68ab37e891
Each subagent now gets its own IterationBudget instead of sharing the parent's. The per-subagent cap is controlled by delegation.max_iterations in config.yaml (default 50). Total iterations across parent + subagents can exceed the parent's max_iterations, but the user retains control via the config setting. Previously, subagents shared the parent's budget, so three parallel subagents configured for max_iterations=50 racing against a parent that already used 60 of 90 would each only get ~10 iterations. Inspired by PR #2928 (Bartok9) which identified the issue (#2873).
791 lines
31 KiB
Python
791 lines
31 KiB
Python
#!/usr/bin/env python3
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"""
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Delegate Tool -- Subagent Architecture
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Spawns child AIAgent instances with isolated context, restricted toolsets,
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and their own terminal sessions. Supports single-task and batch (parallel)
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modes. The parent blocks until all children complete.
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Each child gets:
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- A fresh conversation (no parent history)
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- Its own task_id (own terminal session, file ops cache)
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- A restricted toolset (configurable, with blocked tools always stripped)
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- A focused system prompt built from the delegated goal + context
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The parent's context only sees the delegation call and the summary result,
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never the child's intermediate tool calls or reasoning.
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"""
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import json
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import logging
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logger = logging.getLogger(__name__)
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import os
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import time
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from typing import Any, Dict, List, Optional
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# Tools that children must never have access to
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DELEGATE_BLOCKED_TOOLS = frozenset([
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"delegate_task", # no recursive delegation
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"clarify", # no user interaction
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"memory", # no writes to shared MEMORY.md
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"send_message", # no cross-platform side effects
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"execute_code", # children should reason step-by-step, not write scripts
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])
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MAX_CONCURRENT_CHILDREN = 3
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MAX_DEPTH = 2 # parent (0) -> child (1) -> grandchild rejected (2)
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DEFAULT_MAX_ITERATIONS = 50
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DEFAULT_TOOLSETS = ["terminal", "file", "web"]
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def check_delegate_requirements() -> bool:
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"""Delegation has no external requirements -- always available."""
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return True
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def _build_child_system_prompt(goal: str, context: Optional[str] = None) -> str:
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"""Build a focused system prompt for a child agent."""
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parts = [
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"You are a focused subagent working on a specific delegated task.",
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"",
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f"YOUR TASK:\n{goal}",
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]
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if context and context.strip():
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parts.append(f"\nCONTEXT:\n{context}")
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parts.append(
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"\nComplete this task using the tools available to you. "
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"When finished, provide a clear, concise summary of:\n"
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"- What you did\n"
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"- What you found or accomplished\n"
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"- Any files you created or modified\n"
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"- Any issues encountered\n\n"
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"Be thorough but concise -- your response is returned to the "
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"parent agent as a summary."
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)
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return "\n".join(parts)
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def _strip_blocked_tools(toolsets: List[str]) -> List[str]:
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"""Remove toolsets that contain only blocked tools."""
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blocked_toolset_names = {
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"delegation", "clarify", "memory", "code_execution",
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}
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return [t for t in toolsets if t not in blocked_toolset_names]
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def _build_child_progress_callback(task_index: int, parent_agent, task_count: int = 1) -> Optional[callable]:
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"""Build a callback that relays child agent tool calls to the parent display.
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Two display paths:
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CLI: prints tree-view lines above the parent's delegation spinner
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Gateway: batches tool names and relays to parent's progress callback
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Returns None if no display mechanism is available, in which case the
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child agent runs with no progress callback (identical to current behavior).
