diff --git a/python/packages/autogen-agentchat/src/autogen_agentchat/agents/_assistant_agent.py b/python/packages/autogen-agentchat/src/autogen_agentchat/agents/_assistant_agent.py index 14037d8cdf02..9450d4512e38 100644 --- a/python/packages/autogen-agentchat/src/autogen_agentchat/agents/_assistant_agent.py +++ b/python/packages/autogen-agentchat/src/autogen_agentchat/agents/_assistant_agent.py @@ -50,11 +50,42 @@ def model_post_init(self, __context: Any) -> None: class AssistantAgent(BaseChatAgent): """An agent that provides assistance with tool use. - ```{note} - The assistant agent is not thread-safe or coroutine-safe. - It should not be shared between multiple tasks or coroutines, and it should - not call its methods concurrently. - ``` + The :meth:`on_messages` returns a :class:`~autogen_agentchat.base.Response` + in which :attr:`~autogen_agentchat.base.Response.chat_message` is the final + response message. + + The :meth:`on_messages_stream` creates an async generator that produces + the inner messages as they are created, and the :class:`~autogen_agentchat.base.Response` + object as the last item before closing the generator. + + Tool call behavior: + + * If the model returns no tool call, then the response is immediately returned as a :class:`~autogen_agentchat.messages.TextMessage` in :attr:`~autogen_agentchat.base.Response.chat_message`. + * When the model returns tool calls, they will be executed right away: + - When `reflect_on_tool_use` is False (default), the tool call results are returned as a :class:`~autogen_agentchat.messages.TextMessage` in :attr:`~autogen_agentchat.base.Response.chat_message`. `tool_call_summary_format` can be used to customize the tool call summary. + - When `reflect_on_tool_use` is True, the another model inference is made using the tool calls and results, and the text response is returned as a :class:`~autogen_agentchat.messages.TextMessage` in :attr:`~autogen_agentchat.base.Response.chat_message`. + + Hand off behavior: + + * If a handoff is triggered, a :class:`~autogen_agentchat.messages.HandoffMessage` will be returned in :attr:`~autogen_agentchat.base.Response.chat_message`. + * If there are tool calls, they will also be executed right away before returning the handoff. + + + .. note:: + The assistant agent is not thread-safe or coroutine-safe. + It should not be shared between multiple tasks or coroutines, and it should + not call its methods concurrently. + + .. note:: + By default, the tool call results are returned as response when tool calls are made. + So it is recommended to pay attention to the formatting of the tools return values, + especially if another agent is expecting them in a specific format. + Use `tool_call_summary_format` to customize the tool call summary, if needed. + + .. note:: + If multiple handoffs are detected, only the first handoff is executed. + + Args: name (str): The name of the agent. @@ -66,11 +97,20 @@ class AssistantAgent(BaseChatAgent): If a handoff is a string, it should represent the target agent's name. description (str, optional): The description of the agent. system_message (str, optional): The system message for the model. + reflect_on_tool_use (bool, optional): If `True`, the agent will make another model inference using the tool call and result + to generate a response. If `False`, the tool call result will be returned as the response. Defaults to `False`. + tool_call_summary_format (str, optional): The format string used to create a tool call summary for every tool call result. + Defaults to "{result}". + When `reflect_on_tool_use` is `False`, a concatenation of all the tool call summaries, separated by a new line character ('\\n') + will be returned as the response. + Available variables: `{tool_name}`, `{arguments}`, `{result}`. + For example, `"{tool_name}: {result}"` will create a summary like `"tool_name: result"`. Raises: ValueError: If tool names are not unique. ValueError: If handoff names are not unique. ValueError: If handoff names are not unique from tool names. + ValueError: If maximum number of tool iterations is less than 1. Examples: @@ -181,6 +221,8 @@ def __init__( description: str = "An agent that provides assistance with ability to use tools.", system_message: str | None = "You are a helpful AI assistant. Solve tasks using your tools. Reply with TERMINATE when the task has been completed.", + reflect_on_tool_use: bool = False, + tool_call_summary_format: str = "{result}", ): super().__init__(name=name, description=description) self._model_client = model_client @@ -231,6 +273,8 @@ def __init__( f"Handoff names must be unique from tool names. Handoff names: {handoff_tool_names}; tool names: {tool_names}" ) self._model_context: List[LLMMessage] = [] + self._reflect_on_tool_use = reflect_on_tool_use + self._tool_call_summary_format = tool_call_summary_format self._is_running = False @property @@ -267,53 +311,77 @@ async def on_messages_stream( # Add the response to the model context. self._model_context.append(AssistantMessage(content=result.content, source=self.name)) - # Run tool calls until the model produces a string response. - while isinstance(result.content, list) and all(isinstance(item, FunctionCall) for item in result.content): - tool_call_msg = ToolCallMessage(content=result.content, source=self.name, models_usage=result.usage) - event_logger.debug(tool_call_msg) - # Add the tool call message to the output. - inner_messages.append(tool_call_msg) - yield tool_call_msg - - # Execute the tool calls. - results = await asyncio.gather( - *[self._execute_tool_call(call, cancellation_token) for call in result.content] + # Check if the response is a string and return it. + if isinstance(result.content, str): + yield Response( + chat_message=TextMessage(content=result.content, source=self.name, models_usage=result.usage), + inner_messages=inner_messages, ) - tool_call_result_msg = ToolCallResultMessage(content=results, source=self.name) - event_logger.debug(tool_call_result_msg) - self._model_context.append(FunctionExecutionResultMessage(content=results)) - inner_messages.append(tool_call_result_msg) - yield tool_call_result_msg - - # Detect handoff requests. - handoffs: List[HandoffBase] = [] - for call in result.content: - if call.name in self._handoffs: - handoffs.append(self._handoffs[call.name]) - if len(handoffs) > 0: - if len(handoffs) > 1: - raise ValueError(f"Multiple handoffs detected: {[handoff.name for handoff in handoffs]}") - # Return the output messages to signal the handoff. - yield Response( - chat_message=HandoffMessage( - content=handoffs[0].message, target=handoffs[0].target, source=self.name - ), - inner_messages=inner_messages, + return + + # Process tool calls. + assert isinstance(result.content, list) and all(isinstance(item, FunctionCall) for item in result.content) + tool_call_msg = ToolCallMessage(content=result.content, source=self.name, models_usage=result.usage) + event_logger.debug(tool_call_msg) + # Add the tool call message to the output. + inner_messages.append(tool_call_msg) + yield tool_call_msg + + # Execute the tool calls. + results = await asyncio.gather(*[self._execute_tool_call(call, cancellation_token) for call in result.content]) + tool_call_result_msg = ToolCallResultMessage(content=results, source=self.name) + event_logger.debug(tool_call_result_msg) + self._model_context.append(FunctionExecutionResultMessage(content=results)) + inner_messages.append(tool_call_result_msg) + yield tool_call_result_msg + + # Detect handoff requests. + handoffs: List[HandoffBase] = [] + for call in result.content: + if call.name in self._handoffs: + handoffs.append(self._handoffs[call.name]) + if len(handoffs) > 0: + if len(handoffs) > 1: + # show warning if multiple handoffs detected + warnings.warn( + f"Multiple handoffs detected only the first is executed: {[handoff.name for handoff in handoffs]}", + stacklevel=2, ) - return + # Return the output messages to signal the handoff. + yield Response( + chat_message=HandoffMessage(content=handoffs[0].message, target=handoffs[0].target, source=self.name), + inner_messages=inner_messages, + ) + return - # Generate an inference result based on the current model context. + if self._reflect_on_tool_use: + # Generate another inference result based on the tool call and result. llm_messages = self._system_messages + self._model_context - result = await self._model_client.create( - llm_messages, tools=self._tools + self._handoff_tools, cancellation_token=cancellation_token - ) + result = await self._model_client.create(llm_messages, cancellation_token=cancellation_token) + assert isinstance(result.content, str) + # Add the response to the model context. self._model_context.append(AssistantMessage(content=result.content, source=self.name)) - - assert isinstance(result.content, str) - yield Response( - chat_message=TextMessage(content=result.content, source=self.name, models_usage=result.usage), - inner_messages=inner_messages, - ) + # Yield the response. + yield Response( + chat_message=TextMessage(content=result.content, source=self.name, models_usage=result.usage), + inner_messages=inner_messages, + ) + else: + # Return tool call result as the response. + tool_call_summaries: List[str] = [] + for i in range(len(tool_call_msg.content)): + tool_call_summaries.append( + self._tool_call_summary_format.format( + tool_name=tool_call_msg.content[i].name, + arguments=tool_call_msg.content[i].arguments, + result=tool_call_result_msg.content[i].content, + ), + ) + tool_call_summary = "\n".join(tool_call_summaries) + yield Response( + chat_message=TextMessage(content=tool_call_summary, source=self.name), + inner_messages=inner_messages, + ) async def _execute_tool_call( self, tool_call: FunctionCall, cancellation_token: CancellationToken diff --git a/python/packages/autogen-agentchat/tests/test_assistant_agent.py b/python/packages/autogen-agentchat/tests/test_assistant_agent.py index 479f1c2006ea..d5673b0a942e 100644 --- a/python/packages/autogen-agentchat/tests/test_assistant_agent.py +++ b/python/packages/autogen-agentchat/tests/test_assistant_agent.py @@ -118,6 +118,106 @@ async def test_run_with_tools(monkeypatch: pytest.MonkeyPatch) -> None: tools=[_pass_function, _fail_function, FunctionTool(_echo_function, description="Echo")], ) result = await agent.run(task="task") + + assert len(result.messages) == 4 + assert isinstance(result.messages[0], TextMessage) + assert result.messages[0].models_usage is None + assert isinstance(result.messages[1], ToolCallMessage) + assert result.messages[1].models_usage is not None + assert result.messages[1].models_usage.completion_tokens == 5 + assert result.messages[1].models_usage.prompt_tokens == 10 + assert isinstance(result.messages[2], ToolCallResultMessage) + assert result.messages[2].models_usage is None + assert isinstance(result.messages[3], TextMessage) + assert result.messages[3].content == "pass" + assert result.messages[3].models_usage is None + + # Test streaming. + mock._curr_index = 0 # pyright: ignore + index = 0 + async for message in agent.run_stream(task="task"): + if isinstance(message, TaskResult): + assert message == result + else: + assert message == result.messages[index] + index += 1 + + # Test state saving and loading. + state = await agent.save_state() + agent2 = AssistantAgent( + "tool_use_agent", + model_client=OpenAIChatCompletionClient(model=model, api_key=""), + tools=[_pass_function, _fail_function, FunctionTool(_echo_function, description="Echo")], + ) + await agent2.load_state(state) + state2 = await agent2.save_state() + assert state == state2 + + +@pytest.mark.asyncio +async def test_run_with_tools_and_reflection(monkeypatch: pytest.MonkeyPatch) -> None: + model = "gpt-4o-2024-05-13" + chat_completions = [ + ChatCompletion( + id="id1", + choices=[ + Choice( + finish_reason="tool_calls", + index=0, + message=ChatCompletionMessage( + content=None, + tool_calls=[ + ChatCompletionMessageToolCall( + id="1", + type="function", + function=Function( + name="_pass_function", + arguments=json.dumps({"input": "task"}), + ), + ) + ], + role="assistant", + ), + ) + ], + created=0, + model=model, + object="chat.completion", + usage=CompletionUsage(prompt_tokens=10, completion_tokens=5, total_tokens=0), + ), + ChatCompletion( + id="id2", + choices=[ + Choice(finish_reason="stop", index=0, message=ChatCompletionMessage(content="Hello", role="assistant")) + ], + created=0, + model=model, + object="chat.completion", + usage=CompletionUsage(prompt_tokens=10, completion_tokens=5, total_tokens=0), + ), + ChatCompletion( + id="id2", + choices=[ + Choice( + finish_reason="stop", index=0, message=ChatCompletionMessage(content="TERMINATE", role="assistant") + ) + ], + created=0, + model=model, + object="chat.completion", + usage=CompletionUsage(prompt_tokens=10, completion_tokens=5, total_tokens=0), + ), + ] + mock = _MockChatCompletion(chat_completions) + monkeypatch.setattr(AsyncCompletions, "create", mock.mock_create) + agent = AssistantAgent( + "tool_use_agent", + model_client=OpenAIChatCompletionClient(model=model, api_key=""), + tools=[_pass_function, _fail_function, FunctionTool(_echo_function, description="Echo")], + reflect_on_tool_use=True, + ) + result = await agent.run(task="task") + assert len(result.messages) == 4 assert isinstance(result.messages[0], TextMessage) assert result.messages[0].models_usage is None @@ -128,6 +228,7 @@ async def test_run_with_tools(monkeypatch: pytest.MonkeyPatch) -> None: assert isinstance(result.messages[2], ToolCallResultMessage) assert result.messages[2].models_usage is None assert isinstance(result.messages[3], TextMessage) + assert result.messages[3].content == "Hello" assert result.messages[3].models_usage is not None assert result.messages[3].models_usage.completion_tokens == 5 assert result.messages[3].models_usage.prompt_tokens == 10 diff --git a/python/packages/autogen-agentchat/tests/test_group_chat.py b/python/packages/autogen-agentchat/tests/test_group_chat.py index cf3ba0b206c8..c94ef3191b2a 100644 --- a/python/packages/autogen-agentchat/tests/test_group_chat.py +++ b/python/packages/autogen-agentchat/tests/test_group_chat.py @@ -32,8 +32,7 @@ from autogen_agentchat.teams._group_chat._selector_group_chat import SelectorGroupChatManager from autogen_agentchat.teams._group_chat._swarm_group_chat import SwarmGroupChatManager from autogen_agentchat.ui import Console -from autogen_core import AgentId, CancellationToken, FunctionCall -from autogen_core.models import FunctionExecutionResult +from autogen_core import AgentId, CancellationToken from autogen_core.tools import FunctionTool from autogen_ext.code_executors.local import LocalCommandLineCodeExecutor from autogen_ext.models import OpenAIChatCompletionClient, ReplayChatCompletionClient @@ -306,6 +305,7 @@ async def test_round_robin_group_chat_with_tools(monkeypatch: pytest.MonkeyPatch usage=CompletionUsage(prompt_tokens=0, completion_tokens=0, total_tokens=0), ), ] + # Test with repeat tool calls once mock = _MockChatCompletion(chat_completions) monkeypatch.setattr(AsyncCompletions, "create", mock.mock_create) tool = FunctionTool(_pass_function, name="pass", description="pass function") @@ -320,27 +320,18 @@ async def test_round_robin_group_chat_with_tools(monkeypatch: pytest.MonkeyPatch result = await team.run( task="Write a program that prints 'Hello, world!'", ) - - assert len(result.messages) == 6 + assert len(result.messages) == 8 assert isinstance(result.messages[0], TextMessage) # task assert isinstance(result.messages[1], ToolCallMessage) # tool call assert isinstance(result.messages[2], ToolCallResultMessage) # tool call result assert isinstance(result.messages[3], TextMessage) # tool use agent response assert isinstance(result.messages[4], TextMessage) # echo agent response assert isinstance(result.messages[5], TextMessage) # tool use agent response - assert result.stop_reason is not None and result.stop_reason == "Text 'TERMINATE' mentioned" + assert isinstance(result.messages[6], TextMessage) # echo agent response + assert isinstance(result.messages[7], TextMessage) # tool use agent response, that has TERMINATE + assert result.messages[7].content == "TERMINATE" - context = tool_use_agent._model_context # pyright: ignore - assert context[0].content == "Write a program that prints 'Hello, world!'" - assert isinstance(context[1].content, list) - assert isinstance(context[1].content[0], FunctionCall) - assert context[1].content[0].name == "pass" - assert context[1].content[0].arguments == json.dumps({"input": "pass"}) - assert isinstance(context[2].content, list) - assert isinstance(context[2].content[0], FunctionExecutionResult) - assert context[2].content[0].content == "pass" - assert context[2].content[0].call_id == "1" - assert context[3].content == "Hello" + assert result.stop_reason is not None and result.stop_reason == "Text 'TERMINATE' mentioned" # Test streaming. tool_use_agent._model_context.clear() # pyright: ignore diff --git a/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/examples/company-research.ipynb b/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/examples/company-research.ipynb index 8f4147b031bc..44d1b089a5ab 100644 --- a/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/examples/company-research.ipynb +++ b/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/examples/company-research.ipynb @@ -18,13 +18,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "from autogen_agentchat.agents import CodingAssistantAgent, ToolUseAssistantAgent\n", + "from autogen_agentchat.agents import AssistantAgent\n", "from autogen_agentchat.conditions import TextMentionTermination\n", "from autogen_agentchat.teams import RoundRobinGroupChat\n", + "from autogen_agentchat.ui import Console\n", "from autogen_core.tools import FunctionTool\n", "from autogen_ext.models import OpenAIChatCompletionClient" ] @@ -55,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -78,7 +79,7 @@ " if not api_key or not search_engine_id:\n", " raise ValueError(\"API key or Search Engine ID not found in environment variables\")\n", "\n", - " url = \"https://www.googleapis.com/customsearch/v1\"\n", + " url = \"https://customsearch.googleapis.com/customsearch/v1\"\n", " params = {\"key\": str(api_key), \"cx\": str(search_engine_id), \"q\": str(query), \"num\": str(num_results)}\n", "\n", " response = requests.get(url, params=params)\n", @@ -212,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -233,165 +234,150 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "search_agent = ToolUseAssistantAgent(\n", + "search_agent = AssistantAgent(\n", " name=\"Google_Search_Agent\",\n", - " model_client=OpenAIChatCompletionClient(model=\"gpt-4o-mini\"),\n", - " registered_tools=[google_search_tool],\n", + " model_client=OpenAIChatCompletionClient(model=\"gpt-4o\"),\n", + " tools=[google_search_tool],\n", " description=\"Search Google for information, returns top 2 results with a snippet and body content\",\n", " system_message=\"You are a helpful AI assistant. Solve tasks using your tools.\",\n", ")\n", "\n", - "stock_analysis_agent = ToolUseAssistantAgent(\n", + "stock_analysis_agent = AssistantAgent(\n", " name=\"Stock_Analysis_Agent\",\n", - " model_client=OpenAIChatCompletionClient(model=\"gpt-4o-mini\"),\n", - " registered_tools=[stock_analysis_tool],\n", + " model_client=OpenAIChatCompletionClient(model=\"gpt-4o\"),\n", + " tools=[stock_analysis_tool],\n", " description=\"Analyze stock data and generate a plot\",\n", - " system_message=\"You are a helpful AI assistant. Solve tasks using your tools.\",\n", + " system_message=\"Perform data analysis.\",\n", ")\n", "\n", - "report_agent = CodingAssistantAgent(\n", + "report_agent = AssistantAgent(\n", " name=\"Report_Agent\",\n", - " model_client=OpenAIChatCompletionClient(model=\"gpt-4o-mini\"),\n", - " description=\"Generate a report based on the search and stock analysis results\",\n", + " model_client=OpenAIChatCompletionClient(model=\"gpt-4o\"),\n", + " description=\"Generate a report based the search and results of stock analysis\",\n", " system_message=\"You are a helpful assistant that can generate a comprehensive report on a given topic based on search and stock analysis. When you done with generating the report, reply with TERMINATE.\",\n", - ")\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating the Team\n", "\n", - "termination = TextMentionTermination(\"TERMINATE\")\n", - "team = RoundRobinGroupChat([search_agent, stock_analysis_agent, report_agent], termination_condition=termination)" + "Finally, let's create a team of the three agents and set them to work on researching a company." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "team = RoundRobinGroupChat([stock_analysis_agent, search_agent, report_agent], max_turns=3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use `max_turns=3` to limit the number of turns to exactly the same number of agents in the team. This effectively makes the agents work in a sequential manner." + ] + }, + { + "cell_type": "code", + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - "--------------------------------------------------------------------------- \n", - "\u001b[91m[2024-10-08T13:07:56.762630]:\u001b[0m\n", - "\n", + "---------- user ----------\n", "Write a financial report on American airlines\n", - "From: user\n", - "--------------------------------------------------------------------------- \n", - "\u001b[91m[2024-10-08T13:07:57.840424], Google_Search_Agent:\u001b[0m\n", - "\n", - "[FunctionCall(id='call_Q72sGSLMyu2CLa7kVzZDHMpL', arguments='{\"query\":\"American Airlines financial report 2023\",\"num_results\":5}', name='google_search')]\n", - "From: Google_Search_Agent\n", - "--------------------------------------------------------------------------- \n", - "\u001b[91m[2024-10-08T13:08:04.880251], tool_agent_for_Google_Search_Agent:\u001b[0m\n", - "\n", - "[FunctionExecutionResult(content=\"[{'title': 'American Airlines reports fourth-quarter and full-year 2023 financial ...', 'link': 'https://news.aa.com/news/news-details/2024/American-Airlines-reports-fourth-quarter-and-full-year-2023-financial-results-CORP-FI-01/default.aspx', 'snippet': 'Jan 25, 2024 ... American Airlines Group Inc. (NASDAQ: AAL) today reported its fourth-quarter and full-year 2023 financial results, including: Record\\\\xa0...', 'body': 'Just a moment... Enable JavaScript and cookies to continue'}, {'title': 'Financial AAL | American Airlines', 'link': 'https://americanairlines.gcs-web.com/financial-results/financial-aal', 'snippet': 'Financial Results. Investor Relations; Financial Results; Financial Results ... Transcript 196.4 KB. 2023. Q4. Earnings Release · Form 10-K · Transcript 190.6 KB\\\\xa0...', 'body': 'Financial AAL | American Airlines Skip to main navigation Main Menu Investor Relations Toolkit Presentations & Investor Updates Financial Results SEC Filings Annual Shareholders Meeting Proxy Materials & Virtual Shareholder Meeting AGM – QA Written Responses Stock Info Events Equity Distribution Analysts FAQs Merger Information Contact Us Corporate Information About Us Leadership Bios Fact Sheets Where We Fly Our Planes News Corporate Governance Plan Travel American Airlines AAdvantage'}, {'title': 'American Airlines Group - AnnualReports.com', 'link': 'https://www.annualreports.com/Company/american-airlines-group', 'snippet': 'Most Recent Annual Report · View 2023 Sustainability Report · Older/Archived Annual Reports · Rate This Report.', 'body': 'American Airlines Group - AnnualReports.com Menu BROWSE BY Exchanges Industry Other Filters BROWSE BY Exchanges Industry Other Filters 0 American Airlines Group Ticker AAL Exchange NASDAQ More Industry Air Services, Other More Sector Industrial Goods More 10,000+ Employees Based in Dallas-Fort Worth, Texas American Airlines offers customers 6,800 daily flights to more than 365 destinations in 61 countries from its hubs in Charlotte, Chicago, Dallas-Fort Worth, Los Angeles, Miami, New York,'}, {'title': 'American Airlines reports fourth-quarter and full-year 2023 financial ...', 'link': 'https://americanairlines.gcs-web.com/news-releases/news-release-details/american-airlines-reports-fourth-quarter-and-full-year-2023', 'snippet': 'Jan 25, 2024 ... American produced record revenue of nearly $53 billion. In the fourth quarter, the company generated revenue of more than $13 billion and an operating margin\\\\xa0...', 'body': 'American Airlines reports fourth-quarter and full-year 2023 financial results | American Airlines Skip to main navigation Main Menu Investor Relations Toolkit Presentations & Investor Updates Financial Results SEC Filings Annual Shareholders Meeting Proxy Materials & Virtual Shareholder Meeting AGM – QA Written Responses Stock Info Events Equity Distribution Analysts FAQs Merger Information Contact Us Corporate Information About Us Leadership Bios Fact Sheets Where We Fly Our Planes News'}, {'title': 'American Airlines reports fourth-quarter and full-year 2023 financial ...', 'link': 'https://finance.yahoo.com/news/american-airlines-reports-fourth-quarter-120000360.html', 'snippet': 'Jan 25, 2024 ... FORT WORTH, Texas, Jan. 25, 2024 (GLOBE NEWSWIRE) -- American Airlines Group Inc. (NASDAQ: AAL) today reported its fourth-quarter and\\\\xa0...', 'body': 'Yahoo Will be right back... Thank you for your patience. Our engineers are working quickly to resolve the issue.'}]\", call_id='call_Q72sGSLMyu2CLa7kVzZDHMpL')]\n", - "From: tool_agent_for_Google_Search_Agent\n", - "--------------------------------------------------------------------------- \n", - "\u001b[91m[2024-10-08T13:08:06.643034], Google_Search_Agent:\u001b[0m\n", - "\n", - "[FunctionCall(id='call_7J6Eq4RA2aoxWJ71phUvkojN', arguments='{\"query\": \"American Airlines financial results 2023 full year\", \"num_results\": 2}', name='google_search'), FunctionCall(id='call_ixFuFFKDUDSdQSGLwWyxoIs6', arguments='{\"query\": \"American Airlines Q4 2023 earnings report\", \"num_results\": 2}', name='google_search')]\n", - "From: Google_Search_Agent\n", - "--------------------------------------------------------------------------- \n", - "\u001b[91m[2024-10-08T13:08:09.736878], tool_agent_for_Google_Search_Agent:\u001b[0m\n", - "\n", - "[FunctionExecutionResult(content=\"[{'title': 'American Airlines reports fourth-quarter and full-year 2023 financial ...', 'link': 'https://news.aa.com/news/news-details/2024/American-Airlines-reports-fourth-quarter-and-full-year-2023-financial-results-CORP-FI-01/default.aspx', 'snippet': 'Jan 25, 2024 ... American Airlines Group Inc. (NASDAQ: AAL) today reported its fourth-quarter and full-year 2023 financial results, including: Record\\\\xa0...', 'body': 'Just a moment... Enable JavaScript and cookies to continue'}, {'title': 'American Airlines reports fourth-quarter and full-year 2023 financial ...', 'link': 'https://americanairlines.gcs-web.com/news-releases/news-release-details/american-airlines-reports-fourth-quarter-and-full-year-2023', 'snippet': 'Jan 25, 2024 ... American produced record revenue of nearly $53 billion. In the fourth quarter, the company generated revenue of more than $13 billion and an operating margin\\\\xa0...', 'body': 'American Airlines reports fourth-quarter and full-year 2023 financial results | American Airlines Skip to main navigation Main Menu Investor Relations Toolkit Presentations & Investor Updates Financial Results SEC Filings Annual Shareholders Meeting Proxy Materials & Virtual Shareholder Meeting AGM – QA Written Responses Stock Info Events Equity Distribution Analysts FAQs Merger Information Contact Us Corporate Information About Us Leadership Bios Fact Sheets Where We Fly Our Planes News'}]\", call_id='call_7J6Eq4RA2aoxWJ71phUvkojN'), FunctionExecutionResult(content='[{\\'title\\': \\'American Airlines reports fourth-quarter and full-year 2023 financial ...\\', \\'link\\': \\'https://news.aa.com/news/news-details/2024/American-Airlines-reports-fourth-quarter-and-full-year-2023-financial-results-CORP-FI-01/default.aspx\\', \\'snippet\\': \\'Jan 25, 2024 ... 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Enable JavaScript and cookies to continue\\'}, {\\'title\\': \\'Investor Relations | American Airlines\\', \\'link\\': \\'http://americanairlines.gcs-web.com/\\', \\'snippet\\': \"The Investor Relations website contains information about American Airlines\\'s business for stockholders, potential investors, and financial analysts.\", \\'body\\': \\'Investor Relations | American Airlines Skip to main navigation Main Menu Investor Relations Toolkit Presentations & Investor Updates Financial Results SEC Filings Annual Shareholders Meeting Proxy Materials & Virtual Shareholder Meeting AGM – QA Written Responses Stock Info Events Equity Distribution Analysts FAQs Merger Information Contact Us Corporate Information About Us Leadership Bios Fact Sheets Where We Fly Our Planes News Corporate Governance Plan Travel American Airlines AAdvantage\\'}]', call_id='call_ixFuFFKDUDSdQSGLwWyxoIs6')]\n", - "From: tool_agent_for_Google_Search_Agent\n", - "--------------------------------------------------------------------------- \n", - "\u001b[91m[2024-10-08T13:08:16.385923], Google_Search_Agent:\u001b[0m\n", - "\n", - "### Financial Report: American Airlines Group Inc. (2023)\n", + "---------- Stock_Analysis_Agent ----------\n", + "[FunctionCall(id='call_tPh9gSfGrDu1nC2Ck5RlfbFY', arguments='{\"ticker\":\"AAL\"}', name='analyze_stock')]\n", + "[Prompt tokens: 64, Completion tokens: 16]\n", + "Plot saved as coding/AAL_stockprice.png\n", + "---------- Stock_Analysis_Agent ----------\n", + "[FunctionExecutionResult(content=\"{'ticker': 'AAL', 'current_price': 17.4, '52_week_high': 18.09, '52_week_low': 9.07, '50_day_ma': 13.376799983978271, '200_day_ma': 12.604399962425232, 'ytd_price_change': 3.9600000381469727, 'ytd_percent_change': 29.46428691803602, 'trend': 'Upward', 'volatility': 0.4461582174242901, 'plot_file_path': 'coding/AAL_stockprice.png'}\", call_id='call_tPh9gSfGrDu1nC2Ck5RlfbFY')]\n", + "---------- Stock_Analysis_Agent ----------\n", + "Tool calls:\n", + "analyze_stock({\"ticker\":\"AAL\"}) = {'ticker': 'AAL', 'current_price': 17.4, '52_week_high': 18.09, '52_week_low': 9.07, '50_day_ma': 13.376799983978271, '200_day_ma': 12.604399962425232, 'ytd_price_change': 3.9600000381469727, 'ytd_percent_change': 29.46428691803602, 'trend': 'Upward', 'volatility': 0.4461582174242901, 'plot_file_path': 'coding/AAL_stockprice.png'}\n", + "---------- Google_Search_Agent ----------\n", + "[FunctionCall(id='call_wSHc5Kw1ix3aQDXXT23opVnU', arguments='{\"query\":\"American Airlines financial report 2023\",\"num_results\":1}', name='google_search')]\n", + "[Prompt tokens: 268, Completion tokens: 25]\n", + "---------- Google_Search_Agent ----------\n", + "[FunctionExecutionResult(content=\"[{'title': 'American Airlines reports fourth-quarter and full-year 2023 financial ...', 'link': 'https://news.aa.com/news/news-details/2024/American-Airlines-reports-fourth-quarter-and-full-year-2023-financial-results-CORP-FI-01/default.aspx', 'snippet': 'Jan 25, 2024 ... American Airlines Group Inc. (NASDAQ: AAL) today reported its fourth-quarter and full-year 2023 financial results, including: Record\\\\xa0...', 'body': 'Just a moment... Enable JavaScript and cookies to continue'}]\", call_id='call_wSHc5Kw1ix3aQDXXT23opVnU')]\n", + "---------- Google_Search_Agent ----------\n", + "Tool calls:\n", + "google_search({\"query\":\"American Airlines financial report 2023\",\"num_results\":1}) = [{'title': 'American Airlines reports fourth-quarter and full-year 2023 financial ...', 'link': 'https://news.aa.com/news/news-details/2024/American-Airlines-reports-fourth-quarter-and-full-year-2023-financial-results-CORP-FI-01/default.aspx', 'snippet': 'Jan 25, 2024 ... American Airlines Group Inc. (NASDAQ: AAL) today reported its fourth-quarter and full-year 2023 financial results, including: Record\\xa0...', 'body': 'Just a moment... Enable JavaScript and cookies to continue'}]\n", + "---------- Report_Agent ----------\n", + "### American Airlines Financial Report\n", "\n", "#### Overview\n", - "American Airlines Group Inc. (NASDAQ: AAL), one of the largest airlines in the world, released its financial results for the fourth quarter and full year of 2023 on January 25, 2024.\n", - "\n", - "#### Key Financial Highlights for Full Year 2023\n", - "- **Total Revenue**: American Airlines achieved record revenue of nearly **$53 billion** for the full year.\n", - "- **Operating Revenue**: In the fourth quarter, the airline generated more than **$13 billion** in revenue.\n", - "- **Operating Margin**: The operating margin improved significantly due to increased demand and operational efficiencies.\n", - "\n", - "#### Q4 Financial Results\n", - "- **Revenue in Q4**: The fourth quarter witnessed a robust revenue growth that reflects strong travel demand.\n", - "- **Performance Metrics**: Additional performance metrics include:\n", - " - Detailed information about passenger revenues, cargo revenues, and operational metrics such as capacity and load factors.\n", - " \n", - "#### Future Outlook\n", - "American Airlines is focusing on expanding its flight services and improving overall operational efficiency in 2024. The airline aims to capitalize on the continuing recovery in travel demand, which should positively impact future revenues and profitability.\n", - "\n", - "#### Corporate Strategy\n", - "The airline has emphasized the importance of sustainable practices and operational innovations in its corporate strategy to adapt to evolving market conditions.\n", - "\n", - "#### Links for More Information\n", - "- [American Airlines Q4 2023 Financial Results](https://news.aa.com/news/news-details/2024/American-Airlines-reports-fourth-quarter-and-full-year-2023-financial-results-CORP-FI-01/default.aspx)\n", - "- [Investor Relations Page](http://americanairlines.gcs-web.com/)\n", - "\n", - "This financial report provides a summary of American Airlines' financial performance and outlook as of 2023. For detailed insights, the complete financial statements and press releases should be consulted directly from the airline's investor relations resources.\n", - "From: Google_Search_Agent\n", - "--------------------------------------------------------------------------- \n", - "\u001b[91m[2024-10-08T13:08:16.961784], Stock_Analysis_Agent:\u001b[0m\n", - "\n", - "[FunctionCall(id='call_sXPgieP7Mih48h44NX3fCYIe', arguments='{\"ticker\":\"AAL\"}', name='analyze_stock')]\n", - "From: Stock_Analysis_AgentPlot saved as coding/AAL_stockprice.png\n", + "American Airlines Group Inc. (NASDAQ: AAL) is a major American airline headquartered in Fort Worth, Texas. It is known as one of the largest airlines in the world by fleet size, revenue, and passenger kilometers flown. As of the current quarter in 2023, American Airlines has shown significant financial activities and stock performance noteworthy for investors and analysts.\n", "\n", - "--------------------------------------------------------------------------- \n", - "\u001b[91m[2024-10-08T13:08:21.371132], tool_agent_for_Stock_Analysis_Agent:\u001b[0m\n", + "#### Stock Performance\n", + "- **Current Stock Price**: $17.40\n", + "- **52-Week Range**: The stock price has ranged from $9.07 to $18.09 over the past year, indicating considerable volatility and fluctuation in market interest.\n", + "- **Moving Averages**: \n", + " - 50-Day MA: $13.38\n", + " - 200-Day MA: $12.60\n", + " These moving averages suggest a strong upward trend in recent months as the 50-day moving average is positioned above the 200-day moving average, indicating bullish momentum.\n", "\n", - "[FunctionExecutionResult(content=\"{'ticker': 'AAL', 'current_price': 11.92, '52_week_high': 16.15, '52_week_low': 9.07, '50_day_ma': 10.573600044250488, '200_day_ma': 12.708699951171875, 'ytd_price_change': -1.5199995040893555, 'ytd_percent_change': -11.30952047281244, 'trend': 'Downward', 'volatility': 0.4110122561680265, 'plot_file_path': 'coding/AAL_stockprice.png'}\", call_id='call_sXPgieP7Mih48h44NX3fCYIe')]\n", - "From: tool_agent_for_Stock_Analysis_Agent\n", - "--------------------------------------------------------------------------- \n", - "\u001b[91m[2024-10-08T13:08:28.314277], Stock_Analysis_Agent:\u001b[0m\n", + "- **YTD Price Change**: $3.96\n", + "- **YTD Percent Change**: 29.46%\n", + " The year-to-date figures demonstrate a robust upward momentum, with the stock appreciating by nearly 29.5% since the beginning of the year.\n", "\n", - "### Financial Report: American Airlines Group Inc. (2023)\n", + "- **Trend**: The current stock trend for American Airlines is upward, reflecting positive market sentiment and performance improvements.\n", "\n", - "#### Overview\n", - "American Airlines Group Inc. (NASDAQ: AAL) is one of the largest airlines globally, with substantial operations in passenger and cargo transport. The company has recently released its financial results for the fourth quarter and the full year of 2023.\n", - "\n", - "#### Key Financial Highlights for Full Year 2023\n", - "- **Total Revenue**: American Airlines achieved record revenue of nearly **$53 billion** for the full year.\n", - "- **Operating Revenue**: The airline generated over **$13 billion** in revenue during the fourth quarter.\n", - "- **Operating Margin**: There was a significant improvement in the operating margin, driven by increased travel demand and operational efficiencies.\n", - "\n", - "#### Q4 Financial Results\n", - "- The fourth quarter showed robust revenue growth, reflecting strong demand for travel.\n", - "- Additional performance metrics included passenger revenues, cargo revenues, capacity, and load factors, showcasing the airline's recovery post-pandemic.\n", + "- **Volatility**: 0.446, indicating moderate volatility in the stock, which may attract risk-tolerant investors seeking dynamic movements for potential profit.\n", "\n", - "#### Stock Performance Overview\n", - "- **Current Stock Price**: $11.92\n", - "- **52-Week High**: $16.15\n", - "- **52-Week Low**: $9.07\n", - "- **50-Day Moving Average**: $11.84\n", - "- **200-Day Moving Average**: $12.71\n", - "- **Year-to-Date Price Change**: -$1.52\n", - "- **Year-to-Date Percent Change**: -11.31%\n", - "- **Trend**: The stock is currently in a downward trend.\n", - "- **Volatility**: The stock has a volatility of 41.10%.\n", + "#### Recent Financial Performance\n", + "According to the latest financial reports of 2023 (accessed through a reliable source), American Airlines reported remarkable figures for both the fourth quarter and the full year 2023. Key highlights from the report include:\n", "\n", - "![American Airlines Stock Price Trend](coding/AAL_stockprice.png)\n", + "- **Revenue Growth**: American Airlines experienced substantial revenue increases, driven by high demand for travel as pandemic-related restrictions eased globally.\n", + "- **Profit Margins**: The company managed to enhance its profitability, largely attributed to cost management strategies and increased operational efficiency.\n", + "- **Challenges**: Despite positive momentum, the airline industry faces ongoing challenges including fluctuating fuel prices, geopolitical tensions, and competition pressures.\n", "\n", - "#### Future Outlook\n", - "American Airlines aims to expand its services and improve operational efficiency throughout 2024. The airline is poised to benefit from the continuing recovery in travel demand, which is expected to positively affect future revenues and profitability.\n", + "#### Strategic Initiatives\n", + "American Airlines has been focusing on several strategic initiatives to maintain its market leadership and improve its financial metrics:\n", + "1. **Fleet Modernization**: Continuation of investment in more fuel-efficient aircraft to reduce operating costs and environmental impact.\n", + "2. **Enhanced Customer Experience**: Introduction of new services and technology enhancements aimed at improving customer satisfaction.\n", + "3. **Operational Efficiency**: Streamlining processes to cut costs and increase overall effectiveness, which includes leveraging data analytics for better decision-making.\n", "\n", - "#### Corporate Strategy\n", - "The airline has prioritized sustainability and innovation within its operational strategy to adapt to ever-changing market conditions.\n", + "#### Conclusion\n", + "American Airlines is demonstrating strong market performance and financial growth amid an evolving industry landscape. The company's stock has been on an upward trend, reflecting its solid operational strategies and recovery efforts post-COVID pandemic. Investors should remain mindful of external risks while considering American Airlines as a potential investment, supported by its current upward trajectory and strategic initiatives.\n", "\n", - "#### Links for More Information\n", - "- [American Airlines Q4 2023 Financial Results](https://news.aa.com/news/news-details/2024/American-Airlines-reports-fourth-quarter-and-full-year-2023-financial-results-CORP-FI-01/default.aspx)\n", - "- [Investor Relations Page](http://americanairlines.gcs-web.com/)\n", + "For further details, investors are encouraged to review the full financial reports from American Airlines and assess ongoing market conditions.\n", "\n", - "This financial report provides a comprehensive overview of American Airlines' financial performance and stock analysis as of 2023. For more detailed insights, please refer to the airline's investor relations resources.\n", - "From: Stock_Analysis_Agent\n", - "--------------------------------------------------------------------------- \n", - "\u001b[91m[2024-10-08T13:08:28.703160], Report_Agent:\u001b[0m\n", - "\n", - "TERMINATE\n", - "From: Report_AgentTeamRunResult(messages=[TextMessage(source='user', content='Write a financial report on American airlines'), TextMessage(source='Google_Search_Agent', content=\"### Financial Report: American Airlines Group Inc. (2023)\\n\\n#### Overview\\nAmerican Airlines Group Inc. (NASDAQ: AAL), one of the largest airlines in the world, released its financial results for the fourth quarter and full year of 2023 on January 25, 2024.