Subagent Delegation

The delegate_task tool spawns child AIAgent instances with isolated context, restricted toolsets, and their own terminal sessions. Each child gets a fresh conversation and works independently — only its final summary enters the parent's context.

Watching live agents

On the desktop app, open Fleet Control to see portraits, live tool streams, pause/stop controls, and when to promote long work onto Kanban.

Single Task

delegate_task(
    goal="Debug why tests fail",
    context="Error: assertion in test_foo.py line 42",
    toolsets=["terminal", "file"]
)

Parallel Batch

Up to 3 concurrent subagents by default (configurable, no hard ceiling):

delegate_task(tasks=[
    {"goal": "Research topic A", "toolsets": ["web"]},
    {"goal": "Research topic B", "toolsets": ["web"]},
    {"goal": "Fix the build", "toolsets": ["terminal", "file"]}
])

Mid-run Multitask

When you want parallel work while the parent turn is still running (Cursor-style /multitask), use the product surface — not a new core tool:

Action	Effect
/multitask <goal> / /mt	Spawn background leaf subagent(s) now
Desktop Alt/⌥+Enter	Same, from the composer while busy
Plan tab Build parallel	First ready wave of plan checkboxes → up to 3 children
Plan tab Revise	Mid-run [PLAN REVISE] steer so the parent re-reads the plan file

This wraps delegate_task(..., background=True) with caps and safety:

• Cap: delegation.max_concurrent_children × delegation.multitask.max_prompts_per_dispatch (default 3)
• Path-overlap warning when two goals mention the same file (optional block)
• Soft prompt-character budget before spawn
• Leaf children cannot recursively multitask (same as leaf delegate_task)

# ~/.vibeos/config.yaml
delegation:
  multitask:
    enabled: true
    max_prompts_per_dispatch: 3
    warn_on_write_overlap: true
    block_on_write_overlap: false
    max_estimated_prompt_chars: 12000
    # Opt-in: each child in its own git worktree under .worktrees/multitask-*
    use_worktrees: false
    worktree_sync: true
    # After child finishes: remove tree when no unpushed commits (default on)
    cleanup_worktrees_on_complete: true

When use_worktrees: true, each spawned child gets a dedicated checkout (CLI -w helpers) and a per-task_id cwd pin so parallel writers do not share the parent tree. Paths appear in the dispatch result under worktrees. With cleanup_worktrees_on_complete: true (default), the tree is removed when the child exits if there are no unpushed commits; trees with real unpushed work are kept. Stale leftovers still age out via the same startup prune as vibeos-* (24h soft / 72h hard). Desktop Settings → Advanced exposes the multitask toggles.

Queue vs Steer vs Multitask

• Queue — later, after the current turn
• Steer (/steer / ⌘Enter) — whisper into the same run after the next tool batch
• Multitask — second pair of hands now (separate context; summary returns when done)

Offline smoke: scripts/smoke-multitask-midrun.sh and scripts/smoke-plan-build-parallel.sh.

How Subagent Context Works

Critical: Subagents Know Nothing

Subagents start with a completely fresh conversation. They have zero knowledge of the parent's conversation history, prior tool calls, or anything discussed before delegation. The subagent's only context comes from the goal and context fields the parent agent populates when it calls delegate_task.

This means the parent agent must pass everything the subagent needs in the call:

# BAD - subagent has no idea what "the error" is
delegate_task(goal="Fix the error")

# GOOD - subagent has all context it needs
delegate_task(
    goal="Fix the TypeError in api/handlers.py",
    context="""The file api/handlers.py has a TypeError on line 47:
    'NoneType' object has no attribute 'get'.
    The function process_request() receives a dict from parse_body(),
    but parse_body() returns None when Content-Type is missing.
    The project is at /home/user/myproject and uses Python 3.11."""
)

The subagent receives a focused system prompt built from your goal and context, instructing it to complete the task and provide a structured summary of what it did, what it found, any files modified, and any issues encountered.

Practical Examples

Parallel Research

Research multiple topics simultaneously and collect summaries:

delegate_task(tasks=[
    {
        "goal": "Research the current state of WebAssembly in 2025",
        "context": "Focus on: browser support, non-browser runtimes, language support",
        "toolsets": ["web"]
    },
    {
        "goal": "Research the current state of RISC-V adoption in 2025",
        "context": "Focus on: server chips, embedded systems, software ecosystem",
        "toolsets": ["web"]
    },
    {
        "goal": "Research quantum computing progress in 2025",
        "context": "Focus on: error correction breakthroughs, practical applications, key players",
        "toolsets": ["web"]
    }
])

Code Review + Fix

Delegate a review-and-fix workflow to a fresh context:

delegate_task(
    goal="Review the authentication module for security issues and fix any found",
    context="""Project at /home/user/webapp.
    Auth module files: src/auth/login.py, src/auth/jwt.py, src/auth/middleware.py.
    The project uses Flask, PyJWT, and bcrypt.
    Focus on: SQL injection, JWT validation, password handling, session management.
    Fix any issues found and run the test suite (pytest tests/auth/).""",
    toolsets=["terminal", "file"]
)