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"""
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spinner = getattr(parent_agent, '_delegate_spinner', None)
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parent_cb = getattr(parent_agent, 'tool_progress_callback', None)
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if not spinner and not parent_cb:
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return None # No display → no callback → zero behavior change
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# Show 1-indexed prefix only in batch mode (multiple tasks)
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prefix = f"[{task_index + 1}] " if task_count > 1 else ""
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# Gateway: batch tool names, flush periodically
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_BATCH_SIZE = 5
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_batch: List[str] = []
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def _callback(tool_name: str, preview: str = None):
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# Special "_thinking" event: model produced text content (reasoning)
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if tool_name == "_thinking":
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if spinner:
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short = (preview[:55] + "...") if preview and len(preview) > 55 else (preview or "")
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try:
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spinner.print_above(f" {prefix}├─ 💭 \"{short}\"")
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except Exception as e:
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logger.debug("Spinner print_above failed: %s", e)
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# Don't relay thinking to gateway (too noisy for chat)
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return
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# Regular tool call event
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if spinner:
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short = (preview[:35] + "...") if preview and len(preview) > 35 else (preview or "")
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from agent.display import get_tool_emoji
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emoji = get_tool_emoji(tool_name)
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line = f" {prefix}├─ {emoji} {tool_name}"
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if short:
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line += f" \"{short}\""
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try:
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spinner.print_above(line)
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except Exception as e:
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logger.debug("Spinner print_above failed: %s", e)
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if parent_cb:
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_batch.append(tool_name)
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if len(_batch) >= _BATCH_SIZE:
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summary = ", ".join(_batch)
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try:
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parent_cb("subagent_progress", f"🔀 {prefix}{summary}")
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except Exception as e:
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logger.debug("Parent callback failed: %s", e)
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_batch.clear()
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def _flush():
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"""Flush remaining batched tool names to gateway on completion."""
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if parent_cb and _batch:
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summary = ", ".join(_batch)
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try:
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parent_cb("subagent_progress", f"🔀 {prefix}{summary}")
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except Exception as e:
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logger.debug("Parent callback flush failed: %s", e)
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_batch.clear()
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_callback._flush = _flush
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return _callback
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def _build_child_agent(
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task_index: int,
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goal: str,
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context: Optional[str],
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toolsets: Optional[List[str]],
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model: Optional[str],
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max_iterations: int,
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parent_agent,
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# Credential overrides from delegation config (provider:model resolution)
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override_provider: Optional[str] = None,
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override_base_url: Optional[str] = None,
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override_api_key: Optional[str] = None,
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override_api_mode: Optional[str] = None,
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):
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"""
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Build a child AIAgent on the main thread (thread-safe construction).
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Returns the constructed child agent without running it.
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When override_* params are set (from delegation config), the child uses
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those credentials instead of inheriting from the parent. This enables
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routing subagents to a different provider:model pair (e.g. cheap/fast
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model on OpenRouter while the parent runs on Nous Portal).
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"""
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from run_agent import AIAgent
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import model_tools
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# When no explicit toolsets given, inherit from parent's enabled toolsets
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# so disabled tools (e.g. web) don't leak to subagents.
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if toolsets:
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child_toolsets = _strip_blocked_tools(toolsets)
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elif parent_agent and getattr(parent_agent, "enabled_toolsets", None):
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child_toolsets = _strip_blocked_tools(parent_agent.enabled_toolsets)
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else:
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child_toolsets = _strip_blocked_tools(DEFAULT_TOOLSETS)
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child_prompt = _build_child_system_prompt(goal, context)
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# Extract parent's API key so subagents inherit auth (e.g. Nous Portal).
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parent_api_key = getattr(parent_agent, "api_key", None)
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if (not parent_api_key) and hasattr(parent_agent, "_client_kwargs"):
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parent_api_key = parent_agent._client_kwargs.get("api_key")
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# Build progress callback to relay tool calls to parent display
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child_progress_cb = _build_child_progress_callback(task_index, parent_agent)
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# Each subagent gets its own iteration budget capped at max_iterations
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# (configurable via delegation.max_iterations, default 50). This means
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# total iterations across parent + subagents can exceed the parent's
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# max_iterations. The user controls the per-subagent cap in config.yaml.