\\n\\n#### Key Financial Highlights for Full Year 2023\\n- **Total Revenue**: American Airlines achieved record revenue of nearly **$53 billion** for the full year.\\n- **Operating Revenue**: In the fourth quarter, the airline generated more than **$13 billion** in revenue.\\n- **Operating Margin**: The operating margin improved significantly due to increased demand and operational efficiencies.\\n\\n#### Q4 Financial Results\\n- **Revenue in Q4**: The fourth quarter witnessed a robust revenue growth that reflects strong travel demand.\\n- **Performance Metrics**: Additional performance metrics include:\\n - Detailed information about passenger revenues, cargo revenues, and operational metrics such as capacity and load factors.\\n \\n#### Future Outlook\\nAmerican Airlines is focusing on expanding its flight services and improving overall operational efficiency in 2024. The airline aims to capitalize on the continuing recovery in travel demand, which should positively impact future revenues and profitability.\\n\\n#### Corporate Strategy\\nThe airline has emphasized the importance of sustainable practices and operational innovations in its corporate strategy to adapt to evolving market conditions.\\n\\n#### Links for More Information\\n- [American Airlines Q4 2023 Financial Results](https://news.aa.com/news/news-details/2024/American-Airlines-reports-fourth-quarter-and-full-year-2023-financial-results-CORP-FI-01/default.aspx)\\n- [Investor Relations Page](http://americanairlines.gcs-web.com/)\\n\\nThis financial report provides a summary of American Airlines' financial performance and outlook as of 2023. For detailed insights, the complete financial statements and press releases should be consulted directly from the airline's investor relations resources.\"), TextMessage(source='Stock_Analysis_Agent', content=\"### Financial Report: American Airlines Group Inc. (2023)\\n\\n#### Overview\\nAmerican Airlines Group Inc. (NASDAQ: AAL) is one of the largest airlines globally, with substantial operations in passenger and cargo transport. The company has recently released its financial results for the fourth quarter and the full year of 2023.\\n\\n#### Key Financial Highlights for Full Year 2023\\n- **Total Revenue**: American Airlines achieved record revenue of nearly **$53 billion** for the full year.\\n- **Operating Revenue**: The airline generated over **$13 billion** in revenue during the fourth quarter.\\n- **Operating Margin**: There was a significant improvement in the operating margin, driven by increased travel demand and operational efficiencies.\\n\\n#### Q4 Financial Results\\n- The fourth quarter showed robust revenue growth, reflecting strong demand for travel.\\n- Additional performance metrics included passenger revenues, cargo revenues, capacity, and load factors, showcasing the airline's recovery post-pandemic.\\n\\n#### Stock Performance Overview\\n- **Current Stock Price**: $11.92\\n- **52-Week High**: $16.15\\n- **52-Week Low**: $9.07\\n- **50-Day Moving Average**: $11.84\\n- **200-Day Moving Average**: $12.71\\n- **Year-to-Date Price Change**: -$1.52\\n- **Year-to-Date Percent Change**: -11.31%\\n- **Trend**: The stock is currently in a downward trend.\\n- **Volatility**: The stock has a volatility of 41.10%.\\n\\n![American Airlines Stock Price Trend](coding/AAL_stockprice.png)\\n\\n#### Future Outlook\\nAmerican Airlines aims to expand its services and improve operational efficiency throughout 2024. The airline is poised to benefit from the continuing recovery in travel demand, which is expected to positively affect future revenues and profitability.\\n\\n#### Corporate Strategy\\nThe airline has prioritized sustainability and innovation within its operational strategy to adapt to ever-changing market conditions.\\n\\n#### Links for More Information\\n- [American Airlines Q4 2023 Financial Results](https://news.aa.com/news/news-details/2024/American-Airlines-reports-fourth-quarter-and-full-year-2023-financial-results-CORP-FI-01/default.aspx)\\n- [Investor Relations Page](http://americanairlines.gcs-web.com/)\\n\\nThis financial report provides a comprehensive overview of American Airlines' financial performance and stock analysis as of 2023. For more detailed insights, please refer to the airline's investor relations resources.\"), StopMessage(source='Report_Agent', content='TERMINATE')])\n" + "_TERMINATE_\n", + "[Prompt tokens: 360, Completion tokens: 633]\n", + "---------- Summary ----------\n", + "Number of messages: 8\n", + "Finish reason: Maximum number of turns 3 reached.\n", + "Total prompt tokens: 692\n", + "Total completion tokens: 674\n", + "Duration: 19.38 seconds\n" ] }, { "data": { - "image/png": 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", + "text/plain": [ + "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='Write a financial report on American airlines', type='TextMessage'), ToolCallMessage(source='Stock_Analysis_Agent', models_usage=RequestUsage(prompt_tokens=64, completion_tokens=16), content=[FunctionCall(id='call_tPh9gSfGrDu1nC2Ck5RlfbFY', arguments='{\"ticker\":\"AAL\"}', name='analyze_stock')], type='ToolCallMessage'), ToolCallResultMessage(source='Stock_Analysis_Agent', models_usage=None, content=[FunctionExecutionResult(content=\"{'ticker': 'AAL', 'current_price': 17.4, '52_week_high': 18.09, '52_week_low': 9.07, '50_day_ma': 13.376799983978271, '200_day_ma': 12.604399962425232, 'ytd_price_change': 3.9600000381469727, 'ytd_percent_change': 29.46428691803602, 'trend': 'Upward', 'volatility': 0.4461582174242901, 'plot_file_path': 'coding/AAL_stockprice.png'}\", call_id='call_tPh9gSfGrDu1nC2Ck5RlfbFY')], type='ToolCallResultMessage'), TextMessage(source='Stock_Analysis_Agent', models_usage=None, content='Tool calls:\\nanalyze_stock({\"ticker\":\"AAL\"}) = {\\'ticker\\': \\'AAL\\', \\'current_price\\': 17.4, \\'52_week_high\\': 18.09, \\'52_week_low\\': 9.07, \\'50_day_ma\\': 13.376799983978271, \\'200_day_ma\\': 12.604399962425232, \\'ytd_price_change\\': 3.9600000381469727, \\'ytd_percent_change\\': 29.46428691803602, \\'trend\\': \\'Upward\\', \\'volatility\\': 0.4461582174242901, \\'plot_file_path\\': \\'coding/AAL_stockprice.png\\'}', type='TextMessage'), ToolCallMessage(source='Google_Search_Agent', models_usage=RequestUsage(prompt_tokens=268, completion_tokens=25), content=[FunctionCall(id='call_wSHc5Kw1ix3aQDXXT23opVnU', arguments='{\"query\":\"American Airlines financial report 2023\",\"num_results\":1}', name='google_search')], type='ToolCallMessage'), ToolCallResultMessage(source='Google_Search_Agent', models_usage=None, content=[FunctionExecutionResult(content=\"[{'title': 'American Airlines reports fourth-quarter and full-year 2023 financial ...', 'link': 'https://news.aa.com/news/news-details/2024/American-Airlines-reports-fourth-quarter-and-full-year-2023-financial-results-CORP-FI-01/default.aspx', 'snippet': 'Jan 25, 2024 ... American Airlines Group Inc. (NASDAQ: AAL) today reported its fourth-quarter and full-year 2023 financial results, including: Record\\\\xa0...', 'body': 'Just a moment... Enable JavaScript and cookies to continue'}]\", call_id='call_wSHc5Kw1ix3aQDXXT23opVnU')], type='ToolCallResultMessage'), TextMessage(source='Google_Search_Agent', models_usage=None, content='Tool calls:\\ngoogle_search({\"query\":\"American Airlines financial report 2023\",\"num_results\":1}) = [{\\'title\\': \\'American Airlines reports fourth-quarter and full-year 2023 financial ...\\', \\'link\\': \\'https://news.aa.com/news/news-details/2024/American-Airlines-reports-fourth-quarter-and-full-year-2023-financial-results-CORP-FI-01/default.aspx\\', \\'snippet\\': \\'Jan 25, 2024 ... American Airlines Group Inc. (NASDAQ: AAL) today reported its fourth-quarter and full-year 2023 financial results, including: Record\\\\xa0...\\', \\'body\\': \\'Just a moment... Enable JavaScript and cookies to continue\\'}]', type='TextMessage'), TextMessage(source='Report_Agent', models_usage=RequestUsage(prompt_tokens=360, completion_tokens=633), content=\"### American Airlines Financial Report\\n\\n#### Overview\\nAmerican Airlines Group Inc. (NASDAQ: AAL) is a major American airline headquartered in Fort Worth, Texas. It is known as one of the largest airlines in the world by fleet size, revenue, and passenger kilometers flown. As of the current quarter in 2023, American Airlines has shown significant financial activities and stock performance noteworthy for investors and analysts.\\n\\n#### Stock Performance\\n- **Current Stock Price**: $17.40\\n- **52-Week Range**: The stock price has ranged from $9.07 to $18.09 over the past year, indicating considerable volatility and fluctuation in market interest.\\n- **Moving Averages**: \\n - 50-Day MA: $13.38\\n - 200-Day MA: $12.60\\n These moving averages suggest a strong upward trend in recent months as the 50-day moving average is positioned above the 200-day moving average, indicating bullish momentum.\\n\\n- **YTD Price Change**: $3.96\\n- **YTD Percent Change**: 29.46%\\n The year-to-date figures demonstrate a robust upward momentum, with the stock appreciating by nearly 29.5% since the beginning of the year.\\n\\n- **Trend**: The current stock trend for American Airlines is upward, reflecting positive market sentiment and performance improvements.\\n\\n- **Volatility**: 0.446, indicating moderate volatility in the stock, which may attract risk-tolerant investors seeking dynamic movements for potential profit.\\n\\n#### Recent Financial Performance\\nAccording to the latest financial reports of 2023 (accessed through a reliable source), American Airlines reported remarkable figures for both the fourth quarter and the full year 2023. Key highlights from the report include:\\n\\n- **Revenue Growth**: American Airlines experienced substantial revenue increases, driven by high demand for travel as pandemic-related restrictions eased globally.\\n- **Profit Margins**: The company managed to enhance its profitability, largely attributed to cost management strategies and increased operational efficiency.\\n- **Challenges**: Despite positive momentum, the airline industry faces ongoing challenges including fluctuating fuel prices, geopolitical tensions, and competition pressures.\\n\\n#### Strategic Initiatives\\nAmerican Airlines has been focusing on several strategic initiatives to maintain its market leadership and improve its financial metrics:\\n1. **Fleet Modernization**: Continuation of investment in more fuel-efficient aircraft to reduce operating costs and environmental impact.\\n2. **Enhanced Customer Experience**: Introduction of new services and technology enhancements aimed at improving customer satisfaction.\\n3. **Operational Efficiency**: Streamlining processes to cut costs and increase overall effectiveness, which includes leveraging data analytics for better decision-making.\\n\\n#### Conclusion\\nAmerican Airlines is demonstrating strong market performance and financial growth amid an evolving industry landscape. The company's stock has been on an upward trend, reflecting its solid operational strategies and recovery efforts post-COVID pandemic. Investors should remain mindful of external risks while considering American Airlines as a potential investment, supported by its current upward trajectory and strategic initiatives.\\n\\nFor further details, investors are encouraged to review the full financial reports from American Airlines and assess ongoing market conditions.\\n\\n_TERMINATE_\", type='TextMessage')], stop_reason='Maximum number of turns 3 reached.')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" ] @@ -401,8 +387,8 @@ } ], "source": [ - "result = await team.run(task=\"Write a financial report on American airlines\")\n", - "print(result)" + "stream = team.run_stream(task=\"Write a financial report on American airlines\")\n", + "await Console(stream)" ] } ], @@ -422,7 +408,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.6" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/examples/literature-review.ipynb b/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/examples/literature-review.ipynb index a224f26ef36e..4613b6dc20ab 100644 --- a/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/examples/literature-review.ipynb +++ b/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/examples/literature-review.ipynb @@ -18,13 +18,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "from autogen_agentchat.agents import CodingAssistantAgent, ToolUseAssistantAgent\n", + "from autogen_agentchat.agents import AssistantAgent\n", "from autogen_agentchat.conditions import TextMentionTermination\n", "from autogen_agentchat.teams import RoundRobinGroupChat\n", + "from autogen_agentchat.ui import Console\n", "from autogen_core.tools import FunctionTool\n", "from autogen_ext.models import OpenAIChatCompletionClient" ] @@ -161,180 +162,150 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "google_search_agent = AssistantAgent(\n", + " name=\"Google_Search_Agent\",\n", + " tools=[google_search_tool],\n", + " model_client=OpenAIChatCompletionClient(model=\"gpt-4o-mini\"),\n", + " description=\"An agent that can search Google for information, returns results with a snippet and body content\",\n", + " system_message=\"You are a helpful AI assistant. Solve tasks using your tools.\",\n", + ")\n", + "\n", + "arxiv_search_agent = AssistantAgent(\n", + " name=\"Arxiv_Search_Agent\",\n", + " tools=[arxiv_search_tool],\n", + " model_client=OpenAIChatCompletionClient(model=\"gpt-4o-mini\"),\n", + " description=\"An agent that can search Arxiv for papers related to a given topic, including abstracts\",\n", + " system_message=\"You are a helpful AI assistant. Solve tasks using your tools. Specifically, you can take into consideration the user's request and craft a search query that is most likely to return relevant academi papers.\",\n", + ")\n", + "\n", + "\n", + "report_agent = AssistantAgent(\n", + " name=\"Report_Agent\",\n", + " model_client=OpenAIChatCompletionClient(model=\"gpt-4o-mini\"),\n", + " description=\"Generate a report based on a given topic\",\n", + " system_message=\"You are a helpful assistant. Your task is to synthesize data extracted into a high quality literature review including CORRECT references. You MUST write a final report that is formatted as a literature review with CORRECT references. Your response should end with the word 'TERMINATE'\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating the Team \n", + "\n", + "Finally, we will create a team of agents and configure them to perform the tasks." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "termination = TextMentionTermination(\"TERMINATE\")\n", + "team = RoundRobinGroupChat(\n", + " participants=[google_search_agent, arxiv_search_agent, report_agent], termination_condition=termination\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - "--------------------------------------------------------------------------- \n", - "\u001b[91m[2024-10-08T13:16:31.834796]:\u001b[0m\n", - "\n", + "---------- user ----------\n", "Write a literature review on no code tools for building multi agent ai systems\n", - "From: user\n", - "--------------------------------------------------------------------------- \n", - "\u001b[91m[2024-10-08T13:16:32.601078], Google_Search_Agent:\u001b[0m\n", + "---------- Google_Search_Agent ----------\n", + "[FunctionCall(id='call_bNGwWFsfeTwDhtIpsI6GYISR', arguments='{\"query\":\"no code tools for building multi agent AI systems literature review\",\"num_results\":3}', name='google_search')]\n", + "[Prompt tokens: 123, Completion tokens: 29]\n", + "---------- Google_Search_Agent ----------\n", + "[FunctionExecutionResult(content='[{\\'title\\': \\'Literature Review — AutoGen\\', \\'link\\': \\'https://microsoft.github.io/autogen/dev//user-guide/agentchat-user-guide/examples/literature-review.html\\', \\'snippet\\': \\'run( task=\"Write a literature review on no code tools for building multi agent ai systems\", ) ... ### Conclusion No-code tools for building multi-agent AI systems\\\\xa0...\\', \\'body\\': \\'Literature Review — AutoGen Skip to main content Back to top Ctrl + K AutoGen 0.4 is a work in progress. Go here to find the 0.2 documentation. User Guide Packages API Reference Twitter GitHub PyPI User Guide Packages API Reference Twitter GitHub PyPI AgentChat Installation Quickstart Tutorial Models Messages Agents Teams Selector Group Chat Swarm Termination Custom Agents Managing State Examples Travel Planning Company Research Literature Review Core Quick Start Core Concepts Agent and\\'}, {\\'title\\': \\'Vertex AI Agent Builder | Google Cloud\\', \\'link\\': \\'https://cloud.google.com/products/agent-builder\\', \\'snippet\\': \\'Build and deploy enterprise ready generative AI experiences · Product highlights · Easily build no code conversational AI agents · Ground in Google search and/or\\\\xa0...\\', \\'body\\': \\'Vertex AI Agent Builder | Google Cloud Page Contents Vertex AI Agent Builder is making generative AI more reliable for the enterprise. Read the blog. Vertex AI Agent Builder Build and deploy enterprise ready generative AI experiences Create AI agents and applications using natural language or a code-first approach. Easily ground your agents or apps in enterprise data with a range of options. Vertex AI Agent Builder gathers all the surfaces and tools that developers need to build their AI agents\\'}, {\\'title\\': \\'AI tools I have found useful w/ research. What do you guys think ...\\', \\'link\\': \\'https://www.reddit.com/r/PhD/comments/14d6g09/ai_tools_i_have_found_useful_w_research_what_do/\\', \\'snippet\\': \"Jun 19, 2023 ... Need help deciding on the best ones, and to identify ones I\\'ve missed: ASSISTANTS (chatbots, multi-purpose) Chat with Open Large Language Models.\", \\'body\\': \\'Reddit - Dive into anything Skip to main content Open menu Open navigation Go to Reddit Home r/PhD A chip A close button Get app Get the Reddit app Log In Log in to Reddit Expand user menu Open settings menu Log In / Sign Up Advertise on Reddit Shop Collectible Avatars Get the Reddit app Scan this QR code to download the app now Or check it out in the app stores Go to PhD r/PhD r/PhD A subreddit dedicated to PhDs. Members Online • [deleted] ADMIN MOD AI tools I have found useful w/ research.\\'}]', call_id='call_bNGwWFsfeTwDhtIpsI6GYISR')]\n", + "---------- Google_Search_Agent ----------\n", + "Tool calls:\n", + "google_search({\"query\":\"no code tools for building multi agent AI systems literature review\",\"num_results\":3}) = [{'title': 'Literature Review — AutoGen', 'link': 'https://microsoft.github.io/autogen/dev//user-guide/agentchat-user-guide/examples/literature-review.html', 'snippet': 'run( task=\"Write a literature review on no code tools for building multi agent ai systems\", ) ... ### Conclusion No-code tools for building multi-agent AI systems\\xa0...', 'body': 'Literature Review — AutoGen Skip to main content Back to top Ctrl + K AutoGen 0.4 is a work in progress. Go here to find the 0.2 documentation. User Guide Packages API Reference Twitter GitHub PyPI User Guide Packages API Reference Twitter GitHub PyPI AgentChat Installation Quickstart Tutorial Models Messages Agents Teams Selector Group Chat Swarm Termination Custom Agents Managing State Examples Travel Planning Company Research Literature Review Core Quick Start Core Concepts Agent and'}, {'title': 'Vertex AI Agent Builder | Google Cloud', 'link': 'https://cloud.google.com/products/agent-builder', 'snippet': 'Build and deploy enterprise ready generative AI experiences · Product highlights · Easily build no code conversational AI agents · Ground in Google search and/or\\xa0...', 'body': 'Vertex AI Agent Builder | Google Cloud Page Contents Vertex AI Agent Builder is making generative AI more reliable for the enterprise. Read the blog. Vertex AI Agent Builder Build and deploy enterprise ready generative AI experiences Create AI agents and applications using natural language or a code-first approach. Easily ground your agents or apps in enterprise data with a range of options. Vertex AI Agent Builder gathers all the surfaces and tools that developers need to build their AI agents'}, {'title': 'AI tools I have found useful w/ research. What do you guys think ...', 'link': 'https://www.reddit.com/r/PhD/comments/14d6g09/ai_tools_i_have_found_useful_w_research_what_do/', 'snippet': \"Jun 19, 2023 ... Need help deciding on the best ones, and to identify ones I've missed: ASSISTANTS (chatbots, multi-purpose) Chat with Open Large Language Models.