per proposer/aggregator on transient errors, with exponential backoff.
`iterations`	`number`	`1`	Iterative MoA rounds (`1` = single layer + aggregator).

License

MIT

README

Install

Usage

Config

License

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