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# Resolve effective credentials: config override > parent inherit
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effective_model = model or parent_agent.model
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effective_provider = override_provider or getattr(parent_agent, "provider", None)
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effective_base_url = override_base_url or parent_agent.base_url
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effective_api_key = override_api_key or parent_api_key
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effective_api_mode = override_api_mode or getattr(parent_agent, "api_mode", None)
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effective_acp_command = getattr(parent_agent, "acp_command", None)
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effective_acp_args = list(getattr(parent_agent, "acp_args", []) or [])
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child = AIAgent(
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base_url=effective_base_url,
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api_key=effective_api_key,
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model=effective_model,
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provider=effective_provider,
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api_mode=effective_api_mode,
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acp_command=effective_acp_command,
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acp_args=effective_acp_args,
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max_iterations=max_iterations,
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max_tokens=getattr(parent_agent, "max_tokens", None),
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reasoning_config=getattr(parent_agent, "reasoning_config", None),
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prefill_messages=getattr(parent_agent, "prefill_messages", None),
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enabled_toolsets=child_toolsets,
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quiet_mode=True,
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ephemeral_system_prompt=child_prompt,
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log_prefix=f"[subagent-{task_index}]",
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platform=parent_agent.platform,
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skip_context_files=True,
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skip_memory=True,
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clarify_callback=None,
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session_db=getattr(parent_agent, '_session_db', None),
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providers_allowed=parent_agent.providers_allowed,
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providers_ignored=parent_agent.providers_ignored,
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providers_order=parent_agent.providers_order,
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provider_sort=parent_agent.provider_sort,
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tool_progress_callback=child_progress_cb,
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iteration_budget=None, # fresh budget per subagent
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)
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# Set delegation depth so children can't spawn grandchildren
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child._delegate_depth = getattr(parent_agent, '_delegate_depth', 0) + 1
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# Register child for interrupt propagation
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if hasattr(parent_agent, '_active_children'):
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lock = getattr(parent_agent, '_active_children_lock', None)
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if lock:
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with lock:
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parent_agent._active_children.append(child)
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else:
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parent_agent._active_children.append(child)
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return child
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def _run_single_child(
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task_index: int,
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goal: str,
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child=None,
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parent_agent=None,
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**_kwargs,
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) -> Dict[str, Any]:
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"""
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Run a pre-built child agent. Called from within a thread.
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Returns a structured result dict.
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"""
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child_start = time.monotonic()
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# Get the progress callback from the child agent
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child_progress_cb = getattr(child, 'tool_progress_callback', None)
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# Restore parent tool names using the value saved before child construction
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# mutated the global. This is the correct parent toolset, not the child's.
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import model_tools
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_saved_tool_names = getattr(child, "_delegate_saved_tool_names",
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list(model_tools._last_resolved_tool_names))
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try:
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result = child.run_conversation(user_message=goal)
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# Flush any remaining batched progress to gateway
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if child_progress_cb and hasattr(child_progress_cb, '_flush'):
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try:
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child_progress_cb._flush()
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except Exception as e:
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logger.debug("Progress callback flush failed: %s", e)
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duration = round(time.monotonic() - child_start, 2)
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summary = result.get("final_response") or ""
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completed = result.get("completed", False)
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interrupted = result.get("interrupted", False)
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api_calls = result.get("api_calls", 0)
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if interrupted:
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status = "interrupted"
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elif completed and summary:
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status = "completed"
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else:
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status = "failed"
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# Build tool trace from conversation messages (already in memory).
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# Uses tool_call_id to correctly pair parallel tool calls with results.
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tool_trace: list[Dict[str, Any]] = []
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trace_by_id: Dict[str, Dict[str, Any]] = {}
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messages = result.get("messages") or []
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if isinstance(messages, list):
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for msg in messages:
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if not isinstance(msg, dict):
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continue
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if msg.get("role") == "assistant":
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for tc in (msg.get("tool_calls") or []):
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fn = tc.get("function", {})
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entry_t = {
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"tool": fn.get("name", "unknown"),
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"args_bytes": len(fn.get("arguments", "")),
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}
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tool_trace.append(entry_t)
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tc_id = tc.get("id")
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if tc_id:
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trace_by_id[tc_id] = entry_t
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elif msg.get("role") == "tool":
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content = msg.get("content", "")
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is_error = bool(
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content and "error" in content[:80].lower()
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)
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result_meta = {
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"result_bytes": len(content),
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"status": "error" if is_error else "ok",
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}
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# Match by tool_call_id for parallel calls
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tc_id = msg.get("tool_call_id")
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target = trace_by_id.get(tc_id) if tc_id else None
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if target is not None:
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target.update(result_meta)
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elif tool_trace:
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# Fallback for messages without tool_call_id
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tool_trace[-1].update(result_meta)
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# Determine exit reason
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if interrupted:
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exit_reason = "interrupted"
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elif completed:
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exit_reason = "completed"
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else:
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exit_reason = "max_iterations"
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# Extract token counts (safe for mock objects)
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_input_tokens = getattr(child, "session_prompt_tokens", 0)
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_output_tokens = getattr(child, "session_completion_tokens", 0)
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_model = getattr(child, "model", None)
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entry: Dict[str, Any] = {
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"task_index": task_index,
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"status": status,
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"summary": summary,
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"api_calls": api_calls,
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"duration_seconds": duration,
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"model": _model if isinstance(_model, str) else None,
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"exit_reason": exit_reason,
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"tokens": {
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"input": _input_tokens if isinstance(_input_tokens, (int, float)) else 0,
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"output": _output_tokens if isinstance(_output_tokens, (int, float)) else 0,
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},
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"tool_trace": tool_trace,
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}
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if status == "failed":
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entry["error"] = result.get("error", "Subagent did not produce a response.")