\", 'body': 'Reddit - Dive into anything Skip to main content Open menu Open navigation Go to Reddit Home r/PhD A chip A close button Get app Get the Reddit app Log In Log in to Reddit Expand user menu Open settings menu Log In / Sign Up Advertise on Reddit Shop Collectible Avatars Get the Reddit app Scan this QR code to download the app now Or check it out in the app stores Go to PhD r/PhD r/PhD A subreddit dedicated to PhDs. Members Online • [deleted] ADMIN MOD AI tools I have found useful w/ research.'}]\n", + "---------- Arxiv_Search_Agent ----------\n", + "[FunctionCall(id='call_ZdmwQGTO03X23GeRn6fwDN8q', arguments='{\"query\":\"no code tools for building multi agent AI systems\",\"max_results\":5}', name='arxiv_search')]\n", + "[Prompt tokens: 719, Completion tokens: 28]\n", + "---------- Arxiv_Search_Agent ----------\n", + "[FunctionExecutionResult(content='[{\\'title\\': \\'AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems\\', \\'authors\\': [\\'Victor Dibia\\', \\'Jingya Chen\\', \\'Gagan Bansal\\', \\'Suff Syed\\', \\'Adam Fourney\\', \\'Erkang Zhu\\', \\'Chi Wang\\', \\'Saleema Amershi\\'], \\'published\\': \\'2024-08-09\\', \\'abstract\\': \\'Multi-agent systems, where multiple agents (generative AI models + tools)\\\\ncollaborate, are emerging as an effective pattern for solving long-running,\\\\ncomplex tasks in numerous domains. However, specifying their parameters (such\\\\nas models, tools, and orchestration mechanisms etc,.) and debugging them\\\\nremains challenging for most developers. To address this challenge, we present\\\\nAUTOGEN STUDIO, a no-code developer tool for rapidly prototyping, debugging,\\\\nand evaluating multi-agent workflows built upon the AUTOGEN framework. AUTOGEN\\\\nSTUDIO offers a web interface and a Python API for representing LLM-enabled\\\\nagents using a declarative (JSON-based) specification. It provides an intuitive\\\\ndrag-and-drop UI for agent workflow specification, interactive evaluation and\\\\ndebugging of workflows, and a gallery of reusable agent components. We\\\\nhighlight four design principles for no-code multi-agent developer tools and\\\\ncontribute an open-source implementation at\\\\nhttps://github.com/microsoft/autogen/tree/main/samples/apps/autogen-studio\\', \\'pdf_url\\': \\'http://arxiv.org/pdf/2408.15247v1\\'}, {\\'title\\': \\'Improving Performance of Commercially Available AI Products in a Multi-Agent Configuration\\', \\'authors\\': [\\'Cory Hymel\\', \\'Sida Peng\\', \\'Kevin Xu\\', \\'Charath Ranganathan\\'], \\'published\\': \\'2024-10-29\\', \\'abstract\\': \\'In recent years, with the rapid advancement of large language models (LLMs),\\\\nmulti-agent systems have become increasingly more capable of practical\\\\napplication. At the same time, the software development industry has had a\\\\nnumber of new AI-powered tools developed that improve the software development\\\\nlifecycle (SDLC). Academically, much attention has been paid to the role of\\\\nmulti-agent systems to the SDLC. And, while single-agent systems have\\\\nfrequently been examined in real-world applications, we have seen comparatively\\\\nfew real-world examples of publicly available commercial tools working together\\\\nin a multi-agent system with measurable improvements. In this experiment we\\\\ntest context sharing between Crowdbotics PRD AI, a tool for generating software\\\\nrequirements using AI, and GitHub Copilot, an AI pair-programming tool. By\\\\nsharing business requirements from PRD AI, we improve the code suggestion\\\\ncapabilities of GitHub Copilot by 13.8% and developer task success rate by\\\\n24.5% -- demonstrating a real-world example of commercially-available AI\\\\nsystems working together with improved outcomes.\\', \\'pdf_url\\': \\'http://arxiv.org/pdf/2410.22129v1\\'}, {\\'title\\': \\'AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML\\', \\'authors\\': [\\'Patara Trirat\\', \\'Wonyong Jeong\\', \\'Sung Ju Hwang\\'], \\'published\\': \\'2024-10-03\\', \\'abstract\\': \"Automated machine learning (AutoML) accelerates AI development by automating\\\\ntasks in the development pipeline, such as optimal model search and\\\\nhyperparameter tuning. Existing AutoML systems often require technical\\\\nexpertise to set up complex tools, which is in general time-consuming and\\\\nrequires a large amount of human effort. Therefore, recent works have started\\\\nexploiting large language models (LLM) to lessen such burden and increase the\\\\nusability of AutoML frameworks via a natural language interface, allowing\\\\nnon-expert users to build their data-driven solutions. These methods, however,\\\\nare usually designed only for a particular process in the AI development\\\\npipeline and do not efficiently use the inherent capacity of the LLMs. This\\\\npaper proposes AutoML-Agent, a novel multi-agent framework tailored for\\\\nfull-pipeline AutoML, i.e., from data retrieval to model deployment.\\\\nAutoML-Agent takes user\\'s task descriptions, facilitates collaboration between\\\\nspecialized LLM agents, and delivers deployment-ready models. Unlike existing\\\\nwork, instead of devising a single plan, we introduce a retrieval-augmented\\\\nplanning strategy to enhance exploration to search for more optimal plans. We\\\\nalso decompose each plan into sub-tasks (e.g., data preprocessing and neural\\\\nnetwork design) each of which is solved by a specialized agent we build via\\\\nprompting executing in parallel, making the search process more efficient.\\\\nMoreover, we propose a multi-stage verification to verify executed results and\\\\nguide the code generation LLM in implementing successful solutions. Extensive\\\\nexperiments on seven downstream tasks using fourteen datasets show that\\\\nAutoML-Agent achieves a higher success rate in automating the full AutoML\\\\nprocess, yielding systems with good performance throughout the diverse domains.\", \\'pdf_url\\': \\'http://arxiv.org/pdf/2410.02958v1\\'}, {\\'title\\': \\'Enhancing Trust in LLM-Based AI Automation Agents: New Considerations and Future Challenges\\', \\'authors\\': [\\'Sivan Schwartz\\', \\'Avi Yaeli\\', \\'Segev Shlomov\\'], \\'published\\': \\'2023-08-10\\', \\'abstract\\': \\'Trust in AI agents has been extensively studied in the literature, resulting\\\\nin significant advancements in our understanding of this field. However, the\\\\nrapid advancements in Large Language Models (LLMs) and the emergence of\\\\nLLM-based AI agent frameworks pose new challenges and opportunities for further\\\\nresearch. In the field of process automation, a new generation of AI-based\\\\nagents has emerged, enabling the execution of complex tasks. At the same time,\\\\nthe process of building automation has become more accessible to business users\\\\nvia user-friendly no-code tools and training mechanisms. This paper explores\\\\nthese new challenges and opportunities, analyzes the main aspects of trust in\\\\nAI agents discussed in existing literature, and identifies specific\\\\nconsiderations and challenges relevant to this new generation of automation\\\\nagents. We also evaluate how nascent products in this category address these\\\\nconsiderations. Finally, we highlight several challenges that the research\\\\ncommunity should address in this evolving landscape.\\', \\'pdf_url\\': \\'http://arxiv.org/pdf/2308.05391v1\\'}, {\\'title\\': \\'AI2Apps: A Visual IDE for Building LLM-based AI Agent Applications\\', \\'authors\\': [\\'Xin Pang\\', \\'Zhucong Li\\', \\'Jiaxiang Chen\\', \\'Yuan Cheng\\', \\'Yinghui Xu\\', \\'Yuan Qi\\'], \\'published\\': \\'2024-04-07\\', \\'abstract\\': \\'We introduce AI2Apps, a Visual Integrated Development Environment (Visual\\\\nIDE) with full-cycle capabilities that accelerates developers to build\\\\ndeployable LLM-based AI agent Applications. This Visual IDE prioritizes both\\\\nthe Integrity of its development tools and the Visuality of its components,\\\\nensuring a smooth and efficient building experience.On one hand, AI2Apps\\\\nintegrates a comprehensive development toolkit ranging from a prototyping\\\\ncanvas and AI-assisted code editor to agent debugger, management system, and\\\\ndeployment tools all within a web-based graphical user interface. On the other\\\\nhand, AI2Apps visualizes reusable front-end and back-end code as intuitive\\\\ndrag-and-drop components. Furthermore, a plugin system named AI2Apps Extension\\\\n(AAE) is designed for Extensibility, showcasing how a new plugin with 20\\\\ncomponents enables web agent to mimic human-like browsing behavior. Our case\\\\nstudy demonstrates substantial efficiency improvements, with AI2Apps reducing\\\\ntoken consumption and API calls when debugging a specific sophisticated\\\\nmultimodal agent by approximately 90% and 80%, respectively. The AI2Apps,\\\\nincluding an online demo, open-source code, and a screencast video, is now\\\\npublicly accessible.\\', \\'pdf_url\\': \\'http://arxiv.org/pdf/2404.04902v1\\'}]', call_id='call_ZdmwQGTO03X23GeRn6fwDN8q')]\n", + "---------- Arxiv_Search_Agent ----------\n", + "Tool calls:\n", + "arxiv_search({\"query\":\"no code tools for building multi agent AI systems\",\"max_results\":5}) = [{'title': 'AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems', 'authors': ['Victor Dibia', 'Jingya Chen', 'Gagan Bansal', 'Suff Syed', 'Adam Fourney', 'Erkang Zhu', 'Chi Wang', 'Saleema Amershi'], 'published': '2024-08-09', 'abstract': 'Multi-agent systems, where multiple agents (generative AI models + tools)\\ncollaborate, are emerging as an effective pattern for solving long-running,\\ncomplex tasks in numerous domains. However, specifying their parameters (such\\nas models, tools, and orchestration mechanisms etc,.) and debugging them\\nremains challenging for most developers. To address this challenge, we present\\nAUTOGEN STUDIO, a no-code developer tool for rapidly prototyping, debugging,\\nand evaluating multi-agent workflows built upon the AUTOGEN framework. AUTOGEN\\nSTUDIO offers a web interface and a Python API for representing LLM-enabled\\nagents using a declarative (JSON-based) specification. It provides an intuitive\\ndrag-and-drop UI for agent workflow specification, interactive evaluation and\\ndebugging of workflows, and a gallery of reusable agent components. We\\nhighlight four design principles for no-code multi-agent developer tools and\\ncontribute an open-source implementation at\\nhttps://github.com/microsoft/autogen/tree/main/samples/apps/autogen-studio', 'pdf_url': 'http://arxiv.org/pdf/2408.15247v1'}, {'title': 'Improving Performance of Commercially Available AI Products in a Multi-Agent Configuration', 'authors': ['Cory Hymel', 'Sida Peng', 'Kevin Xu', 'Charath Ranganathan'], 'published': '2024-10-29', 'abstract': 'In recent years, with the rapid advancement of large language models (LLMs),\\nmulti-agent systems have become increasingly more capable of practical\\napplication. At the same time, the software development industry has had a\\nnumber of new AI-powered tools developed that improve the software development\\nlifecycle (SDLC). Academically, much attention has been paid to the role of\\nmulti-agent systems to the SDLC. And, while single-agent systems have\\nfrequently been examined in real-world applications, we have seen comparatively\\nfew real-world examples of publicly available commercial tools working together\\nin a multi-agent system with measurable improvements. In this experiment we\\ntest context sharing between Crowdbotics PRD AI, a tool for generating software\\nrequirements using AI, and GitHub Copilot, an AI pair-programming tool. By\\nsharing business requirements from PRD AI, we improve the code suggestion\\ncapabilities of GitHub Copilot by 13.8% and developer task success rate by\\n24.5% -- demonstrating a real-world example of commercially-available AI\\nsystems working together with improved outcomes.', 'pdf_url': 'http://arxiv.org/pdf/2410.22129v1'}, {'title': 'AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML', 'authors': ['Patara Trirat', 'Wonyong Jeong', 'Sung Ju Hwang'], 'published': '2024-10-03', 'abstract': \"Automated machine learning (AutoML) accelerates AI development by automating\\ntasks in the development pipeline, such as optimal model search and\\nhyperparameter tuning. Existing AutoML systems often require technical\\nexpertise to set up complex tools, which is in general time-consuming and\\nrequires a large amount of human effort. Therefore, recent works have started\\nexploiting large language models (LLM) to lessen such burden and increase the\\nusability of AutoML frameworks via a natural language interface, allowing\\nnon-expert users to build their data-driven solutions. These methods, however,\\nare usually designed only for a particular process in the AI development\\npipeline and do not efficiently use the inherent capacity of the LLMs. This\\npaper proposes AutoML-Agent, a novel multi-agent framework tailored for\\nfull-pipeline AutoML, i.e., from data retrieval to model deployment.\\nAutoML-Agent takes user's task descriptions, facilitates collaboration between\\nspecialized LLM agents, and delivers deployment-ready models. Unlike existing\\nwork, instead of devising a single plan, we introduce a retrieval-augmented\\nplanning strategy to enhance exploration to search for more optimal plans. We\\nalso decompose each plan into sub-tasks (e.g., data preprocessing and neural\\nnetwork design) each of which is solved by a specialized agent we build via\\nprompting executing in parallel, making the search process more efficient.\\nMoreover, we propose a multi-stage verification to verify executed results and\\nguide the code generation LLM in implementing successful solutions. Extensive\\nexperiments on seven downstream tasks using fourteen datasets show that\\nAutoML-Agent achieves a higher success rate in automating the full AutoML\\nprocess, yielding systems with good performance throughout the diverse domains.\", 'pdf_url': 'http://arxiv.org/pdf/2410.02958v1'}, {'title': 'Enhancing Trust in LLM-Based AI Automation Agents: New Considerations and Future Challenges', 'authors': ['Sivan Schwartz', 'Avi Yaeli', 'Segev Shlomov'], 'published': '2023-08-10', 'abstract': 'Trust in AI agents has been extensively studied in the literature, resulting\\nin significant advancements in our understanding of this field. However, the\\nrapid advancements in Large Language Models (LLMs) and the emergence of\\nLLM-based AI agent frameworks pose new challenges and opportunities for further\\nresearch. In the field of process automation, a new generation of AI-based\\nagents has emerged, enabling the execution of complex tasks. At the same time,\\nthe process of building automation has become more accessible to business users\\nvia user-friendly no-code tools and training mechanisms. This paper explores\\nthese new challenges and opportunities, analyzes the main aspects of trust in\\nAI agents discussed in existing literature, and identifies specific\\nconsiderations and challenges relevant to this new generation of automation\\nagents. We also evaluate how nascent products in this category address these\\nconsiderations. Finally, we highlight several challenges that the research\\ncommunity should address in this evolving landscape.', 'pdf_url': 'http://arxiv.org/pdf/2308.05391v1'}, {'title': 'AI2Apps: A Visual IDE for Building LLM-based AI Agent Applications', 'authors': ['Xin Pang', 'Zhucong Li', 'Jiaxiang Chen', 'Yuan Cheng', 'Yinghui Xu', 'Yuan Qi'], 'published': '2024-04-07', 'abstract': 'We introduce AI2Apps, a Visual Integrated Development Environment (Visual\\nIDE) with full-cycle capabilities that accelerates developers to build\\ndeployable LLM-based AI agent Applications. This Visual IDE prioritizes both\\nthe Integrity of its development tools and the Visuality of its components,\\nensuring a smooth and efficient building experience.On one hand, AI2Apps\\nintegrates a comprehensive development toolkit ranging from a prototyping\\ncanvas and AI-assisted code editor to agent debugger, management system, and\\ndeployment tools all within a web-based graphical user interface. On the other\\nhand, AI2Apps visualizes reusable front-end and back-end code as intuitive\\ndrag-and-drop components. Furthermore, a plugin system named AI2Apps Extension\\n(AAE) is designed for Extensibility, showcasing how a new plugin with 20\\ncomponents enables web agent to mimic human-like browsing behavior. Our case\\nstudy demonstrates substantial efficiency improvements, with AI2Apps reducing\\ntoken consumption and API calls when debugging a specific sophisticated\\nmultimodal agent by approximately 90% and 80%, respectively. The AI2Apps,\\nincluding an online demo, open-source code, and a screencast video, is now\\npublicly accessible.', 'pdf_url': 'http://arxiv.org/pdf/2404.04902v1'}]\n", + "---------- Report_Agent ----------\n", + "## Literature Review on No-Code Tools for Building Multi-Agent AI Systems\n", "\n", - "[FunctionCall(id='call_uJyuIbKg0XGXTqozjBMUCQqX', arguments='{\"query\":\"no code tools for building multi agent AI systems\",\"num_results\":5,\"max_chars\":1000}', name='google_search')]\n", - "From: Google_Search_Agent\n", - "--------------------------------------------------------------------------- \n", - "\u001b[91m[2024-10-08T13:16:39.878814], tool_agent_for_Google_Search_Agent:\u001b[0m\n", + "### Introduction\n", "\n", - "[FunctionExecutionResult(content='[{\\'title\\': \\'AutoGen Studio: A No-Code Developer Tool for Building and ...\\', \\'link\\': \\'https://arxiv.org/abs/2408.15247\\', \\'snippet\\': \\'Aug 9, 2024 ... Abstract:Multi-agent systems, where multiple agents (generative AI models + tools) collaborate, are emerging as an effective pattern for\\\\xa0...\\', \\'body\\': \\'[2408.15247] AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems Skip to main content We gratefully acknowledge support from the Simons Foundation, member institutions , and all contributors. Donate > cs > arXiv:2408.15247 Help | Advanced Search All fields Title Author Abstract Comments Journal reference ACM classification MSC classification Report number arXiv identifier DOI ORCID arXiv author ID Help pages Full text Search open search GO open navigation menu quick links Login Help Pages About Computer Science > Software Engineering arXiv:2408.15247 (cs) [Submitted on 9 Aug 2024] Title: AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems Authors: Victor Dibia , Jingya Chen , Gagan Bansal , Suff Syed , Adam Fourney , Erkang Zhu , Chi Wang , Saleema Amershi View a PDF of the paper titled AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems, by Victor Dibia and 7 other authors View\\'}, {\\'title\\': \\'AutoGen Studio: A No-Code Developer Tool for Building and ...\\', \\'link\\': \\'https://www.microsoft.com/en-us/research/publication/autogen-studio-a-no-code-developer-tool-for-building-and-debugging-multi-agent-systems/\\', \\'snippet\\': \\'Aug 2, 2024 ... Multi-agent systems, where multiple agents (generative AI models + tools) collaborate, are emerging as an effective pattern for solving\\\\xa0...\\', \\'body\\': \\'Your request has been blocked. This could be due to several reasons. Skip to main content Microsoft Microsoft 365 Teams Copilot Windows Surface Xbox Deals Small Business Support More All Microsoft Office Windows Surface Xbox Deals Support Software Windows Apps OneDrive Outlook Skype OneNote Microsoft Teams Microsoft Edge PCs & Devices Computers Shop Xbox Accessories VR & mixed reality Phones Entertainment Xbox Game Pass Ultimate Xbox Live Gold Xbox games PC games Windows digital games Movies & TV Business Microsoft Azure Microsoft Dynamics 365 Microsoft 365 Microsoft Industry Data platform Microsoft Advertising Licensing Shop Business Developer & IT .NET Visual Studio Windows Server Windows Dev Center Docs Other Microsoft Rewards Free downloads & security Education Store locations Gift cards View Sitemap Search Search Microsoft.com Cancel Your current User-Agent string appears to be from an automated process, if this is incorrect, please click this link: United States English\\'}, {\\'title\\': \\'Insights and Learnings from Building a Complex Multi-Agent System ...\\', \\'link\\': \\'https://www.reddit.com/r/LangChain/comments/1byz3lr/insights_and_learnings_from_building_a_complex/\\', \\'snippet\\': \"Apr 8, 2024 ... I\\'m a business owner and a tech guy with a background in math, coding, and ML. Since early 2023, I\\'ve fallen in love with the LLM world. So, I\\\\xa0...\", \\'body\\': \"You\\'ve been blocked by network security. To continue, log in to your Reddit account or use your developer token If you think you\\'ve been blocked by mistake, file a ticket below and we\\'ll look into it. Log in File a ticket\"}, {\\'title\\': \\'Multi Agents System (MAS) Builder - Build your AI Workforce\\', \\'link\\': \\'https://relevanceai.com/multi-agents\\', \\'snippet\\': \\'Mar 10, 2024 ... Easily build a multi-agent system. AI workers working collaboratively. No coding required.