Team Configuration

Teams allow multiple agents to collaborate on tasks. MindRoom supports two collaboration modes.

Team Modes

Coordinate Mode

The team coordinator analyzes the task and delegates different subtasks to specific team members:

teams:
  dev_team:
    display_name: Dev Team
    role: Development team for building features
    agents: [architect, coder, reviewer]
    mode: coordinate

In coordinate mode, the coordinator analyzes the task and selects which agents should handle which subtasks based on their roles. The coordinator decides whether to run tasks sequentially or in parallel based on dependencies, then synthesizes all outputs into a cohesive response.

Collaborate Mode

All agents work on the same task simultaneously and their outputs are synthesized:

teams:
  research_team:
    display_name: Research Team
    role: Research team for comprehensive analysis
    agents: [researcher, analyst, writer]
    mode: collaborate

In collaborate mode, the task is delegated to all team members simultaneously. Each agent works on the same task independently, and the coordinator synthesizes all perspectives into a final response. This is useful when you want diverse perspectives on the same problem.

Full Configuration

teams:
  super_team:
    # Display name shown in Matrix
    display_name: Super Team

    # Description of the team's purpose (required)
    role: Multi-disciplinary team for complex tasks

    # Agents in this team (must be defined in agents section)
    agents:
      - code
      - research
      - finance

    # Collaboration mode: coordinate or collaborate (default: coordinate)
    mode: collaborate

    # Rooms the team responds in
    rooms:
      - team-room

    # Model for team coordination (default: "default")
    model: sonnet

    # Participate in room-level startup prewarm for rooms already joined at first sync (default: true)
    startup_thread_prewarm: true

    # Team-scoped replay controls (optional; inherit from defaults when omitted)
    num_history_runs: 8
    num_history_messages: null
    max_tool_calls_from_history: 6

    # Team-scoped required-compaction overrides (optional)
    # Soft thresholds do not compact by themselves while history still fits.
    compaction:
      enabled: true
      threshold_percent: 0.8
      reserve_tokens: 16384

Configuration Fields

Team YAML keys follow the same naming rules as agents: alphanumeric characters and underscores only, and no overlap with agent names.

num_history_runs and num_history_messages are mutually exclusive, just like the agent-level settings. When a named team sets these fields, the team scope uses the team-owned policy instead of inheriting one member's history policy.

Team-scoped compaction supports enabled, threshold_tokens, threshold_percent, reserve_tokens, and model. When the active team model has a known context_window, MindRoom always computes a final replay plan for the shared team scope and reduces or disables persisted replay for the run when needed. Automatic destructive compaction is enabled by default through defaults.compaction, but it runs only when raw history exceeds the hard replay budget for the next reply. threshold_tokens and threshold_percent set a soft trigger budget for planning metadata and compaction notices. Crossing that soft trigger while still within the hard budget leaves the stored session unchanged and relies on replay fitting. Use enabled: false to disable automatic pre-reply compaction for a team. Replay safety always uses the active team model window. If you set compaction.model, that summary model must also define its own context_window, but only for the durable summary-generation pass. Manual compact_context remains available when a compaction model and context window are configured. Compaction uses an in-room lifecycle notice that is edited in place.

Startup thread prewarm is a background, best-effort cache warmup for rooms already joined when first sync completes.

When to Use Each Mode

Dynamic Team Formation

When multiple agents are mentioned in a message (e.g., @code @research analyze this), MindRoom automatically forms an ad-hoc team. Dynamic teams form in these scenarios: In threads with multiple human participants, stale thread context does not auto-form a team. A fresh explicit @mention in the current message is required before agents respond.

1. Multiple agents explicitly tagged - e.g., @code @research analyze this
2. Thread with previously mentioned agents - Follow-up messages in a thread where multiple agents were mentioned earlier, as long as the thread has not become a multi-human conversation that now requires a fresh explicit mention
3. Thread with multiple agent participants - Continuing a conversation where multiple agents have responded, as long as the thread has not become a multi-human conversation that now requires a fresh explicit mention
4. DM room with multiple agents - Messages in a DM room containing multiple agents (main timeline only)

Mode Selection

For dynamic teams, the collaboration mode is selected by AI based on the task:

• Tasks with different subtasks for each agent use coordinate mode
• Tasks asking for opinions or brainstorming use collaborate mode

When AI mode selection is unavailable or fails, MindRoom falls back to: - coordinate when multiple agents are explicitly tagged in the message (they likely have different roles to fulfill) - collaborate for all other cases, such as agents from thread history or DM rooms (likely discussing the same topic)

Dynamic teams do not have a named teams: entry, so their history replay and compaction policy comes from defaults, not from any participating agent's overrides.