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return entry
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except Exception as exc:
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duration = round(time.monotonic() - child_start, 2)
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logging.exception(f"[subagent-{task_index}] failed")
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return {
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"task_index": task_index,
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"status": "error",
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"summary": None,
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"error": str(exc),
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"api_calls": 0,
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"duration_seconds": duration,
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}
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finally:
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# Restore the parent's tool names so the process-global is correct
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# for any subsequent execute_code calls or other consumers.
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import model_tools
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saved_tool_names = getattr(child, "_delegate_saved_tool_names", None)
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if isinstance(saved_tool_names, list):
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model_tools._last_resolved_tool_names = list(saved_tool_names)
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# Unregister child from interrupt propagation
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if hasattr(parent_agent, '_active_children'):
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try:
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lock = getattr(parent_agent, '_active_children_lock', None)
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if lock:
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with lock:
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parent_agent._active_children.remove(child)
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else:
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parent_agent._active_children.remove(child)
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except (ValueError, UnboundLocalError) as e:
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logger.debug("Could not remove child from active_children: %s", e)
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def delegate_task(
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goal: Optional[str] = None,
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context: Optional[str] = None,
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toolsets: Optional[List[str]] = None,
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tasks: Optional[List[Dict[str, Any]]] = None,
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max_iterations: Optional[int] = None,
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parent_agent=None,
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) -> str:
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"""
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Spawn one or more child agents to handle delegated tasks.
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Supports two modes:
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- Single: provide goal (+ optional context, toolsets)
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- Batch: provide tasks array [{goal, context, toolsets}, ...]
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Returns JSON with results array, one entry per task.
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"""
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if parent_agent is None:
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return json.dumps({"error": "delegate_task requires a parent agent context."})
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# Depth limit
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depth = getattr(parent_agent, '_delegate_depth', 0)
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if depth >= MAX_DEPTH:
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return json.dumps({
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"error": (
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f"Delegation depth limit reached ({MAX_DEPTH}). "
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"Subagents cannot spawn further subagents."
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)
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})
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# Load config
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cfg = _load_config()
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default_max_iter = cfg.get("max_iterations", DEFAULT_MAX_ITERATIONS)
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effective_max_iter = max_iterations or default_max_iter
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# Resolve delegation credentials (provider:model pair).
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# When delegation.provider is configured, this resolves the full credential
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# bundle (base_url, api_key, api_mode) via the same runtime provider system
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# used by CLI/gateway startup. When unconfigured, returns None values so
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# children inherit from the parent.
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try:
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creds = _resolve_delegation_credentials(cfg, parent_agent)
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except ValueError as exc:
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return json.dumps({"error": str(exc)})
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# Normalize to task list
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if tasks and isinstance(tasks, list):
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task_list = tasks[:MAX_CONCURRENT_CHILDREN]
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elif goal and isinstance(goal, str) and goal.strip():
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task_list = [{"goal": goal, "context": context, "toolsets": toolsets}]
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else:
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return json.dumps({"error": "Provide either 'goal' (single task) or 'tasks' (batch)."})
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if not task_list:
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return json.dumps({"error": "No tasks provided."})
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# Validate each task has a goal
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for i, task in enumerate(task_list):
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if not task.get("goal", "").strip():
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return json.dumps({"error": f"Task {i} is missing a 'goal'."})
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overall_start = time.monotonic()
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results = []
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n_tasks = len(task_list)
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# Track goal labels for progress display (truncated for readability)
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task_labels = [t["goal"][:40] for t in task_list]
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# Save parent tool names BEFORE any child construction mutates the global.
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# _build_child_agent() calls AIAgent() which calls get_tool_definitions(),
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# which overwrites model_tools._last_resolved_tool_names with child's toolset.
