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Multi-agent & tasks ​

For complex work, a single agent conversation can get unwieldy. Vault Operator handles this with sub-agents: child agents that take on specific parts of a larger task on their own. It also pulls actionable tasks out of conversations and turns them into trackable notes.

You will need: a working chat with at least one configured model. Sub-agents inherit the parent's model unless you give them their own mode with a different model assigned.

Use this guide when: a task is too large for one chat (researching three topics in parallel, processing a folder of files, comparing vault content against external research), or when you want extracted tasks to land as notes you can track.

You will know it works when: the agent spawns a sub-task with new_task, you see it in the activity block as a nested chat, the result flows back into the parent, and (if task extraction is on) actionable checkboxes from the conversation appear as task notes in the configured folder.

When to delegate vs. stay in one chat ​

  • Stay in one chat for sequential work in a single domain ("read note A, then edit note B").
  • Delegate when steps are independent (three searches that do not depend on each other), when one step needs a different mode (Ask for analysis while Agent does writes), or when context would balloon (large research with many tool results).
  • Avoid delegating for tasks that finish in 2-3 tool calls. The cost of spawning a sub-agent outweighs the benefit.

What are sub-agents? ​

A sub-agent is a separate agent instance spawned by the main agent. It gets its own conversation, its own mode, and its own tool access. The parent hands off a specific job, waits for the result, then carries on.

When sub-agents help ​

  • Research fan-out: search multiple topics in parallel instead of one after the other
  • Divide and conquer: break a large task into independent pieces
  • Mode isolation: run a read-only analysis in Ask mode while the parent works in Agent mode
  • Long tasks: keep the main conversation focused while a sub-agent handles a side errand

How new_task works ​

The agent spawns sub-agents through the new_task tool. You don't call this tool directly. The agent decides when delegation makes sense.

What the agent specifies ​

ParameterPurpose
ModeWhich mode the child agent runs in (Ask or Agent)
MessageThe specific task description for the child
ContextRelevant information passed from the parent conversation

Depth guard ​

Sub-agents can spawn their own sub-agents, but Vault Operator enforces a maximum depth of 2 levels. This prevents runaway chains:

Main Agent (level 0)
  -> Sub-Agent A (level 1)
      -> Sub-Agent A1 (level 2, maximum depth, cannot spawn further)
  -> Sub-Agent B (level 1)

Parallel execution ​

Read-safe tools (searching, reading files, semantic search) run in parallel via Promise.all. A sub-agent researching three topics searches all three at once, not one after another.

You don't need to manage this

Sub-agent orchestration is automatic. Describe your goal and the agent decides whether to delegate. For example, "Research these 5 companies and create a comparison table" might spawn one sub-agent per company.

Practical examples ​

Research fan-out ​

Your prompt: "Compare the note-taking approaches described in my notes about Zettelkasten, PARA, and Johnny Decimal"

What happens:

  1. The main agent spawns 3 sub-agents, one for each system
  2. Each sub-agent searches and reads the relevant notes
  3. Results return to the parent agent
  4. The parent creates the comparison

Divide and conquer ​

Your prompt: "Reorganize my Projects/ folder. Group notes by status (active, completed, on hold) and create an index note"

What happens:

  1. A sub-agent analyzes all notes and classifies them by status
  2. The parent agent creates the folder structure and moves files
  3. A final sub-agent generates the index note with links

Task extraction ​

Vault Operator watches for actionable items in agent responses. When the agent produces a list with unchecked checkboxes (- [ ]), the TaskExtractor picks them up automatically.

How it works ​

  1. The agent responds with tasks in its message (a project plan, action items)
  2. Vault Operator detects the - [ ] items
  3. A TaskSelectionModal pops up so you can pick which tasks to save
  4. Selected tasks become individual notes in your vault

Task notes ​

Each extracted task becomes a note with structured frontmatter:

markdown
---
type: task
status: open
source: agent-conversation
created: 2026-03-31
---

# Review Q1 budget allocations

Compare actual spending against planned budget for each department.
Highlight any variance above 10%.

This plays nicely with your existing task management: Dataview queries, kanban boards, or any plugin that reads frontmatter.

Not just agent tasks

Task extraction works on any checklist the agent produces: project plans, follow-ups from meeting notes, research next steps. If the agent writes - [ ] items, you can capture them.

Tips for multi-agent work ​

  1. Be ambitious. Multi-step requests like "research, compare, and summarize" are exactly what sub-agents are good at.
  2. Provide scope. Mention specific folders, tags, or file names so sub-agents know where to look.
  3. Check the activity block. You can see each sub-agent's tool calls in the parent's activity view.
  4. Use task extraction. When the agent gives you a plan, let it create task notes so nothing falls through the cracks.
  5. Trust the depth limit. Two levels of sub-agents cover most real-world scenarios. If you need more, break the work into separate conversations.

Model quality matters

Sub-agents consume additional API calls. Each child agent has its own conversation with the model. Use a capable model (Claude Sonnet or better) for multi-agent tasks. Smaller models may struggle with delegation decisions.

Next steps ​