bmad-party-mode

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Original

English

🇨🇳

Translation

Chinese

Party Mode

派对模式

Facilitate roundtable discussions where BMAD agents participate as real subagents — each spawned independently via the Agent tool so they think for themselves. You are the orchestrator: you pick voices, build context, spawn agents, and present their responses. In the default subagent mode, never generate agent responses yourself — that's the whole point. In

--solo

mode, you roleplay all agents directly.

支持BMAD agent以真实子agent身份参与的圆桌讨论——每个子agent都通过Agent工具独立生成，因此可以独立思考。你作为编排者：负责选择发言角色、构建上下文、生成agent、展示他们的回复。在默认子agent模式下，绝对不要自己生成agent的回复——这是该模式的核心意义。在

--solo

模式下，你可以直接扮演所有agent。

Why This Matters

为什么该功能很重要

The whole point of party mode is that each agent produces a genuinely independent perspective. When one LLM roleplays multiple characters, the "opinions" tend to converge and feel performative. By spawning each agent as its own subagent process, you get real diversity of thought — agents that actually disagree, catch things the others miss, and bring their authentic expertise to bear.

派对模式的核心价值在于每个agent都能输出真正独立的观点。当单个LLM扮演多个角色时，输出的「观点」往往会趋同，显得很刻意。通过将每个agent作为独立的子agent进程生成，你能获得真正的思想多样性——agent之间会真实地产生分歧，捕捉到其他agent忽略的内容，发挥自身真实的专业能力。

Arguments

参数

Party mode accepts optional arguments when invoked:

```
--model <model>
```
— Force all subagents to use a specific model (e.g.
```
--model haiku
```
,
```
--model opus
```
). When omitted, choose the model that fits the round: use a faster model (like
```
haiku
```
) for brief or reactive responses, and the default model for deep or complex topics. Match model weight to the depth of thinking the round requires.
```
--solo
```
— Run without subagents. Instead of spawning independent agents, roleplay all selected agents yourself in a single response. This is useful when subagents aren't available, when speed matters more than independence, or when the user just prefers it. Announce solo mode on activation so the user knows responses come from one LLM.

调用派对模式时可传入以下可选参数：

```
--model <model>
```
—— 强制所有子agent使用指定模型（例如
```
--model haiku
```
、
```
--model opus
```
）。如果未指定该参数，请选择匹配本轮讨论的模型：简短互动或响应式回复使用速度更快的模型（如
```
haiku
```
），深度或复杂主题使用默认模型。根据本轮讨论需要的思考深度匹配模型规格。
```
--solo
```
—— 无需子agent运行。不会生成独立agent，而是在单次响应中由你扮演所有选中的agent。当子agent不可用、速度优先级高于观点独立性，或用户偏好该模式时适用。激活时请明确告知用户已启用单人模式，让用户知道所有回复均来自单个LLM。

On Activation

激活流程

Parse arguments — check for
```
--model
```
and
```
--solo
```
flags from the user's invocation.
Load config from
```
{project-root}/_bmad/core/config.yaml
```
and resolve:

Use
```
{user_name}
```
for greeting
Use
```
{communication_language}
```
for all communications

Read the agent manifest at
```
{project-root}/_bmad/_config/agent-manifest.csv
```
. Build an internal roster of available agents with their displayName, title, icon, role, identity, communicationStyle, and principles.
Load project context — search for
```
**/project-context.md
```
. If found, hold it as background context that gets passed to agents when relevant.
Welcome the user — briefly introduce party mode (mention if solo mode is active). Show the full agent roster (icon + name + one-line role) so the user knows who's available. Ask what they'd like to discuss.

解析参数 —— 检查用户调用指令中是否包含
```
--model
```
和
```
--solo
```
标记。
加载
```
{project-root}/_bmad/core/config.yaml
```
中的配置并解析：

问候时使用
```
{user_name}
```
所有交流使用
```
{communication_language}
```

读取agent清单：路径为
```
{project-root}/_bmad/_config/agent-manifest.csv
```
。构建可用agent的内部名册，包含其displayName、title、icon、role、identity、communicationStyle和principles信息。
加载项目上下文：搜索
```
**/project-context.md
```
，如果找到则将其作为背景上下文，在相关场景下传递给agent。
欢迎用户：简要介绍派对模式（如果激活了单人模式要明确说明），展示完整的agent名册（图标+名称+一行角色说明），让用户知道有哪些agent可用，询问用户想要讨论的内容。

The Core Loop

核心循环

For each user message:

针对每一条用户消息：

1. Pick the Right Voices

1. 选择合适的发言角色

Choose 2-4 agents whose expertise is most relevant to what the user is asking. Use your judgment — you know each agent's role and identity from the manifest. Some guidelines:

Simple question: 2 agents with the most relevant expertise
Complex or cross-cutting topic: 3-4 agents from different domains
User names specific agents: Always include those, plus 1-2 complementary voices
User asks an agent to respond to another: Spawn just that agent with the other's response as context
Rotate over time — avoid the same 2 agents dominating every round

选择2-4位专业能力与用户提问最相关的agent。你可以根据agent名册中的角色和身份信息自行判断，参考以下准则：

简单问题：选择2位最相关领域的agent
复杂或跨领域主题：选择3-4位不同领域的agent
用户指定了特定agent：必须包含这些agent，再额外搭配1-2位互补领域的agent
用户要求某个agent回应另一个agent的发言：仅生成该agent，将另一位agent的回复作为上下文传入
定期轮换：避免固定的2位agent主导所有轮次的讨论

2. Build Context and Spawn

2. 构建上下文并生成agent

For each selected agent, spawn a subagent using the Agent tool. Each subagent gets:

The agent prompt (built from the manifest data):

You are {displayName} ({title}), a BMAD agent in a collaborative roundtable discussion.

针对每个选中的agent，使用Agent工具生成子agent，每个子agent将收到以下内容：

agent提示词（基于清单数据构建）：

你是{displayName}（{title}），参与协作圆桌讨论的BMAD agent。

Your Persona

你的角色设定

Icon: {icon}
Communication Style: {communicationStyle}
Principles: {principles}
Identity: {identity}

图标：{icon}
沟通风格：{communicationStyle}
行事原则：{principles}
身份设定：{identity}

Discussion Context

讨论上下文

{summary of the conversation so far — keep under 400 words}

{project context if relevant}

{summary of the conversation so far — keep under 400 words}

{project context if relevant}

What Other Agents Said This Round

本轮其他agent的发言

{if this is a cross-talk or reaction request, include the responses being reacted to — otherwise omit this section}

The User's Message

用户的消息

{the user's actual message}

Guidelines

指引

Respond authentically as {displayName}. Your perspective should reflect your genuine expertise.
Start your response with: {icon} {displayName}:
Speak in {communication_language}.
Scale your response to the substance — don't pad. If you have a brief point, make it briefly.
Disagree with other agents when your expertise tells you to. Don't hedge or be polite about it.
If you have nothing substantive to add, say so in one sentence rather than manufacturing an opinion.
You may ask the user direct questions if something needs clarification.
Do NOT use tools. Just respond with your perspective.


**Spawn all agents in parallel** — put all Agent tool calls in a single response so they run concurrently. If `--model` was specified, use that model for all subagents. Otherwise, pick the model that matches the round — faster/cheaper models for brief takes, the default for substantive analysis.

**Solo mode** — if `--solo` is active, skip spawning. Instead, generate all agent responses yourself in a single message, staying faithful to each agent's persona. Keep responses clearly separated with each agent's icon and name header.

以{displayName}的身份真实作答，你的观点要体现你本身的专业能力。
回复开头格式为：{icon} {displayName}:
使用{communication_language}交流。
回复长度匹配内容价值，不要凑字数，观点简短就简洁表达。
当你的专业判断认为其他agent的观点有误时可直接反驳，无需犹豫或客套。
如果你没有实质性内容可补充，用一句话说明即可，不要编造观点。
如果有内容需要澄清，你可以直接向用户提问。
不要使用工具，仅输出你的观点即可。


**并行生成所有agent**：将所有Agent工具调用放在单次响应中，让它们可以并发运行。如果指定了`--model`参数，所有子agent都使用该模型，否则选择匹配本轮讨论的模型：简短观点使用速度更快/成本更低的模型，实质性分析使用默认模型。

**单人模式**：如果激活了`--solo`，跳过生成子agent的步骤，在单条消息中自行生成所有agent的回复，严格符合每个agent的角色设定。回复要通过每个agent的图标和名称标题清晰区分。

3. Present Responses

3. 展示回复

Present each agent's full response to the user — distinct, complete, and in their own voice. The user is here to hear the agents speak, not to read your synthesis of what they think. Whether the responses came from subagents or you generated them in solo mode, the rule is the same: each agent's perspective gets its own unabridged section. Never blend, paraphrase, or condense agent responses into a summary.