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import model_tools as _model_tools
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_parent_tool_names = list(_model_tools._last_resolved_tool_names)
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# Build all child agents on the main thread (thread-safe construction)
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# Wrapped in try/finally so the global is always restored even if a
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# child build raises (otherwise _last_resolved_tool_names stays corrupted).
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children = []
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try:
|
|
for i, t in enumerate(task_list):
|
|
child = _build_child_agent(
|
|
task_index=i, goal=t["goal"], context=t.get("context"),
|
|
toolsets=t.get("toolsets") or toolsets, model=creds["model"],
|
|
max_iterations=effective_max_iter, parent_agent=parent_agent,
|
|
override_provider=creds["provider"], override_base_url=creds["base_url"],
|
|
override_api_key=creds["api_key"],
|
|
override_api_mode=creds["api_mode"],
|
|
)
|
|
# Override with correct parent tool names (before child construction mutated global)
|
|
child._delegate_saved_tool_names = _parent_tool_names
|
|
children.append((i, t, child))
|
|
finally:
|
|
# Authoritative restore: reset global to parent's tool names after all children built
|
|
_model_tools._last_resolved_tool_names = _parent_tool_names
|
|
|
|
if n_tasks == 1:
|
|
# Single task -- run directly (no thread pool overhead)
|
|
_i, _t, child = children[0]
|
|
result = _run_single_child(0, _t["goal"], child, parent_agent)
|
|
results.append(result)
|
|
else:
|
|
# Batch -- run in parallel with per-task progress lines
|
|
completed_count = 0
|
|
spinner_ref = getattr(parent_agent, '_delegate_spinner', None)
|
|
|
|
with ThreadPoolExecutor(max_workers=MAX_CONCURRENT_CHILDREN) as executor:
|
|
futures = {}
|
|
for i, t, child in children:
|
|
future = executor.submit(
|
|
_run_single_child,
|
|
task_index=i,
|
|
goal=t["goal"],
|
|
child=child,
|
|
parent_agent=parent_agent,
|
|
)
|
|
futures[future] = i
|
|
|
|
for future in as_completed(futures):
|
|
try:
|
|
entry = future.result()
|
|
except Exception as exc:
|
|
idx = futures[future]
|
|
entry = {
|
|
"task_index": idx,
|
|
"status": "error",
|
|
"summary": None,
|
|
"error": str(exc),
|
|
"api_calls": 0,
|
|
"duration_seconds": 0,
|
|
}
|
|
results.append(entry)
|
|
completed_count += 1
|
|
|
|
# Print per-task completion line above the spinner
|
|
idx = entry["task_index"]
|
|
label = task_labels[idx] if idx < len(task_labels) else f"Task {idx}"
|
|
dur = entry.get("duration_seconds", 0)
|
|
status = entry.get("status", "?")
|
|
icon = "✓" if status == "completed" else "✗"
|
|
remaining = n_tasks - completed_count
|
|
completion_line = f"{icon} [{idx+1}/{n_tasks}] {label} ({dur}s)"
|
|
if spinner_ref:
|
|
try:
|
|
spinner_ref.print_above(completion_line)
|
|
except Exception:
|
|
print(f" {completion_line}")
|
|
else:
|
|
print(f" {completion_line}")
|
|
|
|
# Update spinner text to show remaining count
|
|
if spinner_ref and remaining > 0:
|
|
try:
|
|
spinner_ref.update_text(f"🔀 {remaining} task{'s' if remaining != 1 else ''} remaining")
|
|
except Exception as e:
|
|
logger.debug("Spinner update_text failed: %s", e)
|
|
|
|
# Sort by task_index so results match input order
|
|
results.sort(key=lambda r: r["task_index"])
|
|
|
|
total_duration = round(time.monotonic() - overall_start, 2)
|
|
|
|
return json.dumps({
|
|
"results": results,
|
|
"total_duration_seconds": total_duration,
|
|
}, ensure_ascii=False)
|
|
|
|
|
|
def _resolve_delegation_credentials(cfg: dict, parent_agent) -> dict:
|
|
"""Resolve credentials for subagent delegation.
|
|
|
|
If ``delegation.base_url`` is configured, subagents use that direct
|
|
OpenAI-compatible endpoint. Otherwise, if ``delegation.provider`` is
|
|
configured, the full credential bundle (base_url, api_key, api_mode,
|
|
provider) is resolved via the runtime provider system — the same path used
|
|
by CLI/gateway startup. This lets subagents run on a completely different
|
|
provider:model pair.