Multi-File Refactoring

Delegate a large refactoring task that would flood the parent's context:

delegate_task(
    goal="Refactor all Python files in src/ to replace print() with proper logging",
    context="""Project at /home/user/myproject.
    Use the 'logging' module with logger = logging.getLogger(__name__).
    Replace print() calls with appropriate log levels:
    - print(f"Error: ...") -> logger.error(...)
    - print(f"Warning: ...") -> logger.warning(...)
    - print(f"Debug: ...") -> logger.debug(...)
    - Other prints -> logger.info(...)
    Don't change print() in test files or CLI output.
    Run pytest after to verify nothing broke.""",
    toolsets=["terminal", "file"]
)

Batch Mode Details

When you provide a tasks array, subagents run in parallel using a thread pool:

• Maximum concurrency: 3 tasks by default (configurable via delegation.max_concurrent_children or the DELEGATION_MAX_CONCURRENT_CHILDREN env var; floor of 1, no hard ceiling). Batches larger than the limit return a tool error rather than being silently truncated.
• Thread pool: Uses ThreadPoolExecutor with the configured concurrency limit as max workers
• Progress display: In CLI mode, a tree-view shows tool calls from each subagent in real-time with per-task completion lines. In gateway mode, progress is batched and relayed to the parent's progress callback
• Result ordering: Results are sorted by task index to match input order regardless of completion order
• Interrupt propagation: Interrupting the parent (e.g., sending a new message) interrupts all active children

Single-task delegation runs directly without thread pool overhead.

Model Override

You can configure a different model for subagents via config.yaml — useful for delegating simple tasks to cheaper/faster models:

# In ~/.vibeos/config.yaml
delegation:
  model: "google/gemini-flash-2.0"    # Cheaper model for subagents
  provider: "openrouter"              # Optional: route subagents to a different provider

If omitted, subagents use the same model as the parent.

Toolset Selection Tips

The toolsets parameter controls what tools the subagent has access to. Choose based on the task:

Toolset Pattern	Use Case
["terminal", "file"]	Code work, debugging, file editing, builds
["web"]	Research, fact-checking, documentation lookup
["terminal", "file", "web"]	Full-stack tasks (default)
["file"]	Read-only analysis, code review without execution
["terminal"]	System administration, process management

Certain toolsets are blocked for subagents regardless of what you specify:

• delegation — blocked for leaf subagents (the default). Retained for role="orchestrator" children, bounded by max_spawn_depth — see Depth Limit and Nested Orchestration below.
• clarify — subagents cannot interact with the user
• memory — no writes to shared persistent memory
• code_execution — children should reason step-by-step
• send_message — no cross-platform side effects (e.g., sending Telegram messages)

Max Iterations

Each subagent has an iteration limit (default: 50) that controls how many tool-calling turns it can take:

delegate_task(
    goal="Quick file check",
    context="Check if /etc/nginx/nginx.conf exists and print its first 10 lines",
    max_iterations=10  # Simple task, don't need many turns
)

Child Timeout

By default there is no wall-clock timeout on subagents. Children fail only from what they're actually doing — API errors, tool errors, or hitting their iteration budget — never from a delegation-level stopwatch. Earlier releases shipped a hard cap (300s, later 600s), which kept killing legitimately busy children mid-task: deep code reviews, large research fan-outs, and slow reasoning models routinely need more than 10 minutes while making steady progress the whole time.

Genuinely stuck children are still detected: the heartbeat staleness monitor stops refreshing the parent's activity when a child makes no progress (no API calls, no tool starts), letting the gateway inactivity timeout fire on a truly wedged worker.

If you want a hard cap anyway (e.g. cost control on unattended cron-driven delegation), opt in per-install:

delegation:
  child_timeout_seconds: 0     # default: 0 = no timeout
  # child_timeout_seconds: 1800  # opt-in hard cap (floor 30s)

A positive value enforces a hard wall-clock limit on each child; 0 or a negative value disables it.

Diagnostic dump on zero-call timeout

With a hard cap configured, if a subagent times out having made zero API calls (usually: provider unreachable, auth failure, or tool-schema rejection), delegate_task writes a structured diagnostic to ~/.vibeos/logs/subagent-timeout-<session>-<timestamp>.log containing the subagent's config snapshot, credential-resolution trace, and any early error messages. Much easier to root-cause than the previous silent-timeout behavior.

Monitoring Running Subagents (/agents)

The TUI ships a /agents overlay (alias /tasks) that turns recursive delegate_task fan-out into a first-class audit surface:

• Live tree view of running and recently-finished subagents, grouped by parent
• Per-branch cost, token, and file-touched rollups
• Kill and pause controls — cancel a specific subagent mid-flight without interrupting its siblings
• Post-hoc review: step through each subagent's turn-by-turn history even after they've returned to the parent

The classic CLI just prints /agents as a text summary; the TUI is where the overlay shines. See TUI — Slash commands.