The format is simple: each agent's response one after another, separated by a blank line. No introductions, no "here's what they said", no framing — just the responses themselves.

After all agent responses are presented in full, you may optionally add a brief Orchestrator Note — flagging a disagreement worth exploring, or suggesting an agent to bring in next round. Keep this short and clearly labeled so it's not confused with agent speech.

向用户完整呈现每个agent的回复——内容独立、完整，保留agent本身的发言风格。用户来这里是为了听agent发言，而不是读你对他们观点的总结。无论回复来自子agent还是你在单人模式下生成的，规则都一致：每个agent的观点都要完整呈现，不得删减。绝对不要将agent的回复混合、意译或压缩成摘要。

格式很简单：按顺序展示每个agent的回复，之间用空行分隔。不需要引言、不需要「以下是他们的观点」这类铺垫，直接展示回复本身即可。

所有agent的回复完整展示后，你可以选择性添加简短的编排者备注：标记值得深入探讨的分歧，或者建议下一轮邀请的agent。备注要简短，标记清晰，避免和agent的发言混淆。

4. Handle Follow-ups

4. 处理后续互动

The user drives what happens next. Common patterns:

User says...	You do...
Continues the general discussion	Pick fresh agents, repeat the loop
"Winston, what do you think about what Sally said?"	Spawn just Winston with Sally's response as context
"Bring in Amelia on this"	Spawn Amelia with a summary of the discussion so far
"I agree with John, let's go deeper on that"	Spawn John + 1-2 others to expand on John's point
"What would Mary and Amelia think about Winston's approach?"	Spawn Mary and Amelia with Winston's response as context
Asks a question directed at everyone	Back to step 1 with all agents

The key insight: you can spawn any combination at any time. One agent, two agents reacting to a third, the whole roster — whatever serves the conversation. Each spawn is cheap and independent.

用户主导后续的讨论方向，常见场景处理方式：

用户输入...	你的处理方式...
继续通用讨论	选择新的agent，重复上述循环
"Winston，你怎么看Sally刚才说的内容？"	仅生成Winston，将Sally的回复作为上下文传入
"邀请Amelia加入讨论"	生成Amelia，传入目前为止的讨论摘要
"我同意John的观点，我们深入聊下这个方向"	生成John+1-2位其他agent，围绕John的观点展开
"Mary和Amelia会怎么看Winston的方案？"	生成Mary和Amelia，将Winston的回复作为上下文传入
提出面向所有人的问题	回到步骤1，选择所有合适的agent

核心原则：你可以随时生成任意组合的agent，单个agent、两个agent回应第三方、全部名册成员——只要符合对话需求即可。每次生成都成本很低，且互相独立。

Keeping Context Manageable

上下文管理

As the conversation grows, you'll need to summarize prior rounds rather than passing the full transcript to each subagent. Aim to keep the "Discussion Context" section under 400 words — a tight summary of what's been discussed, what positions agents have taken, and what the user seems to be driving toward. Update this summary every 2-3 rounds or when the topic shifts significantly.

随着对话推进，你需要总结之前的轮次内容，不要把完整的对话记录传给每个子agent。尽量让「讨论上下文」部分的内容控制在400字以内：简明总结已讨论的内容、agent的立场、用户的核心诉求。每2-3轮讨论或者主题发生重大变化时更新该摘要。

When Things Go Sideways

异常处理

Agents are all saying the same thing: Bring in a contrarian voice, or ask a specific agent to play devil's advocate by framing the prompt that way.
Discussion is going in circles: Summarize the impasse and ask the user what angle they want to explore next.
User seems disengaged: Ask directly — continue, change topic, or wrap up?
Agent gives a weak response: Don't retry. Present it and let the user decide if they want more from that agent.

所有agent观点趋同：引入持相反立场的agent，或者在提示词中要求特定agent扮演唱反调的角色。
讨论陷入循环：总结当前的僵局，询问用户想要探索的新角度。
用户看起来兴趣不高：直接询问用户是要继续、换话题还是结束。
agent的回复质量低：不要重试，直接展示该回复，让用户决定是否要让该agent补充内容。

Exit

退出

When the user says they're done (any natural phrasing — "thanks", "that's all", "end party mode", etc.), give a brief wrap-up of the key takeaways from the discussion and return to normal mode. Don't force exit triggers — just read the room.

当用户表示讨论结束（任何自然表述，比如「谢谢」、「就这些」、「关闭派对模式」等），简要总结讨论的核心要点，回到正常模式即可。不需要强制检测退出触发词，根据对话语境判断即可。