|
|
|
|
If neither base_url nor provider is configured, returns None values so the
|
|
child inherits everything from the parent agent.
|
|
|
|
Raises ValueError with a user-friendly message on credential failure.
|
|
"""
|
|
configured_model = str(cfg.get("model") or "").strip() or None
|
|
configured_provider = str(cfg.get("provider") or "").strip() or None
|
|
configured_base_url = str(cfg.get("base_url") or "").strip() or None
|
|
configured_api_key = str(cfg.get("api_key") or "").strip() or None
|
|
|
|
if configured_base_url:
|
|
api_key = (
|
|
configured_api_key
|
|
or os.getenv("OPENAI_API_KEY", "").strip()
|
|
)
|
|
if not api_key:
|
|
raise ValueError(
|
|
"Delegation base_url is configured but no API key was found. "
|
|
"Set delegation.api_key or OPENAI_API_KEY."
|
|
)
|
|
|
|
base_lower = configured_base_url.lower()
|
|
provider = "custom"
|
|
api_mode = "chat_completions"
|
|
if "chatgpt.com/backend-api/codex" in base_lower:
|
|
provider = "openai-codex"
|
|
api_mode = "codex_responses"
|
|
elif "api.anthropic.com" in base_lower:
|
|
provider = "anthropic"
|
|
api_mode = "anthropic_messages"
|
|
|
|
return {
|
|
"model": configured_model,
|
|
"provider": provider,
|
|
"base_url": configured_base_url,
|
|
"api_key": api_key,
|
|
"api_mode": api_mode,
|
|
}
|
|
|
|
if not configured_provider:
|
|
# No provider override — child inherits everything from parent
|
|
return {
|
|
"model": configured_model,
|
|
"provider": None,
|
|
"base_url": None,
|
|
"api_key": None,
|
|
"api_mode": None,
|
|
}
|
|
|
|
# Provider is configured — resolve full credentials
|
|
try:
|
|
from hermes_cli.runtime_provider import resolve_runtime_provider
|
|
runtime = resolve_runtime_provider(requested=configured_provider)
|
|
except Exception as exc:
|
|
raise ValueError(
|
|
f"Cannot resolve delegation provider '{configured_provider}': {exc}. "
|
|
f"Check that the provider is configured (API key set, valid provider name), "
|
|
f"or set delegation.base_url/delegation.api_key for a direct endpoint. "
|
|
f"Available providers: openrouter, nous, zai, kimi-coding, minimax."
|
|
) from exc
|
|
|
|
api_key = runtime.get("api_key", "")
|
|
if not api_key:
|
|
raise ValueError(
|
|
f"Delegation provider '{configured_provider}' resolved but has no API key. "
|
|
f"Set the appropriate environment variable or run 'hermes login'."
|
|
)
|
|
|
|
return {
|
|
"model": configured_model,
|
|
"provider": runtime.get("provider"),
|
|
"base_url": runtime.get("base_url"),
|
|
"api_key": api_key,
|
|
"api_mode": runtime.get("api_mode"),
|
|
"command": runtime.get("command"),
|
|
"args": list(runtime.get("args") or []),
|
|
}
|
|
|
|
|
|
def _load_config() -> dict:
|
|
"""Load delegation config from CLI_CONFIG or persistent config.
|
|
|
|
Checks the runtime config (cli.py CLI_CONFIG) first, then falls back
|
|
to the persistent config (hermes_cli/config.py load_config()) so that
|
|
``delegation.model`` / ``delegation.provider`` are picked up regardless
|
|
of the entry point (CLI, gateway, cron).
