群集#

Swarm 实现了一个团队，其中代理可以根据其能力将任务转交给其他代理。 这是由OpenAI首次在Swarm中引入的多代理设计模式。 关键思想是让代理使用特殊工具调用将任务委托给其他代理，同时所有代理共享相同的消息上下文。 这使得代理能够就任务规划做出本地决策，而不是依赖于如SelectorGroupChat中的中央协调器。

注意

Swarm 是一个高级API。如果你需要更多的控制和定制，而这个API不支持，你可以查看Core API文档中的Handoff Pattern，并实现你自己的Swarm模式。

它是如何工作的？#

其核心是，Swarm团队是一个群聊，代理们轮流生成响应。类似于SelectorGroupChat和RoundRobinGroupChat，参与代理会广播他们的响应，因此所有代理共享相同的消息上下文。

与其他两个群聊团队不同，在每一轮中， 发言代理的选择基于上下文中最近的 HandoffMessage 消息。 这自然要求团队中的每个代理都能生成 HandoffMessage 来指示 它将交接给的其他代理。

对于AssistantAgent，你可以设置handoffs参数来指定它可以交接给哪些代理。你可以使用Handoff来自定义消息内容和交接行为。

整体过程可以总结如下：

  1. 每个代理都有能力生成 HandoffMessage 来表明它可以交接给哪些其他代理。对于 AssistantAgent，这意味着设置 handoffs 参数。

  2. 当团队开始执行任务时，第一个发言的代理会对任务进行操作，并做出是否移交以及移交给谁的局部决策。

  3. 当一个代理生成一个HandoffMessage时，接收代理会在相同的消息上下文中接管任务。

  4. 该过程持续直到满足终止条件。

注意

AssistantAgent 使用模型的工具调用能力来生成交接。这意味着模型必须支持工具调用。如果模型支持并行工具调用，可能会同时生成多个交接。这可能导致意外行为。为了避免这种情况，您可以通过配置模型客户端来禁用并行工具调用。对于 OpenAIChatCompletionClient 和 AzureOpenAIChatCompletionClient，您可以在配置中设置 parallel_tool_calls=False。

在本节中，我们将向您展示两个如何使用 Swarm 团队的示例：

  1. 一个带有人员介入移交的客户支持团队。

  2. 一个自动化团队用于内容生成。

客户支持示例#

Customer Support

该系统实现了一个航班退款场景，包含两个代理:

  • 旅行代理: 处理一般的旅行和退款协调。

  • 航班退款处理员: 专门使用 refund_flight 工具处理航班退款。

此外，当代理交接给"用户"时，我们让用户与代理进行交互。

工作流程#

  1. 旅行代理启动对话并评估用户请求。

  2. 根据请求：

    • 对于退款相关任务，旅行代理将交接给航班退款专员。

    • 对于需要从客户处获取的信息，任何一个代理都可以转交给"user"。

  3. 航班退款处理器在适当时使用refund_flight工具处理退款。

  4. 如果一个代理交接给"user"，团队执行将会停止并等待用户输入响应。

  5. 当用户提供输入时，它将以HandoffMessage的形式发送回团队。该消息会定向到最初请求用户输入的代理。

  6. 流程将持续进行，直到旅行社确定任务完成并终止工作流程。

from typing import Any, Dict, List

from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.conditions import HandoffTermination, TextMentionTermination
from autogen_agentchat.messages import HandoffMessage
from autogen_agentchat.teams import Swarm
from autogen_agentchat.ui import Console
from autogen_ext.models.openai import OpenAIChatCompletionClient

工具#

def refund_flight(flight_id: str) -> str:
    """Refund a flight"""
    return f"Flight {flight_id} refunded"