Depth Limit and Nested Orchestration

By default, delegation is flat: a parent (depth 0) spawns children (depth 1), and those children cannot delegate further. This prevents runaway recursive delegation.

For multi-stage workflows (research → synthesis, or parallel orchestration over sub-problems), a parent can spawn orchestrator children that can delegate their own workers:

delegate_task(
    goal="Survey three code review approaches and recommend one",
    role="orchestrator",  # Allows this child to spawn its own workers
    context="...",
)

• role="leaf" (default): child cannot delegate further — identical to the flat-delegation behavior.
• role="orchestrator": child retains the delegation toolset. Gated by delegation.max_spawn_depth (default 1 = flat, so role="orchestrator" is a no-op at defaults). Raise max_spawn_depth to 2 to allow orchestrator children to spawn leaf grandchildren; 3+ for deeper trees. There is no upper ceiling — cost is the practical limit.
• delegation.orchestrator_enabled: false: global kill switch that forces every child to leaf regardless of the role parameter.

Cost warning: With max_spawn_depth: 3 and max_concurrent_children: 3, the tree can reach 3×3×3 = 27 concurrent leaf agents. Each extra level multiplies spend — raise max_spawn_depth intentionally.

Lifetime and Durability

delegate_task is synchronous — not durable

delegate_task runs inside the parent's current turn. It blocks the parent until every child finishes (or is cancelled). It is not a background job queue:

• If the parent is interrupted (user sends a new message, /stop, /new), all active children are cancelled and return status="interrupted". Their in-progress work is discarded.
• Children do not continue running after the parent turn ends.
• Cancelled children return a structured result (status="interrupted", exit_reason="interrupted"), but because the parent was interrupted too, that result often never makes it into a user-visible reply.

For durable long-running work that must survive interrupts or outlive the current turn, use:

• cronjob (action=create) — schedules a separate agent run; immune to parent-turn interrupts.
• terminal(background=True, notify_on_complete=True) — long-running shell commands that keep running while the agent does other things.

Key Properties

• Each subagent gets its own terminal session (separate from the parent)
• Nested delegation is opt-in — only role="orchestrator" children can delegate further, and only when max_spawn_depth is raised from its default of 1 (flat). Disable globally with orchestrator_enabled: false.
• Leaf subagents cannot call: delegate_task, clarify, memory, send_message, execute_code. Orchestrator subagents retain delegate_task but still cannot use the other four.
• Interrupt propagation — interrupting the parent interrupts all active children (including grandchildren under orchestrators)
• Only the final summary enters the parent's context, keeping token usage efficient
• Subagents inherit the parent's API key, provider configuration, and credential pool (enabling key rotation on rate limits)

Delegation vs execute_code

Factor	delegate_task	execute_code
Reasoning	Full LLM reasoning loop	Just Python code execution
Context	Fresh isolated conversation	No conversation, just script
Tool access	All non-blocked tools with reasoning	7 tools via RPC, no reasoning
Parallelism	3 concurrent subagents by default (configurable)	Single script
Best for	Complex tasks needing judgment	Mechanical multi-step pipelines
Token cost	Higher (full LLM loop)	Lower (only stdout returned)
User interaction	None (subagents can't clarify)	None

Rule of thumb: Use delegate_task when the subtask requires reasoning, judgment, or multi-step problem solving. Use execute_code when you need mechanical data processing or scripted workflows.

Configuration

# In ~/.vibeos/config.yaml
delegation:
  max_iterations: 50                        # Max turns per child (default: 50)
  # max_concurrent_children: 3              # Parallel children per batch (default: 3)
  # max_spawn_depth: 1                      # Tree depth (floor 1, no ceiling, default 1 = flat). Raise to 2 to allow orchestrator children to spawn leaves; 3+ for deeper trees.
  # orchestrator_enabled: true              # Disable to force all children to leaf role.
  model: "google/gemini-3-flash-preview"             # Optional provider/model override
  provider: "openrouter"                             # Optional built-in provider
  api_mode: anthropic_messages                       # optional; auto-detected from base_url for anthropic_messages endpoints

# Or use a direct custom endpoint instead of provider:
delegation:
  model: "qwen2.5-coder"
  base_url: "http://localhost:1234/v1"
  api_key: "local-key"
  # api_mode: "anthropic_messages"  # Optional. Wire protocol override for base_url ("chat_completions", "codex_responses", or "anthropic_messages"). Empty = auto-detect from URL (e.g. /anthropic suffix). Set explicitly for endpoints the heuristic can't classify (Azure AI Foundry, MiniMax, Zhipu GLM, LiteLLM proxies, …).

When base_url points at an Anthropic-compatible endpoint — for example a path ending in /anthropic, an Azure Foundry Claude route, or a MiniMax /anthropic proxy — api_mode is auto-detected as anthropic_messages so the subagent uses the right wire format without you setting anything. Set api_mode explicitly when the auto-detection guess is wrong (rare).

tip

The agent handles delegation automatically based on the task complexity. You don't need to explicitly ask it to delegate — it will do so when it makes sense.