\\', \\'body\\': \\'Multi Agents System (MAS) Builder - Build your AI Workforce Recruit Bosh, the AI BDR Agent, and book meetings on autopilot. Recruit Bosh, the AI BDR Agent, and book meetings on autopilot. Register Learn more Product AI Agents Agent Teams AI Tools Integrations API Function Sales Marketing Customer Support Research Operations Agents Bosh the Sales Agent Inbound - AI SDR Outbound - AI BDR Lima the Lifecycle Agent Resources Blog Customers Documentation\\\\u200b Javascript SDK Python SDK\\\\u200b Templates Building the AI Workforce What is the AI Workforce? Enterprise Pricing Login Sign Up Product AI Agents Agent Teams AI Tools Custom Actions for GPTs API By Function Sales Marketing Customer Support Research Operations Agents Bosh the Sales Agent Inbound - AI SDR Outbound - AI SDR Resources Blog Documentation Workflows Javascript SDK Python SDK Templates Building the AI Workforce Enterprise Pricing Log in Sign up AI Agent Teams Build a Multi Agent System Create your own AI team that work collaboratively\\'}, {\\'title\\': \\'Crew AI\\', \\'link\\': \\'https://www.crewai.com/\\', \\'snippet\\': \"Start by using CrewAI\\'s framework or UI Studio to build your multi-agent automations—whether coding from scratch or leveraging our no-code tools and templates.\", \\'body\\': \\'Crew AI Get the Inside Scoop First! Join Our Exclusive Waitlist Home Enterprise Open Source Login Start Enterprise Trial crewAI © Copyright 2024 Log in Start Enterprise Trial The Leading Multi-Agent Platform The Leading Multi-Agent Platform Streamline workflows across industries with powerful AI agents. Build and deploy automated workflows using any LLM and cloud platform. Start Free Trial I Want A Demo 100,000,000+ 75,000,000 50,000,000 25,000,000 10,000,000 7,500,000 5,000,000 2,500,000 1,000,000 750,000 500,000 250,000 100,000 75,000 50,000 25,000 10,000 5,000 2,500 1,000 500 250 100 50 10 0 Multi-Agent Crews run using CrewAI Trusted By Industry Leaders The Complete Platform for Multi-Agent Automation 1. Build Quickly Start by using CrewAI’s framework or UI Studio to build your multi-agent automations—whether coding from scratch or leveraging our no-code tools and templates. 2. Deploy Confidently Move the crews you built to production with powerful tools for different deployment\\'}]', call_id='call_uJyuIbKg0XGXTqozjBMUCQqX')]\n", - "From: tool_agent_for_Google_Search_Agent\n", - "--------------------------------------------------------------------------- \n", - "\u001b[91m[2024-10-08T13:16:49.739108], Google_Search_Agent:\u001b[0m\n", + "The emergence of multi-agent systems (MAS) has transformed various domains by enabling collaboration among multiple agents—ranging from generative AI models to orchestrated tools—to solve complex, long-term tasks. However, the traditional development of these systems often requires substantial technical expertise, making it inaccessible for non-developers. The introduction of no-code platforms aims to shift this paradigm, allowing users without formal programming knowledge to design, debug, and deploy multi-agent systems. This review synthesizes current literature concerning no-code tools developed for building multi-agent AI systems, highlighting recent advancements and emerging trends.\n", "\n", - "### Literature Review on No-Code Tools for Building Multi-Agent AI Systems\n", + "### No-Code Development Tools\n", "\n", - "The advent of no-code and low-code platforms has revolutionized the development of software applications, including multi-agent AI systems. These tools enable users, regardless of their technical background, to create sophisticated systems through visual interfaces and pre-built components. This literature review explores the current landscape of no-code tools specifically designed for building multi-agent AI systems, examining their capabilities, features, and potential use cases.\n", + "#### AutoGen Studio\n", "\n", - "#### 1. **AutoGen Studio**\n", - "One of the prominent tools in this domain is **AutoGen Studio**, which provides a no-code environment for designing and debugging multi-agent systems. According to a recent paper published in **arXiv**, this tool focuses on facilitating collaboration among different agents, including generative AI models and associated tools. It emphasizes usability, allowing developers to build complex systems without extensive programming knowledge (Dibia et al., 2024).\n", + "One of the prominent no-code tools is **AutoGen Studio**, developed by Dibia et al. (2024). This tool provides a web interface and a declarative specification method utilizing JSON, enabling rapid prototyping, debugging, and evaluating multi-agent workflows. The drag-and-drop capabilities streamline the design process, making complex interactions between agents more manageable. The framework operates on four primary design principles that cater specifically to no-code development, contributing to an accessible pathway for users to harness multi-agent frameworks for various applications (Dibia et al., 2024).\n", "\n", - "#### 2. **Multi Agents System (MAS) Builder**\n", - "Another notable platform is the **Multi Agents System (MAS) Builder** by **Relevance AI**. This tool allows users to construct AI worker systems that can operate collaboratively without requiring any coding skills. The platform highlights features such as the ability to create and deploy AI teams optimized for tasks like sales and customer support, showcasing the practical applications of no-code tools in business environments (Relevance AI, 2024).\n", + "#### AI2Apps Visual IDE\n", "\n", - "#### 3. **Crew AI**\n", - "**Crew AI** offers a comprehensive framework for automating workflows through multi-agent systems. It includes a UI Studio that facilitates the creation of automations without programming. Users can leverage pre-configured templates and build agents that execute tasks across various domains. This flexibility makes it suitable for industries seeking to enhance operational efficiency through automated systems (Crew AI, 2024).\n", + "Another notable tool is **AI2Apps**, described by Pang et al. (2024). It serves as a Visual Integrated Development Environment that incorporates a comprehensive set of tools from prototyping to deployment. The platform's user-friendly interface allows for the visualization of code through drag-and-drop components, facilitating smoother integration of different agents. An extension system enhances the platform's capabilities, showcasing the potential for customization and scalability in agent application development. The reported efficiency improvements in token consumption and API calls indicate substantial benefits in user-centric design (Pang et al., 2024).\n", "\n", - "#### 4. **Insights and Community Experiences**\n", - "Additionally, community discussions and insights shared on platforms like **Reddit** provide anecdotal evidence of the effectiveness and user experiences when employing no-code tools for multi-agent systems. Users share their journeys in building complex systems, highlighting both successes and challenges faced during development (April 2024).\n", + "### Performance Enhancements in Multi-Agent Configurations\n", "\n", - "### Conclusion\n", - "The evolution of no-code tools has significantly lowered the barrier to entry for developing multi-agent AI systems. Platforms such as AutoGen Studio, MAS Builder, and Crew AI exemplify the potential for creating sophisticated systems without traditional coding requirements. As these tools continue to grow in capability and user adoption, they promise to democratize AI development and enable a wider range of professionals to leverage AI technologies in their work.\n", - "\n", - "### References\n", - "1. Dibia, V., Chen, J., Bansal, G., Syed, S., Fourney, A., Zhu, E., Wang, C., & Amershi, S. (2024). AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems. arXiv.\n", - "2. Relevance AI. (2024). Multi Agents System (MAS) Builder - Build your AI Workforce. Retrieved from [Relevance AI](https://relevanceai.com/multi-agents).\n", - "3. Crew AI. (2024). The Leading Multi-Agent Platform. Retrieved from [Crew AI](https://www.crewai.com/).\n", - "4. Insights from Community Discussions. Reddit. (April 2024). \n", - "\n", - "This review highlights the emerging trends and significant tools in the no-code multi-agent AI space, indicating a shift toward more accessible AI system development.\n", - "From: Google_Search_Agent\n", - "--------------------------------------------------------------------------- \n", - "\u001b[91m[2024-10-08T13:16:50.542039], Arxiv_Search_Agent:\u001b[0m\n", - "\n", - "[FunctionCall(id='call_HnNhdJzH3xCbiofbbcoqzFDP', arguments='{\"query\":\"no code tools multi agent AI systems\",\"max_results\":5}', name='arxiv_search')]\n", - "From: Arxiv_Search_Agent\n", - "--------------------------------------------------------------------------- \n", - "\u001b[91m[2024-10-08T13:16:52.486634], tool_agent_for_Arxiv_Search_Agent:\u001b[0m\n", - "\n", - "[FunctionExecutionResult(content='[{\\'title\\': \\'AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems\\', \\'authors\\': [\\'Victor Dibia\\', \\'Jingya Chen\\', \\'Gagan Bansal\\', \\'Suff Syed\\', \\'Adam Fourney\\', \\'Erkang Zhu\\', \\'Chi Wang\\', \\'Saleema Amershi\\'], \\'published\\': \\'2024-08-09\\', \\'abstract\\': \\'Multi-agent systems, where multiple agents (generative AI models + tools)\\\\ncollaborate, are emerging as an effective pattern for solving long-running,\\\\ncomplex tasks in numerous domains. However, specifying their parameters (such\\\\nas models, tools, and orchestration mechanisms etc,.) and debugging them\\\\nremains challenging for most developers. To address this challenge, we present\\\\nAUTOGEN STUDIO, a no-code developer tool for rapidly prototyping, debugging,\\\\nand evaluating multi-agent workflows built upon the AUTOGEN framework. AUTOGEN\\\\nSTUDIO offers a web interface and a Python API for representing LLM-enabled\\\\nagents using a declarative (JSON-based) specification. It provides an intuitive\\\\ndrag-and-drop UI for agent workflow specification, interactive evaluation and\\\\ndebugging of workflows, and a gallery of reusable agent components. We\\\\nhighlight four design principles for no-code multi-agent developer tools and\\\\ncontribute an open-source implementation at\\\\nhttps://github.com/microsoft/autogen/tree/main/samples/apps/autogen-studio\\', \\'pdf_url\\': \\'http://arxiv.org/pdf/2408.15247v1\\'}, {\\'title\\': \\'Collaboration of AI Agents via Cooperative Multi-Agent Deep Reinforcement Learning\\', \\'authors\\': [\\'Niranjan Balachandar\\', \\'Justin Dieter\\', \\'Govardana Sachithanandam Ramachandran\\'], \\'published\\': \\'2019-06-30\\', \\'abstract\\': \\'There are many AI tasks involving multiple interacting agents where agents\\\\nshould learn to cooperate and collaborate to effectively perform the task. Here\\\\nwe develop and evaluate various multi-agent protocols to train agents to\\\\ncollaborate with teammates in grid soccer. We train and evaluate our\\\\nmulti-agent methods against a team operating with a smart hand-coded policy. As\\\\na baseline, we train agents concurrently and independently, with no\\\\ncommunication. Our collaborative protocols were parameter sharing, coordinated\\\\nlearning with communication, and counterfactual policy gradients. Against the\\\\nhand-coded team, the team trained with parameter sharing and the team trained\\\\nwith coordinated learning performed the best, scoring on 89.5% and 94.5% of\\\\nepisodes respectively when playing against the hand-coded team. Against the\\\\nparameter sharing team, with adversarial training the coordinated learning team\\\\nscored on 75% of the episodes, indicating it is the most adaptable of our\\\\nmethods. The insights gained from our work can be applied to other domains\\\\nwhere multi-agent collaboration could be beneficial.\\', \\'pdf_url\\': \\'http://arxiv.org/pdf/1907.00327v1\\'}, {\\'title\\': \\'Levels of AI Agents: from Rules to Large Language Models\\', \\'authors\\': [\\'Yu Huang\\'], \\'published\\': \\'2024-03-06\\', \\'abstract\\': \\'AI agents are defined as artificial entities to perceive the environment,\\\\nmake decisions and take actions. Inspired by the 6 levels of autonomous driving\\\\nby Society of Automotive Engineers, the AI agents are also categorized based on\\\\nutilities and strongness, as the following levels: L0, no AI, with tools taking\\\\ninto account perception plus actions; L1, using rule-based AI; L2, making\\\\nrule-based AI replaced by IL/RL-based AI, with additional reasoning & decision\\\\nmaking; L3, applying LLM-based AI instead of IL/RL-based AI, additionally\\\\nsetting up memory & reflection; L4, based on L3, facilitating autonomous\\\\nlearning & generalization; L5, based on L4, appending personality of emotion\\\\nand character and collaborative behavior with multi-agents.\\', \\'pdf_url\\': \\'http://arxiv.org/pdf/2405.06643v1\\'}, {\\'title\\': \\'HAICOSYSTEM: An Ecosystem for Sandboxing Safety Risks in Human-AI Interactions\\', \\'authors\\': [\\'Xuhui Zhou\\', \\'Hyunwoo Kim\\', \\'Faeze Brahman\\', \\'Liwei Jiang\\', \\'Hao Zhu\\', \\'Ximing Lu\\', \\'Frank Xu\\', \\'Bill Yuchen Lin\\', \\'Yejin Choi\\', \\'Niloofar Mireshghallah\\', \\'Ronan Le Bras\\', \\'Maarten Sap\\'], \\'published\\': \\'2024-09-24\\', \\'abstract\\': \"AI agents are increasingly autonomous in their interactions with human users\\\\nand tools, leading to increased interactional safety risks. We present\\\\nHAICOSYSTEM, a framework examining AI agent safety within diverse and complex\\\\nsocial interactions. HAICOSYSTEM features a modular sandbox environment that\\\\nsimulates multi-turn interactions between human users and AI agents, where the\\\\nAI agents are equipped with a variety of tools (e.g., patient management\\\\nplatforms) to navigate diverse scenarios (e.g., a user attempting to access\\\\nother patients\\' profiles). To examine the safety of AI agents in these\\\\ninteractions, we develop a comprehensive multi-dimensional evaluation framework\\\\nthat uses metrics covering operational, content-related, societal, and legal\\\\nrisks. Through running 1840 simulations based on 92 scenarios across seven\\\\ndomains (e.g., healthcare, finance, education), we demonstrate that HAICOSYSTEM\\\\ncan emulate realistic user-AI interactions and complex tool use by AI agents.\\\\nOur experiments show that state-of-the-art LLMs, both proprietary and\\\\nopen-sourced, exhibit safety risks in over 50\\\\\\\\% cases, with models generally\\\\nshowing higher risks when interacting with simulated malicious users. Our\\\\nfindings highlight the ongoing challenge of building agents that can safely\\\\nnavigate complex interactions, particularly when faced with malicious users. To\\\\nfoster the AI agent safety ecosystem, we release a code platform that allows\\\\npractitioners to create custom scenarios, simulate interactions, and evaluate\\\\nthe safety and performance of their agents.\", \\'pdf_url\\': \\'http://arxiv.org/pdf/2409.16427v2\\'}, {\\'title\\': \\'The Partially Observable Asynchronous Multi-Agent Cooperation Challenge\\', \\'authors\\': [\\'Meng Yao\\', \\'Qiyue Yin\\', \\'Jun Yang\\', \\'Tongtong Yu\\', \\'Shengqi Shen\\', \\'Junge Zhang\\', \\'Bin Liang\\', \\'Kaiqi Huang\\'], \\'published\\': \\'2021-12-07\\', \\'abstract\\': \\'Multi-agent reinforcement learning (MARL) has received increasing attention\\\\nfor its applications in various domains. Researchers have paid much attention\\\\non its partially observable and cooperative settings for meeting real-world\\\\nrequirements. For testing performance of different algorithms, standardized\\\\nenvironments are designed such as the StarCraft Multi-Agent Challenge, which is\\\\none of the most successful MARL benchmarks. To our best knowledge, most of\\\\ncurrent environments are synchronous, where agents execute actions in the same\\\\npace. However, heterogeneous agents usually have their own action spaces and\\\\nthere is no guarantee for actions from different agents to have the same\\\\nexecuted cycle, which leads to asynchronous multi-agent cooperation. Inspired\\\\nfrom the Wargame, a confrontation game between two armies abstracted from real\\\\nworld environment, we propose the first Partially Observable Asynchronous\\\\nmulti-agent Cooperation challenge (POAC) for the MARL community. Specifically,\\\\nPOAC supports two teams of heterogeneous agents to fight with each other, where\\\\nan agent selects actions based on its own observations and cooperates\\\\nasynchronously with its allies. Moreover, POAC is a light weight, flexible and\\\\neasy to use environment, which can be configured by users to meet different\\\\nexperimental requirements such as self-play model, human-AI model and so on.\\\\nAlong with our benchmark, we offer six game scenarios of varying difficulties\\\\nwith the built-in rule-based AI as opponents. Finally, since most MARL\\\\nalgorithms are designed for synchronous agents, we revise several\\\\nrepresentatives to meet the asynchronous setting, and the relatively poor\\\\nexperimental results validate the challenge of POAC. Source code is released in\\\\n\\\\\\\\url{http://turingai.ia.ac.cn/data\\\\\\\\_center/show}.\\', \\'pdf_url\\': \\'http://arxiv.org/pdf/2112.03809v1\\'}]', call_id='call_HnNhdJzH3xCbiofbbcoqzFDP')]\n", - "From: tool_agent_for_Arxiv_Search_Agent\n", - "--------------------------------------------------------------------------- \n", - "\u001b[91m[2024-10-08T13:17:12.845506], Arxiv_Search_Agent:\u001b[0m\n", + "Hymel et al. (2024) examined the collaborative performance of commercially available AI tools, demonstrating a measurable improvement when integrating multiple agents in a shared configuration. Their experiments showcased how cooperation between tools like Crowdbotics PRD AI and GitHub Copilot significantly improved task success rates, illustrating the practical benefits of employing no-code tools in multi-agent environments. This synergy reflects the critical need for frameworks that inherently support such integrations, especially through no-code mechanisms, to enhance user experience and productivity (Hymel et al., 2024).\n", "\n", - "### Literature Review on No-Code Tools for Building Multi-Agent AI Systems\n", + "### Trust and Usability in AI Agents\n", "\n", - "The development of multi-agent AI systems has been significantly enhanced by the emergence of no-code tools, allowing a broader range of users to engage in the creation and management of complex AI applications without extensive programming knowledge. This literature review synthesizes current research on no-code tools tailored for building multi-agent AI systems, discussing their functionalities, design, and implications.\n", + "The concept of trust in AI, particularly in LLM-based automation agents, has gained attention. Schwartz et al. (2023) addressed the challenges and considerations unique to this new generation of agents, highlighting how no-code platforms ease access and usability for non-technical users. The paper emphasizes the need for further research into the trust factors integral to effective multi-agent systems, advocating for a user-centric approach in the design and evaluation of these no-code tools (Schwartz et al., 2023).\n", "\n", - "#### 1. AutoGen Studio\n", - "**AutoGen Studio** is a cutting-edge no-code developer tool specifically designed for building and debugging multi-agent systems. Dibia et al. (2024) highlight that this platform simplifies the development process through a web interface that supports drag-and-drop functionalities for creating agent workflows. With a Python API and a JSON-based specification for representing agents, AutoGen Studio allows users to prototype and evaluate workflows effortlessly. This tool not only enhances usability but also fosters collaboration among various generative AI models and tools. The authors emphasize four core design principles that inform the development of no-code tools, aiming to streamline the creation of multi-agent systems (Dibia et al., 2024). [Read the paper here](http://arxiv.org/pdf/2408.15247v1).\n", + "### Full-Pipeline AutoML with Multi-Agent Systems\n", "\n", - "#### 2. Levels of AI Agents\n", - "In a conceptual exploration, Huang (2024) classifies AI agents into levels based on their capabilities, ranging from rule-based systems (L1) to advanced large language models (LLMs) (L3). This classification is crucial for understanding the potential complexity and collaboration among agents in multi-agent frameworks. These levels imply varying degrees of autonomy and decision-making, which can impact the design of no-code tools intended for multi-agent systems. The consideration of these levels is vital for developing platforms that allow effective integration and collaboration between diverse agent types (Huang, 2024). [Read the paper here](http://arxiv.org/pdf/2405.06643v1).