Swarm#

Swarm implementiert ein Team, in dem Agenten Aufgaben basierend auf ihren Fähigkeiten an andere Agenten übergeben können. Es handelt sich um ein Multi-Agenten-Entwurfsmuster, das erstmals von OpenAI in Swarm eingeführt wurde. Die Kernidee besteht darin, dass Agenten Aufgaben über einen speziellen Tool-Aufruf an andere Agenten delegieren können, während alle Agenten den gleichen Nachrichtenkontext teilen. Dies ermöglicht es Agenten, lokale Entscheidungen über die Aufgabenplanung zu treffen, anstatt sich auf einen zentralen Orchestrator zu verlassen, wie es bei SelectorGroupChat der Fall ist.

Hinweis

Swarm ist eine High-Level-API. Wenn Sie mehr Kontrolle und Anpassungsmöglichkeiten benötigen, die von dieser API nicht unterstützt werden, können Sie sich das Handoff Pattern in der Kern-API-Dokumentation ansehen und Ihre eigene Version des Swarm-Musters implementieren.

Wie funktioniert es?#

Im Kern ist das Swarm-Team ein Gruppenchat, in dem Agenten abwechselnd eine Antwort generieren. Ähnlich wie bei SelectorGroupChat und RoundRobinGroupChat senden die teilnehmenden Agenten ihre Antworten als Broadcast aus, sodass alle Agenten den gleichen Nachrichtenkontext teilen.

Im Gegensatz zu den beiden anderen Gruppenchat-Teams wird der **Sprecher-Agent** in jeder Runde basierend auf der neuesten HandoffMessage im Kontext ausgewählt. Dies erfordert natürlich, dass jeder Agent im Team eine HandoffMessage generieren kann, um zu signalisieren, an welche anderen Agenten er übergibt.

Für AssistantAgent können Sie das Argument handoffs setzen, um anzugeben, an welche Agenten er übergeben kann. Sie können Handoff verwenden, um den Nachrichteninhalt und das Übergabeverhalten anzupassen.

Der Gesamtprozess kann wie folgt zusammengefasst werden

  1. Jeder Agent hat die Fähigkeit, HandoffMessage zu generieren, um zu signalisieren, an welche anderen Agenten er übergeben kann. Für AssistantAgent bedeutet dies das Setzen des Arguments handoffs.

  2. Wenn das Team eine Aufgabe beginnt, arbeiten die ersten Sprecher-Agenten an der Aufgabe und treffen lokale Entscheidungen, ob und an wen sie übergeben.

  3. Wenn ein Agent eine HandoffMessage generiert, übernimmt der empfangende Agent die Aufgabe mit demselben Nachrichtenkontext.

  4. Der Prozess wird fortgesetzt, bis eine Abbruchbedingung erfüllt ist.

Hinweis

Der AssistantAgent verwendet die Tool-Aufruffunktion des Modells, um Übergaben zu generieren. Das bedeutet, dass das Modell Tool-Aufrufe unterstützen muss. Wenn das Modell parallele Tool-Aufrufe durchführt, können gleichzeitig mehrere Übergaben generiert werden. Dies kann zu unerwartetem Verhalten führen. Um dies zu vermeiden, können Sie parallele Tool-Aufrufe deaktivieren, indem Sie den Modellclient konfigurieren. Für OpenAIChatCompletionClient und AzureOpenAIChatCompletionClient können Sie parallel_tool_calls=False in der Konfiguration setzen.

In diesem Abschnitt zeigen wir zwei Beispiele für die Verwendung des Swarm-Teams

  1. Ein Kundensupport-Team mit menschlicher Beteiligung (Human-in-the-Loop).

  2. Ein autonomes Team für die Content-Erstellung.

Beispiel Kundensupport#

Customer Support

Dieses System implementiert ein Flugerstattungsszenario mit zwei Agenten

  • Reisebüro: Kümmert sich um allgemeine Reise- und Erstattungsabwicklung.

  • Flug-Erstatters: Spezialisiert sich auf die Verarbeitung von Flugerstattungen mit dem refund_flight-Tool.

Zusätzlich lassen wir den Benutzer mit den Agenten interagieren, wenn Agenten an den "user" übergeben.

Arbeitsablauf#

  1. Das **Reisebüro** initiiert die Konversation und bewertet die Anfrage des Benutzers.

  2. Basierend auf der Anfrage

    • Für erstattungsbezogene Aufgaben übergibt das Reisebüro an den **Flug-Erstatter**.

    • Für Informationen, die vom Kunden benötigt werden, kann jeder Agent an den "user" übergeben.

  3. Der **Flug-Erstatter** bearbeitet Erstattungen bei Bedarf mit dem refund_flight-Tool.

  4. Wenn ein Agent an den "user" übergibt, stoppt die Teamausführung und wartet auf die Eingabe einer Antwort durch den Benutzer.

  5. Wenn der Benutzer eine Eingabe liefert, wird diese als HandoffMessage an das Team gesendet. Diese Nachricht ist an den Agenten gerichtet, der ursprünglich die Benutzereingabe angefordert hat.

  6. Der Prozess wird fortgesetzt, bis das Reisebüro feststellt, dass die Aufgabe abgeschlossen ist und den Arbeitsablauf beendet.

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

Tools#

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

Agenten#

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()
await model_client.close()

---------- 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

Beispiel Aktienanalyse#

Stock Research

Dieses System ist darauf ausgelegt, Aktienanalyseaufgaben durch die Nutzung von vier Agenten durchzuführen

  • Planer: Der zentrale Koordinator, der spezifische Aufgaben basierend auf der Expertise an spezialisierte Agenten delegiert. Der Planer stellt sicher, dass jeder Agent effizient eingesetzt wird und überwacht den gesamten Arbeitsablauf.

  • Finanzanalyst: Ein spezialisierter Agent, der für die Analyse von Finanzkennzahlen und Aktiendaten mit Tools wie get_stock_data verantwortlich ist.

  • Nachrichtenanalyst: Ein Agent, der sich auf das Sammeln und Zusammenfassen aktueller Nachrichtenartikel konzentriert, die für die Aktie relevant sind, und dabei Tools wie get_news verwendet.

  • Autor: Ein Agent, der beauftragt ist, die Ergebnisse der Aktien- und Nachrichtenanalyse zu einem kohärenten Abschlussbericht zusammenzufassen.

Arbeitsablauf#

  1. Der **Planer** initiiert den Rechercheprozess, indem er die Aufgaben schrittweise an die entsprechenden Agenten delegiert.

  2. Jeder Agent führt seine Aufgabe unabhängig aus und hängt seine Arbeit an den gemeinsamen **Nachrichten-Thread/Verlauf** an. Anstatt Ergebnisse direkt an den Planer zurückzugeben, tragen alle Agenten zu diesem gemeinsamen Nachrichtenverlauf bei und lesen daraus. Wenn Agenten ihre Arbeit mit dem LLM generieren, haben sie Zugriff auf diesen gemeinsamen Nachrichtenverlauf, der Kontext liefert und hilft, den Gesamtfortschritt der Aufgabe zu verfolgen.

  3. Sobald ein Agent seine Aufgabe abgeschlossen hat, gibt er die Kontrolle an den Planer zurück.

  4. Der Prozess wird fortgesetzt, bis der Planer feststellt, dass alle erforderlichen Aufgaben abgeschlossen sind und beschließt, den Arbeitsablauf zu beenden.

Tools#

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))
await model_client.close()

---------- 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")