代理#

model_client = OpenAIChatCompletionClient(
    model="gpt-4o",
    # api_key="YOUR_API_KEY",
)

travel_agent = AssistantAgent(
    "travel_agent",
    model_client=model_client,
    handoffs=["flights_refunder", "user"],
    system_message="""You are a travel agent.
    The flights_refunder is in charge of refunding flights.
    If you need information from the user, you must first send your message, then you can handoff to the user.
    Use TERMINATE when the travel planning is complete.""",
)

flights_refunder = AssistantAgent(
    "flights_refunder",
    model_client=model_client,
    handoffs=["travel_agent", "user"],
    tools=[refund_flight],
    system_message="""You are an agent specialized in refunding flights.
    You only need flight reference numbers to refund a flight.
    You have the ability to refund a flight using the refund_flight tool.
    If you need information from the user, you must first send your message, then you can handoff to the user.
    When the transaction is complete, handoff to the travel agent to finalize.""",
)

termination = HandoffTermination(target="user") | TextMentionTermination("TERMINATE")
team = Swarm([travel_agent, flights_refunder], termination_condition=termination)

task = "I need to refund my flight."


async def run_team_stream() -> None:
    task_result = await Console(team.run_stream(task=task))
    last_message = task_result.messages[-1]

    while isinstance(last_message, HandoffMessage) and last_message.target == "user":
        user_message = input("User: ")

        task_result = await Console(
            team.run_stream(task=HandoffMessage(source="user", target=last_message.source, content=user_message))
        )
        last_message = task_result.messages[-1]


# Use asyncio.run(...) if you are running this in a script.
await run_team_stream()

---------- user ----------
I need to refund my flight.
---------- travel_agent ----------
[FunctionCall(id='call_ZQ2rGjq4Z29pd0yP2sNcuyd2', arguments='{}', name='transfer_to_flights_refunder')]
[Prompt tokens: 119, Completion tokens: 14]
---------- travel_agent ----------
[FunctionExecutionResult(content='Transferred to flights_refunder, adopting the role of flights_refunder immediately.', call_id='call_ZQ2rGjq4Z29pd0yP2sNcuyd2')]
---------- travel_agent ----------
Transferred to flights_refunder, adopting the role of flights_refunder immediately.
---------- flights_refunder ----------
Could you please provide me with the flight reference number so I can process the refund for you?
[Prompt tokens: 191, Completion tokens: 20]
---------- flights_refunder ----------
[FunctionCall(id='call_1iRfzNpxTJhRTW2ww9aQJ8sK', arguments='{}', name='transfer_to_user')]
[Prompt tokens: 219, Completion tokens: 11]
---------- flights_refunder ----------
[FunctionExecutionResult(content='Transferred to user, adopting the role of user immediately.', call_id='call_1iRfzNpxTJhRTW2ww9aQJ8sK')]
---------- flights_refunder ----------
Transferred to user, adopting the role of user immediately.
---------- Summary ----------
Number of messages: 8
Finish reason: Handoff to user from flights_refunder detected.
Total prompt tokens: 529
Total completion tokens: 45
Duration: 2.05 seconds
---------- user ----------
Sure, it's 507811
---------- flights_refunder ----------
[FunctionCall(id='call_UKCsoEBdflkvpuT9Bi2xlvTd', arguments='{"flight_id":"507811"}', name='refund_flight')]
[Prompt tokens: 266, Completion tokens: 18]
---------- flights_refunder ----------
[FunctionExecutionResult(content='Flight 507811 refunded', call_id='call_UKCsoEBdflkvpuT9Bi2xlvTd')]
---------- flights_refunder ----------
Tool calls:
refund_flight({"flight_id":"507811"}) = Flight 507811 refunded
---------- flights_refunder ----------
[FunctionCall(id='call_MQ2CXR8UhVtjNc6jG3wSQp2W', arguments='{}', name='transfer_to_travel_agent')]
[Prompt tokens: 303, Completion tokens: 13]
---------- flights_refunder ----------
[FunctionExecutionResult(content='Transferred to travel_agent, adopting the role of travel_agent immediately.', call_id='call_MQ2CXR8UhVtjNc6jG3wSQp2W')]
---------- flights_refunder ----------
Transferred to travel_agent, adopting the role of travel_agent immediately.
---------- travel_agent ----------
Your flight with reference number 507811 has been successfully refunded. If you need anything else, feel free to let me know. Safe travels! TERMINATE
[Prompt tokens: 272, Completion tokens: 32]
---------- Summary ----------
Number of messages: 8
Finish reason: Text 'TERMINATE' mentioned
Total prompt tokens: 841
Total completion tokens: 63
Duration: 1.64 seconds