\n", - "\n", - "#### 3. HAICOSYSTEM\n", - "**HAICOSYSTEM** presents a novel framework that examines safety risks in human-AI interactions, focusing on multi-agent systems' operational complexities. Zhou et al. (2024) discuss a modular sandbox environment that simulates interactions between AI agents and human users across various scenarios. The framework allows practitioners to evaluate and ensure the safety and performance of agents, emphasizing a crucial aspect that no-code tools must address—handling operational risks in real-time interactions. This research underscores the need for built-in safety and evaluation features within no-code platforms for multi-agent systems (Zhou et al., 2024). [Read the paper here](http://arxiv.org/pdf/2409.16427v2).\n", - "\n", - "#### 4. Collaboration Protocols in AI Agents\n", - "Balachandar et al. (2019) focus on the collaborative protocols that enable multiple agents to work together effectively in a competitive setting like grid soccer. Their work discusses various strategies for cooperation and highlights the importance of communication among agents. This foundational understanding of multi-agent interaction can influence the design and implementation of no-code tools by offering insights into how agents can collaborate seamlessly without the need for extensive programming (Balachandar et al., 2019). [Read the paper here](http://arxiv.org/pdf/1907.00327v1).\n", - "\n", - "#### 5. Asynchronous Multi-Agent Cooperation\n", - "In a more technical aspect, Yao et al. (2021) introduce the **Partially Observable Asynchronous Multi-Agent Cooperation (POAC)** challenge to evaluate multi-agent reinforcement learning (MARL) algorithms in asynchronous environments. This environment design shifts the focus from synchronous operations, which are common in many existing no-code tools, to more realistic settings reflecting real-world complexities. The flexibility and adaptability required for asynchronous operations highlight critical design considerations for the next generation of no-code development tools (Yao et al., 2021). [Read the paper here](http://arxiv.org/pdf/2112.03809v1).\n", + "The **AutoML-Agent** framework proposed by Trirat et al. (2024) brings another layer of innovation to the no-code landscape. This framework enhances existing automated machine learning processes by using multiple specialized agents that collaboratively manage the full AI development pipeline from data retrieval to model deployment. The novelty lies in its retrieval-augmented planning strategy, which allows for efficient task decomposition and parallel execution, optimizing the overall development experience for non-experts (Trirat et al., 2024).\n", "\n", "### Conclusion\n", - "No-code tools for building multi-agent AI systems are rapidly evolving, offering unprecedented access to AI development for users without programming expertise. Tools like AutoGen Studio provide essential frameworks for agent collaboration, while safety frameworks like HAICOSYSTEM remind developers of the necessity of operational integrity. Insights from collaboration protocols and asynchronous environments further inform the development and refinement of these tools. As the landscape grows, these no-code platforms will likely play a pivotal role in democratizing the development of sophisticated multi-agent systems across various domains.\n", - "\n", - "### References\n", - "1. Dibia, V., Chen, J., Bansal, G., Syed, S., Fourney, A., Zhu, E., Wang, C., & Amershi, S. (2024). AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems. arXiv.\n", - "2. Huang, Y. (2024). Levels of AI Agents: from Rules to Large Language Models. arXiv.\n", - "3. Zhou, X., Kim, H., Brahman, F., Jiang, L., Zhu, H., Lu, X., Xu, F., Lin, B. Y., Choi, Y., Mireshghallah, N., Bras, R. L., & Sap, M. (2024). HAICOSYSTEM: An Ecosystem for Sandboxing Safety Risks in Human-AI Interactions. arXiv.\n", - "4. Balachandar, N., Dieter, J., & Ramachandran, G. S. (2019). Collaboration of AI Agents via Cooperative Multi-Agent Deep Reinforcement Learning. arXiv.\n", - "5. Yao, M., Yin, Q., Yang, J., Yu, T., Shen, S., Zhang, J., Liang, B., & Huang, K. (2021). The Partially Observable Asynchronous Multi-Agent Cooperation Challenge. arXiv.\n", - "From: Arxiv_Search_Agent\n", - "--------------------------------------------------------------------------- \n", - "\u001b[91m[2024-10-08T13:17:36.794418], Report_Agent:\u001b[0m\n", "\n", - "### Literature Review on No-Code Tools for Building Multi-Agent AI Systems\n", - "\n", - "The emergence of no-code tools signals a significant shift in the development landscape of multi-agent AI systems, enabling users with minimal programming knowledge to design and implement complex AI applications. This literature review synthesizes current research and practical insights on no-code tools crafted for establishing multi-agent AI systems, emphasizing their features, capabilities, and applications.\n", - "\n", - "#### 1. AutoGen Studio\n", - "**AutoGen Studio** serves as a pioneering no-code platform tailored for the design and debugging of multi-agent systems. Dibia et al. (2024) highlight that this tool utilizes a web-based interface that enables users to construct workflows through intuitive drag-and-drop functionalities. The flexibility offered by a Python API, along with a JSON-based framework for representing agents, streamlines the prototyping and evaluation processes. Such features foster collaboration among various generative AI models and enhance usability, ultimately addressing the diverse needs of non-technical users in constructing multi-agent environments (Dibia et al., 2024). [Read the paper here](http://arxiv.org/pdf/2408.15247v1).\n", - "\n", - "#### 2. Levels of AI Agents\n", - "Huang (2024) introduces a conceptual framework categorizing AI agents by their capabilities. This classification ranges from simple rule-based systems to advanced large language models, underscoring the varying complexities in multi-agent interactions. Understanding these levels aids in informing the design of no-code tools to support effective collaboration among agents of differing capabilities. By integrating awareness of these agent levels, developers can enhance how no-code platforms facilitate interactions within multi-agent systems (Huang, 2024). [Read the paper here](http://arxiv.org/pdf/2405.06643v1).\n", - "\n", - "#### 3. HAICOSYSTEM\n", - "The **HAICOSYSTEM** framework examines the safety considerations inherent in human-AI interactions, especially concerning multi-agent contexts. Zhou et al. (2024) propose a modular sandbox environment that simulates various operational scenarios, allowing practitioners to assess and ensure safety while interacting with agents. This research emphasizes the necessity of incorporating safety evaluation features into no-code platforms for multi-agent systems, ensuring that these tools not only enhance usability but also promote reliable and secure interactions (Zhou et al., 2024). [Read the paper here](http://arxiv.org/pdf/2409.16427v2).\n", - "\n", - "#### 4. Collaboration Protocols in AI Agents\n", - "The investigation by Balachandar et al. (2019) into collaborative protocols among AI agents reveals fundamental strategies that can enhance cooperative behavior in multi-agent systems. Their insights are invaluable for informing the design of no-code platforms, highlighting the importance of effective communication and cooperation among agents. By embedding these collaborative features into no-code tools, developers can facilitate more seamless integration and interaction among agents, which is essential for complex multi-agent tasks (Balachandar et al., 2019). [Read the paper here](http://arxiv.org/pdf/1907.00327v1).\n", - "\n", - "#### 5. Asynchronous Multi-Agent Cooperation\n", - "Yao et al. (2021) present the **Partially Observable Asynchronous Multi-Agent Cooperation (POAC)** challenge, which evaluates the performance of multi-agent reinforcement learning algorithms in asynchronous environments. This design paradigm shifts focus from synchronous operations, commonly found in traditional no-code tools, toward interfaces that reflect realistic interactions. The implications of asynchronous cooperation underscore critical design considerations for developing future no-code tools, emphasizing the need for flexibility and adaptability in systems that work under real-world constraints (Yao et al., 2021). [Read the paper here](http://arxiv.org/pdf/2112.03809v1).\n", - "\n", - "### Conclusion\n", - "No-code tools for developing multi-agent AI systems are rapidly advancing, providing unprecedented opportunities for users without programming skills to engage in complex AI development. Platforms like AutoGen Studio are at the forefront of these innovations, facilitating collaboration and simplifying design processes. Concurrent research on AI agent levels, safety frameworks, collaboration protocols, and asynchronous cooperation further enhances the understanding of requirements for effective no-code tool design. As these tools proliferate, they are poised to democratize access to multi-agent system development across diverse sectors.\n", + "The literature presents a growing array of no-code tools designed to democratize the development of multi-agent systems. Innovations such as AutoGen Studio, AI2Apps, and collaborative frameworks like AutoML-Agent highlight a trend towards user-centric, efficient design that encourages participation beyond technical boundaries. Future research should continue to explore aspects of trust, usability, and integration to further refine these tools and expand their applicability across various domains.\n", "\n", "### References\n", - "1. Dibia, V., Chen, J., Bansal, G., Syed, S., Fourney, A., Zhu, E., Wang, C., & Amershi, S. (2024). AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems. arXiv. [Available here](http://arxiv.org/pdf/2408.15247v1).\n", - "2. Huang, Y. (2024). Levels of AI Agents: from Rules to Large Language Models. arXiv. [Available here](http://arxiv.org/pdf/2405.06643v1).\n", - "3. Zhou, X., Kim, H., Brahman, F., Jiang, L., Zhu, H., Lu, X., Xu, F., Lin, B. Y., Choi, Y., Mireshghallah, N., Bras, R. L., & Sap, M. (2024). HAICOSYSTEM: An Ecosystem for Sandboxing Safety Risks in Human-AI Interactions. arXiv. [Available here](http://arxiv.org/pdf/2409.16427v2).\n", - "4. Balachandar, N., Dieter, J., & Ramachandran, G. S. (2019). Collaboration of AI Agents via Cooperative Multi-Agent Deep Reinforcement Learning. arXiv. [Available here](http://arxiv.org/pdf/1907.00327v1).\n", - "5. Yao, M., Yin, Q., Yang, J., Yu, T., Shen, S., Zhang, J., Liang, B., & Huang, K. (2021). The Partially Observable Asynchronous Multi-Agent Cooperation Challenge. arXiv. [Available here](http://arxiv.org/pdf/2112.03809v1).\n", + "\n", + "- Dibia, V., Chen, J., Bansal, G., Syed, S., Fourney, A., Zhu, E., Wang, C., & Amershi, S. (2024). AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems. *arXiv:2408.15247*.\n", + "- Hymel, C., Peng, S., Xu, K., & Ranganathan, C. (2024). Improving Performance of Commercially Available AI Products in a Multi-Agent Configuration. *arXiv:2410.22129*.\n", + "- Pang, X., Li, Z., Chen, J., Cheng, Y., Xu, Y., & Qi, Y. (2024). AI2Apps: A Visual IDE for Building LLM-based AI Agent Applications. *arXiv:2404.04902*.\n", + "- Schwartz, S., Yaeli, A., & Shlomov, S. (2023). Enhancing Trust in LLM-Based AI Automation Agents: New Considerations and Future Challenges. *arXiv:2308.05391*.\n", + "- Trirat, P., Jeong, W., & Hwang, S. J. (2024). AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML. *arXiv:2410.02958*.\n", "\n", "TERMINATE\n", - "From: Report_Agent" + "[Prompt tokens: 2381, Completion tokens: 1090]\n", + "---------- Summary ----------\n", + "Number of messages: 8\n", + "Finish reason: Text 'TERMINATE' mentioned\n", + "Total prompt tokens: 3223\n", + "Total completion tokens: 1147\n", + "Duration: 17.06 seconds\n" ] }, { "data": { "text/plain": [ - "TeamRunResult(messages=[TextMessage(source='user', content='Write a literature review on no code tools for building multi agent ai systems'), TextMessage(source='Google_Search_Agent', content='### Literature Review on No-Code Tools for Building Multi-Agent AI Systems\\n\\nThe advent of no-code and low-code platforms has revolutionized the development of software applications, including multi-agent AI systems. These tools enable users, regardless of their technical background, to create sophisticated systems through visual interfaces and pre-built components. This literature review explores the current landscape of no-code tools specifically designed for building multi-agent AI systems, examining their capabilities, features, and potential use cases.\\n\\n#### 1. **AutoGen Studio**\\nOne of the prominent tools in this domain is **AutoGen Studio**, which provides a no-code environment for designing and debugging multi-agent systems. According to a recent paper published in **arXiv**, this tool focuses on facilitating collaboration among different agents, including generative AI models and associated tools. It emphasizes usability, allowing developers to build complex systems without extensive programming knowledge (Dibia et al., 2024).\\n\\n#### 2. **Multi Agents System (MAS) Builder**\\nAnother notable platform is the **Multi Agents System (MAS) Builder** by **Relevance AI**. This tool allows users to construct AI worker systems that can operate collaboratively without requiring any coding skills. The platform highlights features such as the ability to create and deploy AI teams optimized for tasks like sales and customer support, showcasing the practical applications of no-code tools in business environments (Relevance AI, 2024).\\n\\n#### 3. **Crew AI**\\n**Crew AI** offers a comprehensive framework for automating workflows through multi-agent systems. It includes a UI Studio that facilitates the creation of automations without programming. Users can leverage pre-configured templates and build agents that execute tasks across various domains. This flexibility makes it suitable for industries seeking to enhance operational efficiency through automated systems (Crew AI, 2024).\\n\\n#### 4. **Insights and Community Experiences**\\nAdditionally, community discussions and insights shared on platforms like **Reddit** provide anecdotal evidence of the effectiveness and user experiences when employing no-code tools for multi-agent systems. Users share their journeys in building complex systems, highlighting both successes and challenges faced during development (April 2024).\\n\\n### Conclusion\\nThe evolution of no-code tools has significantly lowered the barrier to entry for developing multi-agent AI systems. Platforms such as AutoGen Studio, MAS Builder, and Crew AI exemplify the potential for creating sophisticated systems without traditional coding requirements. As these tools continue to grow in capability and user adoption, they promise to democratize AI development and enable a wider range of professionals to leverage AI technologies in their work.\\n\\n### References\\n1. Dibia, V., Chen, J., Bansal, G., Syed, S., Fourney, A., Zhu, E., Wang, C., & Amershi, S. (2024). AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems. arXiv.\\n2. Relevance AI. (2024). Multi Agents System (MAS) Builder - Build your AI Workforce. Retrieved from [Relevance AI](https://relevanceai.com/multi-agents).\\n3. Crew AI. (2024). The Leading Multi-Agent Platform. Retrieved from [Crew AI](https://www.crewai.com/).\\n4. Insights from Community Discussions. Reddit. (April 2024). \\n\\nThis review highlights the emerging trends and significant tools in the no-code multi-agent AI space, indicating a shift toward more accessible AI system development.'), TextMessage(source='Arxiv_Search_Agent', content=\"### Literature Review on No-Code Tools for Building Multi-Agent AI Systems\\n\\nThe development of multi-agent AI systems has been significantly enhanced by the emergence of no-code tools, allowing a broader range of users to engage in the creation and management of complex AI applications without extensive programming knowledge. This literature review synthesizes current research on no-code tools tailored for building multi-agent AI systems, discussing their functionalities, design, and implications.\\n\\n#### 1. AutoGen Studio\\n**AutoGen Studio** is a cutting-edge no-code developer tool specifically designed for building and debugging multi-agent systems. Dibia et al. (2024) highlight that this platform simplifies the development process through a web interface that supports drag-and-drop functionalities for creating agent workflows. With a Python API and a JSON-based specification for representing agents, AutoGen Studio allows users to prototype and evaluate workflows effortlessly. This tool not only enhances usability but also fosters collaboration among various generative AI models and tools. The authors emphasize four core design principles that inform the development of no-code tools, aiming to streamline the creation of multi-agent systems (Dibia et al., 2024). [Read the paper here](http://arxiv.org/pdf/2408.15247v1).\\n\\n#### 2. Levels of AI Agents\\nIn a conceptual exploration, Huang (2024) classifies AI agents into levels based on their capabilities, ranging from rule-based systems (L1) to advanced large language models (LLMs) (L3). This classification is crucial for understanding the potential complexity and collaboration among agents in multi-agent frameworks. These levels imply varying degrees of autonomy and decision-making, which can impact the design of no-code tools intended for multi-agent systems. The consideration of these levels is vital for developing platforms that allow effective integration and collaboration between diverse agent types (Huang, 2024). [Read the paper here](http://arxiv.org/pdf/2405.06643v1).\\n\\n#### 3. HAICOSYSTEM\\n**HAICOSYSTEM** presents a novel framework that examines safety risks in human-AI interactions, focusing on multi-agent systems' operational complexities. Zhou et al. (2024) discuss a modular sandbox environment that simulates interactions between AI agents and human users across various scenarios. The framework allows practitioners to evaluate and ensure the safety and performance of agents, emphasizing a crucial aspect that no-code tools must address—handling operational risks in real-time interactions. This research underscores the need for built-in safety and evaluation features within no-code platforms for multi-agent systems (Zhou et al., 2024). [Read the paper here](http://arxiv.org/pdf/2409.16427v2).\\n\\n#### 4. Collaboration Protocols in AI Agents\\nBalachandar et al. (2019) focus on the collaborative protocols that enable multiple agents to work together effectively in a competitive setting like grid soccer. Their work discusses various strategies for cooperation and highlights the importance of communication among agents. This foundational understanding of multi-agent interaction can influence the design and implementation of no-code tools by offering insights into how agents can collaborate seamlessly without the need for extensive programming (Balachandar et al., 2019). [Read the paper here](http://arxiv.org/pdf/1907.00327v1).\\n\\n#### 5. Asynchronous Multi-Agent Cooperation\\nIn a more technical aspect, Yao et al. (2021) introduce the **Partially Observable Asynchronous Multi-Agent Cooperation (POAC)** challenge to evaluate multi-agent reinforcement learning (MARL) algorithms in asynchronous environments. This environment design shifts the focus from synchronous operations, which are common in many existing no-code tools, to more realistic settings reflecting real-world complexities. The flexibility and adaptability required for asynchronous operations highlight critical design considerations for the next generation of no-code development tools (Yao et al., 2021). [Read the paper here](http://arxiv.org/pdf/2112.03809v1).\\n\\n### Conclusion\\nNo-code tools for building multi-agent AI systems are rapidly evolving, offering unprecedented access to AI development for users without programming expertise. Tools like AutoGen Studio provide essential frameworks for agent collaboration, while safety frameworks like HAICOSYSTEM remind developers of the necessity of operational integrity. Insights from collaboration protocols and asynchronous environments further inform the development and refinement of these tools. As the landscape grows, these no-code platforms will likely play a pivotal role in democratizing the development of sophisticated multi-agent systems across various domains.\\n\\n### References\\n1. Dibia, V., Chen, J., Bansal, G., Syed, S., Fourney, A., Zhu, E., Wang, C., & Amershi, S. (2024). AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems. arXiv.\\n2. Huang, Y. (2024). Levels of AI Agents: from Rules to Large Language Models. arXiv.\\n3. Zhou, X., Kim, H., Brahman, F., Jiang, L., Zhu, H., Lu, X., Xu, F., Lin, B. Y., Choi, Y., Mireshghallah, N., Bras, R. L., & Sap, M. (2024). HAICOSYSTEM: An Ecosystem for Sandboxing Safety Risks in Human-AI Interactions. arXiv.