241 lines
8.4 KiB
Python
241 lines
8.4 KiB
Python
"""Subagent manager for background task execution."""
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import asyncio
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import json
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import uuid
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from pathlib import Path
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from typing import Any
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from loguru import logger
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from nanobot.bus.events import InboundMessage
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from nanobot.bus.queue import MessageBus
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from nanobot.providers.base import LLMProvider
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from nanobot.agent.tools.registry import ToolRegistry
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from nanobot.agent.tools.filesystem import ReadFileTool, WriteFileTool, ListDirTool
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from nanobot.agent.tools.shell import ExecTool
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from nanobot.agent.tools.web import WebSearchTool, WebFetchTool
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class SubagentManager:
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"""
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Manages background subagent execution.
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Subagents are lightweight agent instances that run in the background
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to handle specific tasks. They share the same LLM provider but have
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isolated context and a focused system prompt.
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"""
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def __init__(
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self,
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provider: LLMProvider,
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workspace: Path,
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bus: MessageBus,
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model: str | None = None,
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brave_api_key: str | None = None,
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exec_config: "ExecToolConfig | None" = None,
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):
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from nanobot.config.schema import ExecToolConfig
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self.provider = provider
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self.workspace = workspace
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self.bus = bus
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self.model = model or provider.get_default_model()
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self.brave_api_key = brave_api_key
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self.exec_config = exec_config or ExecToolConfig()
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self._running_tasks: dict[str, asyncio.Task[None]] = {}
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async def spawn(
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self,
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task: str,
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label: str | None = None,
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origin_channel: str = "cli",
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origin_chat_id: str = "direct",
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) -> str:
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"""
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Spawn a subagent to execute a task in the background.
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Args:
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task: The task description for the subagent.
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label: Optional human-readable label for the task.
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origin_channel: The channel to announce results to.
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origin_chat_id: The chat ID to announce results to.
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Returns:
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Status message indicating the subagent was started.
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"""
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task_id = str(uuid.uuid4())[:8]
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display_label = label or task[:30] + ("..." if len(task) > 30 else "")
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origin = {
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"channel": origin_channel,
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"chat_id": origin_chat_id,
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}
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# Create background task
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bg_task = asyncio.create_task(
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self._run_subagent(task_id, task, display_label, origin)
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)
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self._running_tasks[task_id] = bg_task
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# Cleanup when done
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bg_task.add_done_callback(lambda _: self._running_tasks.pop(task_id, None))
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logger.info(f"Spawned subagent [{task_id}]: {display_label}")
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return f"Subagent [{display_label}] started (id: {task_id}). I'll notify you when it completes."
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async def _run_subagent(
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self,
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task_id: str,
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task: str,
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label: str,
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origin: dict[str, str],
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) -> None:
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"""Execute the subagent task and announce the result."""
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logger.info(f"Subagent [{task_id}] starting task: {label}")
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try:
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# Build subagent tools (no message tool, no spawn tool)
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tools = ToolRegistry()
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tools.register(ReadFileTool())
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tools.register(WriteFileTool())
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tools.register(ListDirTool())
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tools.register(ExecTool(
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working_dir=str(self.workspace),
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timeout=self.exec_config.timeout,
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restrict_to_workspace=self.exec_config.restrict_to_workspace,
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))
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tools.register(WebSearchTool(api_key=self.brave_api_key))
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tools.register(WebFetchTool())
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# Build messages with subagent-specific prompt
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system_prompt = self._build_subagent_prompt(task)
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messages: list[dict[str, Any]] = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": task},
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]
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# Run agent loop (limited iterations)
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max_iterations = 15
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iteration = 0
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final_result: str | None = None
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while iteration < max_iterations:
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iteration += 1
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response = await self.provider.chat(
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messages=messages,
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tools=tools.get_definitions(),
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model=self.model,
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)
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if response.has_tool_calls:
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# Add assistant message with tool calls
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tool_call_dicts = [
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{
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"id": tc.id,
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"type": "function",
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"function": {
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"name": tc.name,
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"arguments": json.dumps(tc.arguments),
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},
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}
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for tc in response.tool_calls
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]
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messages.append({
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"role": "assistant",
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"content": response.content or "",
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"tool_calls": tool_call_dicts,
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})
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# Execute tools
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for tool_call in response.tool_calls:
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logger.debug(f"Subagent [{task_id}] executing: {tool_call.name}")
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result = await tools.execute(tool_call.name, tool_call.arguments)
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messages.append({
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"role": "tool",
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"tool_call_id": tool_call.id,
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"name": tool_call.name,
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"content": result,
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})
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else:
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final_result = response.content
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break
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if final_result is None:
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final_result = "Task completed but no final response was generated."
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logger.info(f"Subagent [{task_id}] completed successfully")
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await self._announce_result(task_id, label, task, final_result, origin, "ok")
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except Exception as e:
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error_msg = f"Error: {str(e)}"
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logger.error(f"Subagent [{task_id}] failed: {e}")
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await self._announce_result(task_id, label, task, error_msg, origin, "error")
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async def _announce_result(
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self,
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task_id: str,
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label: str,
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task: str,
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result: str,
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origin: dict[str, str],
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status: str,
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) -> None:
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"""Announce the subagent result to the main agent via the message bus."""
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status_text = "completed successfully" if status == "ok" else "failed"
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announce_content = f"""[Subagent '{label}' {status_text}]
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Task: {task}
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Result:
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{result}
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Summarize this naturally for the user. Keep it brief (1-2 sentences). Do not mention technical details like "subagent" or task IDs."""
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# Inject as system message to trigger main agent
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msg = InboundMessage(
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channel="system",
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sender_id="subagent",
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chat_id=f"{origin['channel']}:{origin['chat_id']}",
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content=announce_content,
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)
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await self.bus.publish_inbound(msg)
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logger.debug(f"Subagent [{task_id}] announced result to {origin['channel']}:{origin['chat_id']}")
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def _build_subagent_prompt(self, task: str) -> str:
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"""Build a focused system prompt for the subagent."""
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return f"""# Subagent
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You are a subagent spawned by the main agent to complete a specific task.
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## Your Task
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{task}
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## Rules
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1. Stay focused - complete only the assigned task, nothing else
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2. Your final response will be reported back to the main agent
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3. Do not initiate conversations or take on side tasks
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4. Be concise but informative in your findings
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## What You Can Do
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- Read and write files in the workspace
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- Execute shell commands
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- Search the web and fetch web pages
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- Complete the task thoroughly
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## What You Cannot Do
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- Send messages directly to users (no message tool available)
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- Spawn other subagents
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- Access the main agent's conversation history
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## Workspace
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Your workspace is at: {self.workspace}
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When you have completed the task, provide a clear summary of your findings or actions."""
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def get_running_count(self) -> int:
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"""Return the number of currently running subagents."""
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return len(self._running_tasks)
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