股票研究示例#

Stock Research

该系统旨在通过利用四个代理来执行股票研究任务：

  • Planner: 中央协调器，根据专家的专业知识将特定任务委托给专门的代理。Planner确保每个代理被高效利用，并监督整体工作流程。

  • 财务分析师: 一个专门负责分析财务指标和股票数据的代理，使用如get_stock_data等工具。

  • 新闻分析师: 一个专注于收集和总结与股票相关的近期新闻文章的代理，使用诸如get_news等工具。

  • 编写者: 负责将股票和新闻分析的结果汇编成一份连贯的最终报告的代理。

工作流程#

  1. Planner通过逐步将任务委派给适当的代理来启动研究过程。

  2. 每个代理独立执行其任务，并将其工作附加到共享的消息线程/历史记录中。与直接向规划者返回结果不同，所有代理都会贡献并从这个共享消息历史记录中读取信息。当代理使用LLM生成他们的工作时，他们可以访问这个共享的消息历史记录，这提供了上下文并有助于跟踪任务的整体进度。

  3. 一旦代理完成任务，它将控制权交还给计划器。

  4. 该过程将持续进行，直到规划者确定所有必要任务已完成并决定终止工作流程。

工具#

async def get_stock_data(symbol: str) -> Dict[str, Any]:
    """Get stock market data for a given symbol"""
    return {"price": 180.25, "volume": 1000000, "pe_ratio": 65.4, "market_cap": "700B"}


async def get_news(query: str) -> List[Dict[str, str]]:
    """Get recent news articles about a company"""
    return [
        {
            "title": "Tesla Expands Cybertruck Production",
            "date": "2024-03-20",
            "summary": "Tesla ramps up Cybertruck manufacturing capacity at Gigafactory Texas, aiming to meet strong demand.",
        },
        {
            "title": "Tesla FSD Beta Shows Promise",
            "date": "2024-03-19",
            "summary": "Latest Full Self-Driving beta demonstrates significant improvements in urban navigation and safety features.",
        },
        {
            "title": "Model Y Dominates Global EV Sales",
            "date": "2024-03-18",
            "summary": "Tesla's Model Y becomes best-selling electric vehicle worldwide, capturing significant market share.",
        },
    ]

model_client = OpenAIChatCompletionClient(
    model="gpt-4o",
    # api_key="YOUR_API_KEY",
)

planner = AssistantAgent(
    "planner",
    model_client=model_client,
    handoffs=["financial_analyst", "news_analyst", "writer"],
    system_message="""You are a research planning coordinator.
    Coordinate market research by delegating to specialized agents:
    - Financial Analyst: For stock data analysis
    - News Analyst: For news gathering and analysis
    - Writer: For compiling final report
    Always send your plan first, then handoff to appropriate agent.
    Always handoff to a single agent at a time.
    Use TERMINATE when research is complete.""",
)

financial_analyst = AssistantAgent(
    "financial_analyst",
    model_client=model_client,
    handoffs=["planner"],
    tools=[get_stock_data],
    system_message="""You are a financial analyst.
    Analyze stock market data using the get_stock_data tool.
    Provide insights on financial metrics.
    Always handoff back to planner when analysis is complete.""",
)

news_analyst = AssistantAgent(
    "news_analyst",
    model_client=model_client,
    handoffs=["planner"],
    tools=[get_news],
    system_message="""You are a news analyst.
    Gather and analyze relevant news using the get_news tool.
    Summarize key market insights from news.
    Always handoff back to planner when analysis is complete.""",
)

writer = AssistantAgent(
    "writer",
    model_client=model_client,
    handoffs=["planner"],
    system_message="""You are a financial report writer.
    Compile research findings into clear, concise reports.
    Always handoff back to planner when writing is complete.""",
)