\\n4. Balachandar, N., Dieter, J., & Ramachandran, G. S. (2019). Collaboration of AI Agents via Cooperative Multi-Agent Deep Reinforcement Learning. arXiv.\\n5. Yao, M., Yin, Q., Yang, J., Yu, T., Shen, S., Zhang, J., Liang, B., & Huang, K. (2021). The Partially Observable Asynchronous Multi-Agent Cooperation Challenge. arXiv.\"), StopMessage(source='Report_Agent', content='### Literature Review on No-Code Tools for Building Multi-Agent AI Systems\\n\\nThe emergence of no-code tools signals a significant shift in the development landscape of multi-agent AI systems, enabling users with minimal programming knowledge to design and implement complex AI applications. This literature review synthesizes current research and practical insights on no-code tools crafted for establishing multi-agent AI systems, emphasizing their features, capabilities, and applications.\\n\\n#### 1. AutoGen Studio\\n**AutoGen Studio** serves as a pioneering no-code platform tailored for the design and debugging of multi-agent systems. Dibia et al. (2024) highlight that this tool utilizes a web-based interface that enables users to construct workflows through intuitive drag-and-drop functionalities. The flexibility offered by a Python API, along with a JSON-based framework for representing agents, streamlines the prototyping and evaluation processes. Such features foster collaboration among various generative AI models and enhance usability, ultimately addressing the diverse needs of non-technical users in constructing multi-agent environments (Dibia et al., 2024). [Read the paper here](http://arxiv.org/pdf/2408.15247v1).\\n\\n#### 2. Levels of AI Agents\\nHuang (2024) introduces a conceptual framework categorizing AI agents by their capabilities. This classification ranges from simple rule-based systems to advanced large language models, underscoring the varying complexities in multi-agent interactions. Understanding these levels aids in informing the design of no-code tools to support effective collaboration among agents of differing capabilities. By integrating awareness of these agent levels, developers can enhance how no-code platforms facilitate interactions within multi-agent systems (Huang, 2024). [Read the paper here](http://arxiv.org/pdf/2405.06643v1).\\n\\n#### 3. HAICOSYSTEM\\nThe **HAICOSYSTEM** framework examines the safety considerations inherent in human-AI interactions, especially concerning multi-agent contexts. Zhou et al. (2024) propose a modular sandbox environment that simulates various operational scenarios, allowing practitioners to assess and ensure safety while interacting with agents. This research emphasizes the necessity of incorporating safety evaluation features into no-code platforms for multi-agent systems, ensuring that these tools not only enhance usability but also promote reliable and secure interactions (Zhou et al., 2024). [Read the paper here](http://arxiv.org/pdf/2409.16427v2).\\n\\n#### 4. Collaboration Protocols in AI Agents\\nThe investigation by Balachandar et al. (2019) into collaborative protocols among AI agents reveals fundamental strategies that can enhance cooperative behavior in multi-agent systems. Their insights are invaluable for informing the design of no-code platforms, highlighting the importance of effective communication and cooperation among agents. By embedding these collaborative features into no-code tools, developers can facilitate more seamless integration and interaction among agents, which is essential for complex multi-agent tasks (Balachandar et al., 2019). [Read the paper here](http://arxiv.org/pdf/1907.00327v1).\\n\\n#### 5. Asynchronous Multi-Agent Cooperation\\nYao et al. (2021) present the **Partially Observable Asynchronous Multi-Agent Cooperation (POAC)** challenge, which evaluates the performance of multi-agent reinforcement learning algorithms in asynchronous environments. This design paradigm shifts focus from synchronous operations, commonly found in traditional no-code tools, toward interfaces that reflect realistic interactions. The implications of asynchronous cooperation underscore critical design considerations for developing future no-code tools, emphasizing the need for flexibility and adaptability in systems that work under real-world constraints (Yao et al., 2021). [Read the paper here](http://arxiv.org/pdf/2112.03809v1).\\n\\n### Conclusion\\nNo-code tools for developing multi-agent AI systems are rapidly advancing, providing unprecedented opportunities for users without programming skills to engage in complex AI development. Platforms like AutoGen Studio are at the forefront of these innovations, facilitating collaboration and simplifying design processes. Concurrent research on AI agent levels, safety frameworks, collaboration protocols, and asynchronous cooperation further enhances the understanding of requirements for effective no-code tool design. As these tools proliferate, they are poised to democratize access to multi-agent system development across diverse sectors.\\n\\n### References\\n1. Dibia, V., Chen, J., Bansal, G., Syed, S., Fourney, A., Zhu, E., Wang, C., & Amershi, S. (2024). AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems. arXiv. [Available here](http://arxiv.org/pdf/2408.15247v1).\\n2. Huang, Y. (2024). Levels of AI Agents: from Rules to Large Language Models. arXiv. [Available here](http://arxiv.org/pdf/2405.06643v1).\\n3. Zhou, X., Kim, H., Brahman, F., Jiang, L., Zhu, H., Lu, X., Xu, F., Lin, B. Y., Choi, Y., Mireshghallah, N., Bras, R. L., & Sap, M. (2024). HAICOSYSTEM: An Ecosystem for Sandboxing Safety Risks in Human-AI Interactions. arXiv. [Available here](http://arxiv.org/pdf/2409.16427v2).\\n4. Balachandar, N., Dieter, J., & Ramachandran, G. S. (2019). Collaboration of AI Agents via Cooperative Multi-Agent Deep Reinforcement Learning. arXiv. [Available here](http://arxiv.org/pdf/1907.00327v1).\\n5. Yao, M., Yin, Q., Yang, J., Yu, T., Shen, S., Zhang, J., Liang, B., & Huang, K. (2021). The Partially Observable Asynchronous Multi-Agent Cooperation Challenge. arXiv. [Available here](http://arxiv.org/pdf/2112.03809v1).\\n\\nTERMINATE')])" + "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='Write a literature review on no code tools for building multi agent ai systems', type='TextMessage'), ToolCallMessage(source='Google_Search_Agent', models_usage=RequestUsage(prompt_tokens=123, completion_tokens=29), content=[FunctionCall(id='call_bNGwWFsfeTwDhtIpsI6GYISR', arguments='{\"query\":\"no code tools for building multi agent AI systems literature review\",\"num_results\":3}', name='google_search')], type='ToolCallMessage'), ToolCallResultMessage(source='Google_Search_Agent', models_usage=None, content=[FunctionExecutionResult(content='[{\\'title\\': \\'Literature Review — AutoGen\\', \\'link\\': \\'https://microsoft.github.io/autogen/dev//user-guide/agentchat-user-guide/examples/literature-review.html\\', \\'snippet\\': \\'run( task=\"Write a literature review on no code tools for building multi agent ai systems\", ) ... ### Conclusion No-code tools for building multi-agent AI systems\\\\xa0...\\', \\'body\\': \\'Literature Review — AutoGen Skip to main content Back to top Ctrl + K AutoGen 0.4 is a work in progress. Go here to find the 0.2 documentation. User Guide Packages API Reference Twitter GitHub PyPI User Guide Packages API Reference Twitter GitHub PyPI AgentChat Installation Quickstart Tutorial Models Messages Agents Teams Selector Group Chat Swarm Termination Custom Agents Managing State Examples Travel Planning Company Research Literature Review Core Quick Start Core Concepts Agent and\\'}, {\\'title\\': \\'Vertex AI Agent Builder | Google Cloud\\', \\'link\\': \\'https://cloud.google.com/products/agent-builder\\', \\'snippet\\': \\'Build and deploy enterprise ready generative AI experiences · Product highlights · Easily build no code conversational AI agents · Ground in Google search and/or\\\\xa0...\\', \\'body\\': \\'Vertex AI Agent Builder | Google Cloud Page Contents Vertex AI Agent Builder is making generative AI more reliable for the enterprise. Read the blog. Vertex AI Agent Builder Build and deploy enterprise ready generative AI experiences Create AI agents and applications using natural language or a code-first approach. Easily ground your agents or apps in enterprise data with a range of options. Vertex AI Agent Builder gathers all the surfaces and tools that developers need to build their AI agents\\'}, {\\'title\\': \\'AI tools I have found useful w/ research. What do you guys think ...\\', \\'link\\': \\'https://www.reddit.com/r/PhD/comments/14d6g09/ai_tools_i_have_found_useful_w_research_what_do/\\', \\'snippet\\': \"Jun 19, 2023 ... Need help deciding on the best ones, and to identify ones I\\'ve missed: ASSISTANTS (chatbots, multi-purpose) Chat with Open Large Language Models.\", \\'body\\': \\'Reddit - Dive into anything Skip to main content Open menu Open navigation Go to Reddit Home r/PhD A chip A close button Get app Get the Reddit app Log In Log in to Reddit Expand user menu Open settings menu Log In / Sign Up Advertise on Reddit Shop Collectible Avatars Get the Reddit app Scan this QR code to download the app now Or check it out in the app stores Go to PhD r/PhD r/PhD A subreddit dedicated to PhDs. Members Online • [deleted] ADMIN MOD AI tools I have found useful w/ research.\\'}]', call_id='call_bNGwWFsfeTwDhtIpsI6GYISR')], type='ToolCallResultMessage'), TextMessage(source='Google_Search_Agent', models_usage=None, content='Tool calls:\\ngoogle_search({\"query\":\"no code tools for building multi agent AI systems literature review\",\"num_results\":3}) = [{\\'title\\': \\'Literature Review — AutoGen\\', \\'link\\': \\'https://microsoft.github.io/autogen/dev//user-guide/agentchat-user-guide/examples/literature-review.html\\', \\'snippet\\': \\'run( task=\"Write a literature review on no code tools for building multi agent ai systems\", ) ... ### Conclusion No-code tools for building multi-agent AI systems\\\\xa0...\\', \\'body\\': \\'Literature Review — AutoGen Skip to main content Back to top Ctrl + K AutoGen 0.4 is a work in progress. Go here to find the 0.2 documentation. User Guide Packages API Reference Twitter GitHub PyPI User Guide Packages API Reference Twitter GitHub PyPI AgentChat Installation Quickstart Tutorial Models Messages Agents Teams Selector Group Chat Swarm Termination Custom Agents Managing State Examples Travel Planning Company Research Literature Review Core Quick Start Core Concepts Agent and\\'}, {\\'title\\': \\'Vertex AI Agent Builder | Google Cloud\\', \\'link\\': \\'https://cloud.google.com/products/agent-builder\\', \\'snippet\\': \\'Build and deploy enterprise ready generative AI experiences · Product highlights · Easily build no code conversational AI agents · Ground in Google search and/or\\\\xa0...\\', \\'body\\': \\'Vertex AI Agent Builder | Google Cloud Page Contents Vertex AI Agent Builder is making generative AI more reliable for the enterprise. Read the blog. Vertex AI Agent Builder Build and deploy enterprise ready generative AI experiences Create AI agents and applications using natural language or a code-first approach. Easily ground your agents or apps in enterprise data with a range of options. Vertex AI Agent Builder gathers all the surfaces and tools that developers need to build their AI agents\\'}, {\\'title\\': \\'AI tools I have found useful w/ research. What do you guys think ...\\', \\'link\\': \\'https://www.reddit.com/r/PhD/comments/14d6g09/ai_tools_i_have_found_useful_w_research_what_do/\\', \\'snippet\\': \"Jun 19, 2023 ... 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Members Online • [deleted] ADMIN MOD AI tools I have found useful w/ research.\\'}]', type='TextMessage'), ToolCallMessage(source='Arxiv_Search_Agent', models_usage=RequestUsage(prompt_tokens=719, completion_tokens=28), content=[FunctionCall(id='call_ZdmwQGTO03X23GeRn6fwDN8q', arguments='{\"query\":\"no code tools for building multi agent AI systems\",\"max_results\":5}', name='arxiv_search')], type='ToolCallMessage'), ToolCallResultMessage(source='Arxiv_Search_Agent', models_usage=None, content=[FunctionExecutionResult(content='[{\\'title\\': \\'AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems\\', \\'authors\\': [\\'Victor Dibia\\', \\'Jingya Chen\\', \\'Gagan Bansal\\', \\'Suff Syed\\', \\'Adam Fourney\\', \\'Erkang Zhu\\', \\'Chi Wang\\', \\'Saleema Amershi\\'], \\'published\\': \\'2024-08-09\\', \\'abstract\\': \\'Multi-agent systems, where multiple agents (generative AI models + tools)\\\\ncollaborate, are emerging as an effective pattern for solving long-running,\\\\ncomplex tasks in numerous domains. However, specifying their parameters (such\\\\nas models, tools, and orchestration mechanisms etc,.) and debugging them\\\\nremains challenging for most developers. To address this challenge, we present\\\\nAUTOGEN STUDIO, a no-code developer tool for rapidly prototyping, debugging,\\\\nand evaluating multi-agent workflows built upon the AUTOGEN framework. AUTOGEN\\\\nSTUDIO offers a web interface and a Python API for representing LLM-enabled\\\\nagents using a declarative (JSON-based) specification. It provides an intuitive\\\\ndrag-and-drop UI for agent workflow specification, interactive evaluation and\\\\ndebugging of workflows, and a gallery of reusable agent components. 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And, while single-agent systems have\\\\nfrequently been examined in real-world applications, we have seen comparatively\\\\nfew real-world examples of publicly available commercial tools working together\\\\nin a multi-agent system with measurable improvements. In this experiment we\\\\ntest context sharing between Crowdbotics PRD AI, a tool for generating software\\\\nrequirements using AI, and GitHub Copilot, an AI pair-programming tool. By\\\\nsharing business requirements from PRD AI, we improve the code suggestion\\\\ncapabilities of GitHub Copilot by 13.8% and developer task success rate by\\\\n24.5% -- demonstrating a real-world example of commercially-available AI\\\\nsystems working together with improved outcomes.\\', \\'pdf_url\\': \\'http://arxiv.org/pdf/2410.22129v1\\'}, {\\'title\\': \\'AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML\\', \\'authors\\': [\\'Patara Trirat\\', \\'Wonyong Jeong\\', \\'Sung Ju Hwang\\'], \\'published\\': \\'2024-10-03\\', \\'abstract\\': \"Automated machine learning (AutoML) accelerates AI development by automating\\\\ntasks in the development pipeline, such as optimal model search and\\\\nhyperparameter tuning. Existing AutoML systems often require technical\\\\nexpertise to set up complex tools, which is in general time-consuming and\\\\nrequires a large amount of human effort. Therefore, recent works have started\\\\nexploiting large language models (LLM) to lessen such burden and increase the\\\\nusability of AutoML frameworks via a natural language interface, allowing\\\\nnon-expert users to build their data-driven solutions. These methods, however,\\\\nare usually designed only for a particular process in the AI development\\\\npipeline and do not efficiently use the inherent capacity of the LLMs. This\\\\npaper proposes AutoML-Agent, a novel multi-agent framework tailored for\\\\nfull-pipeline AutoML, i.e., from data retrieval to model deployment.\\\\nAutoML-Agent takes user\\'s task descriptions, facilitates collaboration between\\\\nspecialized LLM agents, and delivers deployment-ready models. Unlike existing\\\\nwork, instead of devising a single plan, we introduce a retrieval-augmented\\\\nplanning strategy to enhance exploration to search for more optimal plans. We\\\\nalso decompose each plan into sub-tasks (e.g., data preprocessing and neural\\\\nnetwork design) each of which is solved by a specialized agent we build via\\\\nprompting executing in parallel, making the search process more efficient.\\\\nMoreover, we propose a multi-stage verification to verify executed results and\\\\nguide the code generation LLM in implementing successful solutions. Extensive\\\\nexperiments on seven downstream tasks using fourteen datasets show that\\\\nAutoML-Agent achieves a higher success rate in automating the full AutoML\\\\nprocess, yielding systems with good performance throughout the diverse domains.\", \\'pdf_url\\': \\'http://arxiv.org/pdf/2410.02958v1\\'}, {\\'title\\': \\'Enhancing Trust in LLM-Based AI Automation Agents: New Considerations and Future Challenges\\', \\'authors\\': [\\'Sivan Schwartz\\', \\'Avi Yaeli\\', \\'Segev Shlomov\\'], \\'published\\': \\'2023-08-10\\', \\'abstract\\': \\'Trust in AI agents has been extensively studied in the literature, resulting\\\\nin significant advancements in our understanding of this field. However, the\\\\nrapid advancements in Large Language Models (LLMs) and the emergence of\\\\nLLM-based AI agent frameworks pose new challenges and opportunities for further\\\\nresearch. In the field of process automation, a new generation of AI-based\\\\nagents has emerged, enabling the execution of complex tasks. At the same time,\\\\nthe process of building automation has become more accessible to business users\\\\nvia user-friendly no-code tools and training mechanisms. This paper explores\\\\nthese new challenges and opportunities, analyzes the main aspects of trust in\\\\nAI agents discussed in existing literature, and identifies specific\\\\nconsiderations and challenges relevant to this new generation of automation\\\\nagents. We also evaluate how nascent products in this category address these\\\\nconsiderations. Finally, we highlight several challenges that the research\\\\ncommunity should address in this evolving landscape.\\', \\'pdf_url\\': \\'http://arxiv.org/pdf/2308.05391v1\\'}, {\\'title\\': \\'AI2Apps: A Visual IDE for Building LLM-based AI Agent Applications\\', \\'authors\\': [\\'Xin Pang\\', \\'Zhucong Li\\', \\'Jiaxiang Chen\\', \\'Yuan Cheng\\', \\'Yinghui Xu\\', \\'Yuan Qi\\'], \\'published\\': \\'2024-04-07\\', \\'abstract\\': \\'We introduce AI2Apps, a Visual Integrated Development Environment (Visual\\\\nIDE) with full-cycle capabilities that accelerates developers to build\\\\ndeployable LLM-based AI agent Applications. This Visual IDE prioritizes both\\\\nthe Integrity of its development tools and the Visuality of its components,\\\\nensuring a smooth and efficient building experience.On one hand, AI2Apps\\\\nintegrates a comprehensive development toolkit ranging from a prototyping\\\\ncanvas and AI-assisted code editor to agent debugger, management system, and\\\\ndeployment tools all within a web-based graphical user interface. On the other\\\\nhand, AI2Apps visualizes reusable front-end and back-end code as intuitive\\\\ndrag-and-drop components. Furthermore, a plugin system named AI2Apps Extension\\\\n(AAE) is designed for Extensibility, showcasing how a new plugin with 20\\\\ncomponents enables web agent to mimic human-like browsing behavior. Our case\\\\nstudy demonstrates substantial efficiency improvements, with AI2Apps reducing\\\\ntoken consumption and API calls when debugging a specific sophisticated\\\\nmultimodal agent by approximately 90% and 80%, respectively. The AI2Apps,\\\\nincluding an online demo, open-source code, and a screencast video, is now\\\\npublicly accessible.\\', \\'pdf_url\\': \\'http://arxiv.org/pdf/2404.04902v1\\'}]', call_id='call_ZdmwQGTO03X23GeRn6fwDN8q')], type='ToolCallResultMessage'), TextMessage(source='Arxiv_Search_Agent', models_usage=None, content='Tool calls:\\narxiv_search({\"query\":\"no code tools for building multi agent AI systems\",\"max_results\":5}) = [{\\'title\\': \\'AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems\\', \\'authors\\': [\\'Victor Dibia\\', \\'Jingya Chen\\', \\'Gagan Bansal\\', \\'Suff Syed\\', \\'Adam Fourney\\', \\'Erkang Zhu\\', \\'Chi Wang\\', \\'Saleema Amershi\\'], \\'published\\': \\'2024-08-09\\', \\'abstract\\': \\'Multi-agent systems, where multiple agents (generative AI models + tools)\\\\ncollaborate, are emerging as an effective pattern for solving long-running,\\\\ncomplex tasks in numerous domains. However, specifying their parameters (such\\\\nas models, tools, and orchestration mechanisms etc,.) and debugging them\\\\nremains challenging for most developers. To address this challenge, we present\\\\nAUTOGEN STUDIO, a no-code developer tool for rapidly prototyping, debugging,\\\\nand evaluating multi-agent workflows built upon the AUTOGEN framework. AUTOGEN\\\\nSTUDIO offers a web interface and a Python API for representing LLM-enabled\\\\nagents using a declarative (JSON-based) specification. It provides an intuitive\\\\ndrag-and-drop UI for agent workflow specification, interactive evaluation and\\\\ndebugging of workflows, and a gallery of reusable agent components. We\\\\nhighlight four design principles for no-code multi-agent developer tools and\\\\ncontribute an open-source implementation at\\\\nhttps://github.com/microsoft/autogen/tree/main/samples/apps/autogen-studio\\', \\'pdf_url\\': \\'http://arxiv.org/pdf/2408.15247v1\\'}, {\\'title\\': \\'Improving Performance of Commercially Available AI Products in a Multi-Agent Configuration\\', \\'authors\\': [\\'Cory Hymel\\', \\'Sida Peng\\', \\'Kevin Xu\\', \\'Charath Ranganathan\\'], \\'published\\': \\'2024-10-29\\', \\'abstract\\': \\'In recent years, with the rapid advancement of large language models (LLMs),\\\\nmulti-agent systems have become increasingly more capable of practical\\\\napplication. At the same time, the software development industry has had a\\\\nnumber of new AI-powered tools developed that improve the software development\\\\nlifecycle (SDLC). Academically, much attention has been paid to the role of\\\\nmulti-agent systems to the SDLC. And, while single-agent systems have\\\\nfrequently been examined in real-world applications, we have seen comparatively\\\\nfew real-world examples of publicly available commercial tools working together\\\\nin a multi-agent system with measurable improvements. In this experiment we\\\\ntest context sharing between Crowdbotics PRD AI, a tool for generating software\\\\nrequirements using AI, and GitHub Copilot, an AI pair-programming tool. By\\\\nsharing business requirements from PRD AI, we improve the code suggestion\\\\ncapabilities of GitHub Copilot by 13.8% and developer task success rate by\\\\n24.5% -- demonstrating a real-world example of commercially-available AI\\\\nsystems working together with improved outcomes.