|
|
"""
|
|
try:
|
|
from cli import CLI_CONFIG
|
|
cfg = CLI_CONFIG.get("delegation", {})
|
|
if cfg:
|
|
return cfg
|
|
except Exception:
|
|
pass
|
|
try:
|
|
from hermes_cli.config import load_config
|
|
full = load_config()
|
|
return full.get("delegation", {})
|
|
except Exception:
|
|
return {}
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# OpenAI Function-Calling Schema
|
|
# ---------------------------------------------------------------------------
|
|
|
|
DELEGATE_TASK_SCHEMA = {
|
|
"name": "delegate_task",
|
|
"description": (
|
|
"Spawn one or more subagents to work on tasks in isolated contexts. "
|
|
"Each subagent gets its own conversation, terminal session, and toolset. "
|
|
"Only the final summary is returned -- intermediate tool results "
|
|
"never enter your context window.\n\n"
|
|
"TWO MODES (one of 'goal' or 'tasks' is required):\n"
|
|
"1. Single task: provide 'goal' (+ optional context, toolsets)\n"
|
|
"2. Batch (parallel): provide 'tasks' array with up to 3 items. "
|
|
"All run concurrently and results are returned together.\n\n"
|
|
"WHEN TO USE delegate_task:\n"
|
|
"- Reasoning-heavy subtasks (debugging, code review, research synthesis)\n"
|
|
"- Tasks that would flood your context with intermediate data\n"
|
|
"- Parallel independent workstreams (research A and B simultaneously)\n\n"
|
|
"WHEN NOT TO USE (use these instead):\n"
|
|
"- Mechanical multi-step work with no reasoning needed -> use execute_code\n"
|
|
"- Single tool call -> just call the tool directly\n"
|
|
"- Tasks needing user interaction -> subagents cannot use clarify\n\n"
|
|
"IMPORTANT:\n"
|
|
"- Subagents have NO memory of your conversation. Pass all relevant "
|
|
"info (file paths, error messages, constraints) via the 'context' field.\n"
|
|
"- Subagents CANNOT call: delegate_task, clarify, memory, send_message, "
|
|
"execute_code.\n"
|
|
"- Each subagent gets its own terminal session (separate working directory and state).\n"
|
|
"- Results are always returned as an array, one entry per task."
|
|
),
|
|
"parameters": {
|
|
"type": "object",
|
|
"properties": {
|
|
"goal": {
|
|
"type": "string",
|
|
"description": (
|
|
"What the subagent should accomplish. Be specific and "
|
|
"self-contained -- the subagent knows nothing about your "
|
|
"conversation history."
|
|
),
|
|
},
|
|
"context": {
|
|
"type": "string",
|
|
"description": (
|
|
"Background information the subagent needs: file paths, "
|
|
"error messages, project structure, constraints. The more "
|
|
"specific you are, the better the subagent performs."
|
|
),
|
|
},
|
|
"toolsets": {
|
|
"type": "array",
|
|
"items": {"type": "string"},
|
|
"description": (
|
|
"Toolsets to enable for this subagent. "
|
|
"Default: inherits your enabled toolsets. "
|
|
"Common patterns: ['terminal', 'file'] for code work, "
|
|
"['web'] for research, ['terminal', 'file', 'web'] for "
|
|
"full-stack tasks."
|
|
),
|
|
},
|
|
"tasks": {
|
|
"type": "array",
|
|
"items": {
|
|
"type": "object",
|
|
"properties": {
|
|
"goal": {"type": "string", "description": "Task goal"},
|
|
"context": {"type": "string", "description": "Task-specific context"},
|
|
"toolsets": {
|
|
"type": "array",
|
|
"items": {"type": "string"},
|
|
"description": "Toolsets for this specific task",
|
|
},
|
|
},
|
|
"required": ["goal"],
|
|
},
|
|
"maxItems": 3,
|
|
"description": (
|
|
"Batch mode: up to 3 tasks to run in parallel. Each gets "
|
|
"its own subagent with isolated context and terminal session. "
|
|
"When provided, top-level goal/context/toolsets are ignored."
|
|
),
|
|
},
|
|
"max_iterations": {
|
|
"type": "integer",
|
|
"description": (
|
|
"Max tool-calling turns per subagent (default: 50). "
|
|
"Only set lower for simple tasks."
|
|
),
|
|
},
|
|
},
|
|
"required": [],
|
|
},
|
|
}
|
|
|
|
|
|
# --- Registry ---
|
|
from tools.registry import registry
|
|
|
|
registry.register(
|
|
name="delegate_task",
|
|
toolset="delegation",
|
|
schema=DELEGATE_TASK_SCHEMA,
|
|
handler=lambda args, **kw: delegate_task(
|
|
goal=args.get("goal"),
|
|
context=args.get("context"),
|
|
toolsets=args.get("toolsets"),
|
|
tasks=args.get("tasks"),
|
|
max_iterations=args.get("max_iterations"),
|
|
parent_agent=kw.get("parent_agent")),
|
|
check_fn=check_delegate_requirements,
|
|
emoji="🔀",
|
|
)
|