# Define termination condition
text_termination = TextMentionTermination("TERMINATE")
termination = text_termination

research_team = Swarm(
    participants=[planner, financial_analyst, news_analyst, writer], termination_condition=termination
)

task = "Conduct market research for TSLA stock"
await Console(research_team.run_stream(task=task))

---------- user ----------
Conduct market research for TSLA stock
---------- planner ----------
[FunctionCall(id='call_BX5QaRuhmB8CxTsBlqCUIXPb', arguments='{}', name='transfer_to_financial_analyst')]
[Prompt tokens: 169, Completion tokens: 166]
---------- planner ----------
[FunctionExecutionResult(content='Transferred to financial_analyst, adopting the role of financial_analyst immediately.', call_id='call_BX5QaRuhmB8CxTsBlqCUIXPb')]
---------- planner ----------
Transferred to financial_analyst, adopting the role of financial_analyst immediately.
---------- financial_analyst ----------
[FunctionCall(id='call_SAXy1ebtA9mnaZo4ztpD2xHA', arguments='{"symbol":"TSLA"}', name='get_stock_data')]
[Prompt tokens: 136, Completion tokens: 16]
---------- financial_analyst ----------
[FunctionExecutionResult(content="{'price': 180.25, 'volume': 1000000, 'pe_ratio': 65.4, 'market_cap': '700B'}", call_id='call_SAXy1ebtA9mnaZo4ztpD2xHA')]
---------- financial_analyst ----------
Tool calls:
get_stock_data({"symbol":"TSLA"}) = {'price': 180.25, 'volume': 1000000, 'pe_ratio': 65.4, 'market_cap': '700B'}
---------- financial_analyst ----------
[FunctionCall(id='call_IsdcFUfBVmtcVzfSuwQpeAwl', arguments='{}', name='transfer_to_planner')]
[Prompt tokens: 199, Completion tokens: 337]
---------- financial_analyst ----------
[FunctionExecutionResult(content='Transferred to planner, adopting the role of planner immediately.', call_id='call_IsdcFUfBVmtcVzfSuwQpeAwl')]
---------- financial_analyst ----------
Transferred to planner, adopting the role of planner immediately.
---------- planner ----------
[FunctionCall(id='call_tN5goNFahrdcSfKnQqT0RONN', arguments='{}', name='transfer_to_news_analyst')]
[Prompt tokens: 291, Completion tokens: 14]
---------- planner ----------
[FunctionExecutionResult(content='Transferred to news_analyst, adopting the role of news_analyst immediately.', call_id='call_tN5goNFahrdcSfKnQqT0RONN')]
---------- planner ----------
Transferred to news_analyst, adopting the role of news_analyst immediately.
---------- news_analyst ----------
[FunctionCall(id='call_Owjw6ZbiPdJgNWMHWxhCKgsp', arguments='{"query":"Tesla market news"}', name='get_news')]
[Prompt tokens: 235, Completion tokens: 16]
---------- news_analyst ----------
[FunctionExecutionResult(content='[{\'title\': \'Tesla Expands Cybertruck Production\', \'date\': \'2024-03-20\', \'summary\': \'Tesla ramps up Cybertruck manufacturing capacity at Gigafactory Texas, aiming to meet strong demand.\'}, {\'title\': \'Tesla FSD Beta Shows Promise\', \'date\': \'2024-03-19\', \'summary\': \'Latest Full Self-Driving beta demonstrates significant improvements in urban navigation and safety features.\'}, {\'title\': \'Model Y Dominates Global EV Sales\', \'date\': \'2024-03-18\', \'summary\': "Tesla\'s Model Y becomes best-selling electric vehicle worldwide, capturing significant market share."}]', call_id='call_Owjw6ZbiPdJgNWMHWxhCKgsp')]
---------- news_analyst ----------
Tool calls:
get_news({"query":"Tesla market news"}) = [{'title': 'Tesla Expands Cybertruck Production', 'date': '2024-03-20', 'summary': 'Tesla ramps up Cybertruck manufacturing capacity at Gigafactory Texas, aiming to meet strong demand.'}, {'title': 'Tesla FSD Beta Shows Promise', 'date': '2024-03-19', 'summary': 'Latest Full Self-Driving beta demonstrates significant improvements in urban navigation and safety features.'}, {'title': 'Model Y Dominates Global EV Sales', 'date': '2024-03-18', 'summary': "Tesla's Model Y becomes best-selling electric vehicle worldwide, capturing significant market share."}]
---------- news_analyst ----------
Here are some of the key market insights regarding Tesla (TSLA):

