NanmiCoder/dsh-agent-teams

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One prompt. A working team.

dsh-agent-teams turns the current DeepSeek Harness session into a captain that can assemble durable sub-agents, split a goal into dependency-aware tasks, and coordinate work through direct messages.

Ask in natural language. The plugin provides the team protocol, eleven coordination tools, persistent state, an automatic shared-task scheduler, and a live Web UI—without requiring a separate workflow engine.

Releases

Read the latest release notes or browse the complete release history. The same Markdown notes are included in the npm package under release-notes/.

Why AgentTeams?

The conversation card and activity panel use Harness's official locale service. They follow live language changes between English and Simplified Chinese—including status labels, dynamic summaries, controls, archive markers, and accessibility text—without a page reload or a separate plugin setting.

Install

[!NOTE] Requires an existing DeepSeek Harness installation.

npm

dsh plugin --profile web add @nanmicoder/dsh-agent-teams@latest

Build from source

git clone https://github.com/NanmiCoder/dsh-agent-teams.git
cd dsh-agent-teams
pnpm install
pnpm build
dsh plugin --profile web add .

Run pnpm build again after changing the source. The local plugin install remains linked to this checkout.

Validate the composed profile, restart DSH, and refresh the Web UI:

dsh --profile web --dump-config
dsh web

Then ask for a team directly:

Use AgentTeams to review the commits after v0.5.3 from performance, security, and product perspectives. Return one consolidated report.

How it works

1. The current session creates a team and becomes its captain.
2. The captain adds role-specific members backed by continuable sub-agents.
3. The goal becomes tasks with owners and explicit dependencies.
4. The shared scheduler uses real running / idle / ready state to atomically claim one ready task per idle member and wake it. An interrupted resident attempt stays parked and can resume through a direct message without losing its capability; after a cold process restart, the scheduler retries stranded open work with a fresh attempt.
5. Members update with the current attempt_id; reassignment or captain takeover revokes the old attempt and waits for the old worker to quiesce before a new attempt starts.
6. The captain presents the combined result, then archives the complete team record.

Team state is stored under <workspace>/.agent-teams/; the Web panel reads that disk truth and combines it with live sub-agent activity.

Member creation is zero-interaction by default: a member on the captain's current LLM route snapshots that provider, model, and reasoning effort, while a member on a requested alternative route snapshots the target model's default effort; later continuations restore the resolved snapshot. Only an explicit heterogeneous-team request (for example, “backend on provider A/model X, frontend on provider B/model Y”) supplies a member-specific provider + model; there is no per-member model or reasoning prompt.

Slash command

No “use AgentTeams” phrasing required. The plugin registers the closed-namespace /agent-teams host command, so the Web GUI slash menu shows an agent-teams placeholder with an input hint: pick it (or type the command), describe the goal, and press Enter.

/agent-teams research the pricing pages of three competitors

The command pipeline claims the line, then preserves that exact input as an ordinary user follow-up so it remains visible in the main chat. The gesture boundary adds the deterministic activation directive at pre-step, so the captain protocol still starts immediately. The invocation is also durably logged (command/run / command/done).

Surfaces without command adjudication (for example the headless CLI) get the same deterministic activation through a gesture boundary: any genuine user message starting with /agent-teams activates the protocol for the rest of the text. Mid-sentence mentions stay ordinary prose.

Configuration

Defaults work without extra setup. A trusted profile can override member behavior:

- id: agent-teams
  config:
    stateDir: .agent-teams
    memberProvider: spawn
    memberModel: deepseek-v4
    memberMaxDepth: 1
    maxMembers: 8

memberProvider is the sub-agent runtime backend (spawn / fork), not an LLM provider. Cross-LLM-provider routing uses the optional provider + model fields of agent_teams_add_member; memberModel is only a model default for all members. A member on the captain's current provider/model inherits the captain's reasoning effort, while a changed provider or model automatically uses the target model's default. To request a particular effort, pass the optional reasoning_effort field — one of the target model's supported effort ids, or "default" to force the model's own default.

slashCommand: false disables the deterministic /agent-teams activation surfaces (slash command and gesture boundary), leaving the natural-language trigger as the only entry point.

Boundaries

• One captain leads one active team at a time.
• Idle members with no open task are automatically reused for ready work. An idle member that still owns an open attempt is parked until messaged or explicitly reassigned; messages that cannot be delivered live remain durable and are retried at a later status boundary.
• State is file-backed and serialized within one DSH process; concurrent processes editing the same team are not coordinated.
• The activity panel reports persisted state as-is. Models may occasionally finish work without performing the expected task-state update.

See docs/usage.md for the full tool reference, state model, Web UI behavior, configuration, and known limits.

Plugin development Skill

The repository also ships the open Agent Skills package dsh-plugin-development:

npx skills add NanmiCoder/dsh-agent-teams --skill dsh-plugin-development

Documentation

Development

pnpm install
pnpm build
pnpm verify

Named multi-role profiles

Configure one or more complete team profiles in cordis.patch.yml. A profile always supplies the roster (independent provider/model/role/reasoning effort). Set taskPlanning: captain when the Captain should derive the DAG from the user's goal; omit it or set taskPlanning: seed to keep a fixed template workflow:

profiles:
  demo-delivery:
    description: Ship a small feature
    protocol: Discuss requirements, review, test, then prepare release; do not deploy automatically.
    members:
      - name: analyst
        model: gpt-5.6-sol
        role: Analyze requirements
      - name: implementer
        model: gpt-5.6-terra
        role: Implement the approved solution
    tasks:
      - id: requirements
        subject: Requirements discussion
        assignee: analyst
      - id: implementation
        subject: Implement solution
        assignee: implementer
        dependencies: [requirements]

Use an explicit profile flag: /agent-teams --profile demo-delivery implement the feature. The first ordinary token is never treated as an implicit profile. Normal command runs call agent_teams_create({ profile, approval: "required" }): the roster and seed/Captain-designed DAG remain staged, no child session is created, and no task is claimed. Edit the plan in the activity panel, then click Approve & Run. Approval resolves the final provider/model/reasoning choices, atomically spawns the roster, and starts only ready tasks. Direct tool clients may pass approval: "automatic" for the legacy immediate path. Failed review/test tasks do not unlock downstream work; automatic repair/review tasks do not depend on the failed review.

License

MIT