1. **Expansion in Cybertruck Production**: Tesla has increased its Cybertruck production capacity at the Gigafactory in Texas to meet the high demand. This move might positively impact Tesla's revenues if the demand for the Cybertruck continues to grow.

2. **Advancements in Full Self-Driving (FSD) Technology**: The recent beta release of Tesla's Full Self-Driving software shows significant advancements, particularly in urban navigation and safety. Progress in this area could enhance Tesla's competitive edge in the autonomous driving sector.

3. **Dominance of Model Y in EV Sales**: Tesla's Model Y has become the best-selling electric vehicle globally, capturing a substantial market share. Such strong sales performance reinforces Tesla's leadership in the electric vehicle market.

These developments reflect Tesla's ongoing innovation and ability to capture market demand, which could positively influence its stock performance and market position. 

I will now hand off back to the planner.
[Prompt tokens: 398, Completion tokens: 203]
---------- news_analyst ----------
[FunctionCall(id='call_pn7y6PKsBspWA17uOh3AKNMT', arguments='{}', name='transfer_to_planner')]
[Prompt tokens: 609, Completion tokens: 12]
---------- news_analyst ----------
[FunctionExecutionResult(content='Transferred to planner, adopting the role of planner immediately.', call_id='call_pn7y6PKsBspWA17uOh3AKNMT')]
---------- news_analyst ----------
Transferred to planner, adopting the role of planner immediately.
---------- planner ----------
[FunctionCall(id='call_MmXyWuD2uJT64ZdVI5NfhYdX', arguments='{}', name='transfer_to_writer')]
[Prompt tokens: 722, Completion tokens: 11]
---------- planner ----------
[FunctionExecutionResult(content='Transferred to writer, adopting the role of writer immediately.', call_id='call_MmXyWuD2uJT64ZdVI5NfhYdX')]
---------- planner ----------
Transferred to writer, adopting the role of writer immediately.
---------- writer ----------
[FunctionCall(id='call_Pdgu39O6GMYplBiB8jp3uyN3', arguments='{}', name='transfer_to_planner')]
[Prompt tokens: 599, Completion tokens: 323]
---------- writer ----------
[FunctionExecutionResult(content='Transferred to planner, adopting the role of planner immediately.', call_id='call_Pdgu39O6GMYplBiB8jp3uyN3')]
---------- writer ----------
Transferred to planner, adopting the role of planner immediately.
---------- planner ----------
TERMINATE
[Prompt tokens: 772, Completion tokens: 4]
---------- Summary ----------
Number of messages: 27
Finish reason: Text 'TERMINATE' mentioned
Total prompt tokens: 4130
Total completion tokens: 1102
Duration: 17.74 seconds

TaskResult(messages=[TextMessage(source='user', models_usage=None, content='Conduct market research for TSLA stock', type='TextMessage'), ToolCallRequestEvent(source='planner', models_usage=RequestUsage(prompt_tokens=169, completion_tokens=166), content=[FunctionCall(id='call_BX5QaRuhmB8CxTsBlqCUIXPb', arguments='{}', name='transfer_to_financial_analyst')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='planner', models_usage=None, content=[FunctionExecutionResult(content='Transferred to financial_analyst, adopting the role of financial_analyst immediately.', call_id='call_BX5QaRuhmB8CxTsBlqCUIXPb')], type='ToolCallExecutionEvent'), HandoffMessage(source='planner', models_usage=None, target='financial_analyst', content='Transferred to financial_analyst, adopting the role of financial_analyst immediately.', type='HandoffMessage'), ToolCallRequestEvent(source='financial_analyst', models_usage=RequestUsage(prompt_tokens=136, completion_tokens=16), content=[FunctionCall(id='call_SAXy1ebtA9mnaZo4ztpD2xHA', arguments='{"symbol":"TSLA"}', name='get_stock_data')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='financial_analyst', models_usage=None, content=[FunctionExecutionResult(content="{'price': 180.25, 'volume': 1000000, 'pe_ratio': 65.4, 'market_cap': '700B'}", call_id='call_SAXy1ebtA9mnaZo4ztpD2xHA')], type='ToolCallExecutionEvent'), TextMessage(source='financial_analyst', models_usage=None, content='Tool calls:\nget_stock_data({"symbol":"TSLA"}) = {\'price\': 180.25, \'volume\': 1000000, \'pe_ratio\': 65.4, \'market_cap\': \'700B\'}', type='TextMessage'), ToolCallRequestEvent(source='financial_analyst', models_usage=RequestUsage(prompt_tokens=199, completion_tokens=337), content=[FunctionCall(id='call_IsdcFUfBVmtcVzfSuwQpeAwl', arguments='{}', name='transfer_to_planner')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='financial_analyst', models_usage=None, content=[FunctionExecutionResult(content='Transferred to planner, adopting the role of planner immediately.', call_id='call_IsdcFUfBVmtcVzfSuwQpeAwl')], type='ToolCallExecutionEvent'), HandoffMessage(source='financial_analyst', models_usage=None, target='planner', content='Transferred to planner, adopting the role of planner immediately.', type='HandoffMessage'), ToolCallRequestEvent(source='planner', models_usage=RequestUsage(prompt_tokens=291, completion_tokens=14), content=[FunctionCall(id='call_tN5goNFahrdcSfKnQqT0RONN', arguments='{}', name='transfer_to_news_analyst')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='planner', models_usage=None, content=[FunctionExecutionResult(content='Transferred to news_analyst, adopting the role of news_analyst immediately.', call_id='call_tN5goNFahrdcSfKnQqT0RONN')], type='ToolCallExecutionEvent'), HandoffMessage(source='planner', models_usage=None, target='news_analyst', content='Transferred to news_analyst, adopting the role of news_analyst immediately.', type='HandoffMessage'), ToolCallRequestEvent(source='news_analyst', models_usage=RequestUsage(prompt_tokens=235, completion_tokens=16), content=[FunctionCall(id='call_Owjw6ZbiPdJgNWMHWxhCKgsp', arguments='{"query":"Tesla market news"}', name='get_news')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='news_analyst', models_usage=None, content=[FunctionExecutionResult(content='[{\'title\': \'Tesla Expands Cybertruck Production\', \'date\': \'2024-03-20\', \'summary\': \'Tesla ramps up Cybertruck manufacturing capacity at Gigafactory Texas, aiming to meet strong demand.\'}, {\'title\': \'Tesla FSD Beta Shows Promise\', \'date\': \'2024-03-19\', \'summary\': \'Latest Full Self-Driving beta demonstrates significant improvements in urban navigation and safety features.\'}, {\'title\': \'Model Y Dominates Global EV Sales\', \'date\': \'2024-03-18\', \'summary\': "Tesla\'s Model Y becomes best-selling electric vehicle worldwide, capturing significant market share."}]', call_id='call_Owjw6ZbiPdJgNWMHWxhCKgsp')], type='ToolCallExecutionEvent'), TextMessage(source='news_analyst', models_usage=None, content='Tool calls:\nget_news({"query":"Tesla market news"}) = [{\'title\': \'Tesla Expands Cybertruck Production\', \'date\': \'2024-03-20\', \'summary\': \'Tesla ramps up Cybertruck manufacturing capacity at Gigafactory Texas, aiming to meet strong demand.\'}, {\'title\': \'Tesla FSD Beta Shows Promise\', \'date\': \'2024-03-19\', \'summary\': \'Latest Full Self-Driving beta demonstrates significant improvements in urban navigation and safety features.\'}, {\'title\': \'Model Y Dominates Global EV Sales\', \'date\': \'2024-03-18\', \'summary\': "Tesla\'s Model Y becomes best-selling electric vehicle worldwide, capturing significant market share."}]', type='TextMessage'), TextMessage(source='news_analyst', models_usage=RequestUsage(prompt_tokens=398, completion_tokens=203), content="Here are some of the key market insights regarding Tesla (TSLA):\n\n1. **Expansion in Cybertruck Production**: Tesla has increased its Cybertruck production capacity at the Gigafactory in Texas to meet the high demand. This move might positively impact Tesla's revenues if the demand for the Cybertruck continues to grow.\n\n2. **Advancements in Full Self-Driving (FSD) Technology**: The recent beta release of Tesla's Full Self-Driving software shows significant advancements, particularly in urban navigation and safety. Progress in this area could enhance Tesla's competitive edge in the autonomous driving sector.\n\n3. **Dominance of Model Y in EV Sales**: Tesla's Model Y has become the best-selling electric vehicle globally, capturing a substantial market share. Such strong sales performance reinforces Tesla's leadership in the electric vehicle market.\n\nThese developments reflect Tesla's ongoing innovation and ability to capture market demand, which could positively influence its stock performance and market position. \n\nI will now hand off back to the planner.", type='TextMessage'), ToolCallRequestEvent(source='news_analyst', models_usage=RequestUsage(prompt_tokens=609, completion_tokens=12), content=[FunctionCall(id='call_pn7y6PKsBspWA17uOh3AKNMT', arguments='{}', name='transfer_to_planner')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='news_analyst', models_usage=None, content=[FunctionExecutionResult(content='Transferred to planner, adopting the role of planner immediately.', call_id='call_pn7y6PKsBspWA17uOh3AKNMT')], type='ToolCallExecutionEvent'), HandoffMessage(source='news_analyst', models_usage=None, target='planner', content='Transferred to planner, adopting the role of planner immediately.', type='HandoffMessage'), ToolCallRequestEvent(source='planner', models_usage=RequestUsage(prompt_tokens=722, completion_tokens=11), content=[FunctionCall(id='call_MmXyWuD2uJT64ZdVI5NfhYdX', arguments='{}', name='transfer_to_writer')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='planner', models_usage=None, content=[FunctionExecutionResult(content='Transferred to writer, adopting the role of writer immediately.', call_id='call_MmXyWuD2uJT64ZdVI5NfhYdX')], type='ToolCallExecutionEvent'), HandoffMessage(source='planner', models_usage=None, target='writer', content='Transferred to writer, adopting the role of writer immediately.', type='HandoffMessage'), ToolCallRequestEvent(source='writer', models_usage=RequestUsage(prompt_tokens=599, completion_tokens=323), content=[FunctionCall(id='call_Pdgu39O6GMYplBiB8jp3uyN3', arguments='{}', name='transfer_to_planner')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='writer', models_usage=None, content=[FunctionExecutionResult(content='Transferred to planner, adopting the role of planner immediately.', call_id='call_Pdgu39O6GMYplBiB8jp3uyN3')], type='ToolCallExecutionEvent'), HandoffMessage(source='writer', models_usage=None, target='planner', content='Transferred to planner, adopting the role of planner immediately.', type='HandoffMessage'), TextMessage(source='planner', models_usage=RequestUsage(prompt_tokens=772, completion_tokens=4), content='TERMINATE', type='TextMessage')], stop_reason="Text 'TERMINATE' mentioned")