\\', \\'pdf_url\\': \\'http://arxiv.org/pdf/2410.22129v1\\'}, {\\'title\\': \\'AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML\\', \\'authors\\': [\\'Patara Trirat\\', \\'Wonyong Jeong\\', \\'Sung Ju Hwang\\'], \\'published\\': \\'2024-10-03\\', \\'abstract\\': \"Automated machine learning (AutoML) accelerates AI development by automating\\\\ntasks in the development pipeline, such as optimal model search and\\\\nhyperparameter tuning. Existing AutoML systems often require technical\\\\nexpertise to set up complex tools, which is in general time-consuming and\\\\nrequires a large amount of human effort. Therefore, recent works have started\\\\nexploiting large language models (LLM) to lessen such burden and increase the\\\\nusability of AutoML frameworks via a natural language interface, allowing\\\\nnon-expert users to build their data-driven solutions. These methods, however,\\\\nare usually designed only for a particular process in the AI development\\\\npipeline and do not efficiently use the inherent capacity of the LLMs. This\\\\npaper proposes AutoML-Agent, a novel multi-agent framework tailored for\\\\nfull-pipeline AutoML, i.e., from data retrieval to model deployment.\\\\nAutoML-Agent takes user\\'s task descriptions, facilitates collaboration between\\\\nspecialized LLM agents, and delivers deployment-ready models. Unlike existing\\\\nwork, instead of devising a single plan, we introduce a retrieval-augmented\\\\nplanning strategy to enhance exploration to search for more optimal plans. We\\\\nalso decompose each plan into sub-tasks (e.g., data preprocessing and neural\\\\nnetwork design) each of which is solved by a specialized agent we build via\\\\nprompting executing in parallel, making the search process more efficient.\\\\nMoreover, we propose a multi-stage verification to verify executed results and\\\\nguide the code generation LLM in implementing successful solutions. Extensive\\\\nexperiments on seven downstream tasks using fourteen datasets show that\\\\nAutoML-Agent achieves a higher success rate in automating the full AutoML\\\\nprocess, yielding systems with good performance throughout the diverse domains.\", \\'pdf_url\\': \\'http://arxiv.org/pdf/2410.02958v1\\'}, {\\'title\\': \\'Enhancing Trust in LLM-Based AI Automation Agents: New Considerations and Future Challenges\\', \\'authors\\': [\\'Sivan Schwartz\\', \\'Avi Yaeli\\', \\'Segev Shlomov\\'], \\'published\\': \\'2023-08-10\\', \\'abstract\\': \\'Trust in AI agents has been extensively studied in the literature, resulting\\\\nin significant advancements in our understanding of this field. However, the\\\\nrapid advancements in Large Language Models (LLMs) and the emergence of\\\\nLLM-based AI agent frameworks pose new challenges and opportunities for further\\\\nresearch. In the field of process automation, a new generation of AI-based\\\\nagents has emerged, enabling the execution of complex tasks. At the same time,\\\\nthe process of building automation has become more accessible to business users\\\\nvia user-friendly no-code tools and training mechanisms. This paper explores\\\\nthese new challenges and opportunities, analyzes the main aspects of trust in\\\\nAI agents discussed in existing literature, and identifies specific\\\\nconsiderations and challenges relevant to this new generation of automation\\\\nagents. We also evaluate how nascent products in this category address these\\\\nconsiderations. Finally, we highlight several challenges that the research\\\\ncommunity should address in this evolving landscape.\\', \\'pdf_url\\': \\'http://arxiv.org/pdf/2308.05391v1\\'}, {\\'title\\': \\'AI2Apps: A Visual IDE for Building LLM-based AI Agent Applications\\', \\'authors\\': [\\'Xin Pang\\', \\'Zhucong Li\\', \\'Jiaxiang Chen\\', \\'Yuan Cheng\\', \\'Yinghui Xu\\', \\'Yuan Qi\\'], \\'published\\': \\'2024-04-07\\', \\'abstract\\': \\'We introduce AI2Apps, a Visual Integrated Development Environment (Visual\\\\nIDE) with full-cycle capabilities that accelerates developers to build\\\\ndeployable LLM-based AI agent Applications. This Visual IDE prioritizes both\\\\nthe Integrity of its development tools and the Visuality of its components,\\\\nensuring a smooth and efficient building experience.On one hand, AI2Apps\\\\nintegrates a comprehensive development toolkit ranging from a prototyping\\\\ncanvas and AI-assisted code editor to agent debugger, management system, and\\\\ndeployment tools all within a web-based graphical user interface. On the other\\\\nhand, AI2Apps visualizes reusable front-end and back-end code as intuitive\\\\ndrag-and-drop components. Furthermore, a plugin system named AI2Apps Extension\\\\n(AAE) is designed for Extensibility, showcasing how a new plugin with 20\\\\ncomponents enables web agent to mimic human-like browsing behavior. Our case\\\\nstudy demonstrates substantial efficiency improvements, with AI2Apps reducing\\\\ntoken consumption and API calls when debugging a specific sophisticated\\\\nmultimodal agent by approximately 90% and 80%, respectively. The AI2Apps,\\\\nincluding an online demo, open-source code, and a screencast video, is now\\\\npublicly accessible.\\', \\'pdf_url\\': \\'http://arxiv.org/pdf/2404.04902v1\\'}]', type='TextMessage'), TextMessage(source='Report_Agent', models_usage=RequestUsage(prompt_tokens=2381, completion_tokens=1090), content=\"## Literature Review on No-Code Tools for Building Multi-Agent AI Systems\\n\\n### Introduction\\n\\nThe emergence of multi-agent systems (MAS) has transformed various domains by enabling collaboration among multiple agents—ranging from generative AI models to orchestrated tools—to solve complex, long-term tasks. However, the traditional development of these systems often requires substantial technical expertise, making it inaccessible for non-developers. The introduction of no-code platforms aims to shift this paradigm, allowing users without formal programming knowledge to design, debug, and deploy multi-agent systems. This review synthesizes current literature concerning no-code tools developed for building multi-agent AI systems, highlighting recent advancements and emerging trends.\\n\\n### No-Code Development Tools\\n\\n#### AutoGen Studio\\n\\nOne of the prominent no-code tools is **AutoGen Studio**, developed by Dibia et al. (2024). This tool provides a web interface and a declarative specification method utilizing JSON, enabling rapid prototyping, debugging, and evaluating multi-agent workflows. The drag-and-drop capabilities streamline the design process, making complex interactions between agents more manageable. The framework operates on four primary design principles that cater specifically to no-code development, contributing to an accessible pathway for users to harness multi-agent frameworks for various applications (Dibia et al., 2024).\\n\\n#### AI2Apps Visual IDE\\n\\nAnother notable tool is **AI2Apps**, described by Pang et al. (2024). It serves as a Visual Integrated Development Environment that incorporates a comprehensive set of tools from prototyping to deployment. The platform's user-friendly interface allows for the visualization of code through drag-and-drop components, facilitating smoother integration of different agents. An extension system enhances the platform's capabilities, showcasing the potential for customization and scalability in agent application development. The reported efficiency improvements in token consumption and API calls indicate substantial benefits in user-centric design (Pang et al., 2024).\\n\\n### Performance Enhancements in Multi-Agent Configurations\\n\\nHymel et al. (2024) examined the collaborative performance of commercially available AI tools, demonstrating a measurable improvement when integrating multiple agents in a shared configuration. Their experiments showcased how cooperation between tools like Crowdbotics PRD AI and GitHub Copilot significantly improved task success rates, illustrating the practical benefits of employing no-code tools in multi-agent environments. This synergy reflects the critical need for frameworks that inherently support such integrations, especially through no-code mechanisms, to enhance user experience and productivity (Hymel et al., 2024).\\n\\n### Trust and Usability in AI Agents\\n\\nThe concept of trust in AI, particularly in LLM-based automation agents, has gained attention. Schwartz et al. (2023) addressed the challenges and considerations unique to this new generation of agents, highlighting how no-code platforms ease access and usability for non-technical users. The paper emphasizes the need for further research into the trust factors integral to effective multi-agent systems, advocating for a user-centric approach in the design and evaluation of these no-code tools (Schwartz et al., 2023).\\n\\n### Full-Pipeline AutoML with Multi-Agent Systems\\n\\nThe **AutoML-Agent** framework proposed by Trirat et al. (2024) brings another layer of innovation to the no-code landscape. This framework enhances existing automated machine learning processes by using multiple specialized agents that collaboratively manage the full AI development pipeline from data retrieval to model deployment. The novelty lies in its retrieval-augmented planning strategy, which allows for efficient task decomposition and parallel execution, optimizing the overall development experience for non-experts (Trirat et al., 2024).\\n\\n### Conclusion\\n\\nThe literature presents a growing array of no-code tools designed to democratize the development of multi-agent systems. Innovations such as AutoGen Studio, AI2Apps, and collaborative frameworks like AutoML-Agent highlight a trend towards user-centric, efficient design that encourages participation beyond technical boundaries. Future research should continue to explore aspects of trust, usability, and integration to further refine these tools and expand their applicability across various domains.\\n\\n### References\\n\\n- Dibia, V., Chen, J., Bansal, G., Syed, S., Fourney, A., Zhu, E., Wang, C., & Amershi, S. (2024). AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems. *arXiv:2408.15247*.\\n- Hymel, C., Peng, S., Xu, K., & Ranganathan, C. (2024). Improving Performance of Commercially Available AI Products in a Multi-Agent Configuration. *arXiv:2410.22129*.\\n- Pang, X., Li, Z., Chen, J., Cheng, Y., Xu, Y., & Qi, Y. (2024). AI2Apps: A Visual IDE for Building LLM-based AI Agent Applications. *arXiv:2404.04902*.\\n- Schwartz, S., Yaeli, A., & Shlomov, S. (2023). Enhancing Trust in LLM-Based AI Automation Agents: New Considerations and Future Challenges. *arXiv:2308.05391*.\\n- Trirat, P., Jeong, W., & Hwang, S. J. (2024). AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML. *arXiv:2410.02958*.\\n\\nTERMINATE\", type='TextMessage')], stop_reason=\"Text 'TERMINATE' mentioned\")" ] }, - "execution_count": 4, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "google_search_agent = ToolUseAssistantAgent(\n", - " name=\"Google_Search_Agent\",\n", - " registered_tools=[google_search_tool],\n", - " model_client=OpenAIChatCompletionClient(model=\"gpt-4o-mini\"),\n", - " description=\"An agent that can search Google for information, returns results with a snippet and body content\",\n", - " system_message=\"You are a helpful AI assistant. Solve tasks using your tools.\",\n", - ")\n", - "\n", - "arxiv_search_agent = ToolUseAssistantAgent(\n", - " name=\"Arxiv_Search_Agent\",\n", - " registered_tools=[arxiv_search_tool],\n", - " model_client=OpenAIChatCompletionClient(model=\"gpt-4o-mini\"),\n", - " description=\"An agent that can search Arxiv for papers related to a given topic, including abstracts\",\n", - " system_message=\"You are a helpful AI assistant. Solve tasks using your tools. Specifically, you can take into consideration the user's request and craft a search query that is most likely to return relevant academi papers.\",\n", - ")\n", - "\n", - "\n", - "report_agent = CodingAssistantAgent(\n", - " name=\"Report_Agent\",\n", - " model_client=OpenAIChatCompletionClient(model=\"gpt-4o-mini\"),\n", - " description=\"Generate a report based on a given topic\",\n", - " system_message=\"You are a helpful assistant. Your task is to synthesize data extracted into a high quality literature review including CORRECT references. You MUST write a final report that is formatted as a literature review with CORRECT references. Your response should end with the word 'TERMINATE'\",\n", - ")\n", - "\n", - "termination = TextMentionTermination(\"TERMINATE\")\n", - "team = RoundRobinGroupChat(\n", - " participants=[google_search_agent, arxiv_search_agent, report_agent], termination_condition=termination\n", - ")\n", - "\n", - "result = await team.run(\n", - " task=\"Write a literature review on no code tools for building multi agent ai systems\",\n", + "await Console(\n", + " team.run_stream(\n", + " task=\"Write a literature review on no code tools for building multi agent ai systems\",\n", + " )\n", ")" ] } @@ -355,7 +326,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.6" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/examples/travel-planning.ipynb b/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/examples/travel-planning.ipynb index b3d16b2fe2c2..b802127c4cd6 100644 --- a/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/examples/travel-planning.ipynb +++ b/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/examples/travel-planning.ipynb @@ -13,13 +13,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "from autogen_agentchat.agents import CodingAssistantAgent\n", + "from autogen_agentchat.agents import AssistantAgent\n", "from autogen_agentchat.conditions import TextMentionTermination\n", "from autogen_agentchat.teams import RoundRobinGroupChat\n", + "from autogen_agentchat.ui import Console\n", "from autogen_ext.models import OpenAIChatCompletionClient" ] }, @@ -34,34 +35,34 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "planner_agent = CodingAssistantAgent(\n", + "planner_agent = AssistantAgent(\n", " \"planner_agent\",\n", - " model_client=OpenAIChatCompletionClient(model=\"gpt-4\"),\n", + " model_client=OpenAIChatCompletionClient(model=\"gpt-4o\"),\n", " description=\"A helpful assistant that can plan trips.\",\n", " system_message=\"You are a helpful assistant that can suggest a travel plan for a user based on their request.\",\n", ")\n", "\n", - "local_agent = CodingAssistantAgent(\n", + "local_agent = AssistantAgent(\n", " \"local_agent\",\n", - " model_client=OpenAIChatCompletionClient(model=\"gpt-4\"),\n", + " model_client=OpenAIChatCompletionClient(model=\"gpt-4o\"),\n", " description=\"A local assistant that can suggest local activities or places to visit.\",\n", " system_message=\"You are a helpful assistant that can suggest authentic and interesting local activities or places to visit for a user and can utilize any context information provided.\",\n", ")\n", "\n", - "language_agent = CodingAssistantAgent(\n", + "language_agent = AssistantAgent(\n", " \"language_agent\",\n", - " model_client=OpenAIChatCompletionClient(model=\"gpt-4\"),\n", + " model_client=OpenAIChatCompletionClient(model=\"gpt-4o\"),\n", " description=\"A helpful assistant that can provide language tips for a given destination.\",\n", " system_message=\"You are a helpful assistant that can review travel plans, providing feedback on important/critical tips about how best to address language or communication challenges for the given destination. If the plan already includes language tips, you can mention that the plan is satisfactory, with rationale.\",\n", ")\n", "\n", - "travel_summary_agent = CodingAssistantAgent(\n", + "travel_summary_agent = AssistantAgent(\n", " \"travel_summary_agent\",\n", - " model_client=OpenAIChatCompletionClient(model=\"gpt-4\"),\n", + " model_client=OpenAIChatCompletionClient(model=\"gpt-4o\"),\n", " description=\"A helpful assistant that can summarize the travel plan.\",\n", " system_message=\"You are a helpful assistant that can take in all of the suggestions and advice from the other agents and provide a detailed tfinal travel plan. You must ensure th b at the final plan is integrated and complete. YOUR FINAL RESPONSE MUST BE THE COMPLETE PLAN. When the plan is complete and all perspectives are integrated, you can respond with TERMINATE.\",\n", ")" @@ -69,128 +70,196 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "--------------------------------------------------------------------------------\n", - "user:\n", + "---------- user ----------\n", "Plan a 3 day trip to Nepal.\n", - "--------------------------------------------------------------------------------\n", - "planner_agent:\n", - "Day 1: Arrival and Sightseeing in Kathmandu\n", - "\n", - "Start your trip upon arrival at Tribhuvan International Airport in Kathmandu. After checking into a hotel, head off for sightseeing in Kathmandu, the cultural heart of Nepal.\n", - "\n", - "First, visit Kathmandu Durbar Square, a UNESCO World Heritage Site. This square is filled with ancient architecture, temples, and has an old Royal Palace site.\n", - "\n", - "Next, make your way to Swayambhunath Stupa (Monkey Temple), perched on a hilltop with panoramic views of Kathmandu city. \n", - "\n", - "In the evening, visit Thamel, a bustling district known for its shops, food stalls, and nightlife. Enjoy local Nepali cuisine for dinner to experience the local flavors.\n", - "\n", - "Day 2: Explore Bhaktapur and Patan\n", - "\n", - "Take a scenic drive to Bhaktapur, the city of devotees. Start with Bhaktapur Durbar Square, another UNESCO World Heritage Site, and then explore the local pottery square where traditional ceramics are made.\n", - "\n", - "Then, head to Patan city, renowned for its vibrant arts and crafts tradition. Visit Patan Durbar Square, marvel at its architectural brilliance and learn about the unique mix of Buddhism and Hinduism in Nepal.\n", - "\n", - "Day 3: Visit Pokhara\n", - "\n", - "On your final day, take a domestic flight or a scenic 6-hour drive to Pokhara from Kathmandu. Pokhara is known for its stunning landscapes, charming water body - the Phewa lake, and the Himalayan range including the Annapurna massif.\n", - "\n", - "Swing by the charming Lakeside market for some shopping, savor a relaxing boat ride in Phewa lake and visit the Tal Barahi Temple located in the center of the lake. Take the opportunity to visit Davis Falls, a unique waterfall that directly goes into a narrow and deep tunnel leading into a few kilometers long cave.\n", - "\n", - "Please note that this is just a skeletal plan and actual trip might have variances based on your flight timings, weather, and local conditions.\n", - "\n", - "Don't forget to carry essentials like warm clothing (especially if you're visiting in winters), toiletries, good quality trekking shoes; and remember to check about any visa or entry requirements. ALWAYS respect local customs and traditions when visiting religious places.\n", - "\n", - "Please talk to local travel operators for transport, guide, and tickets to various tourist and heritage spots. Enjoy your trip!\n", - "--------------------------------------------------------------------------------\n", - "local_agent:\n", - "Day 1: Arrival and Sightseeing in Kathmandu\n", - "\n", - "Start your trip upon arrival at Tribhuvan International Airport in Kathmandu. After checking into a hotel, head off for sightseeing in Kathmandu, the cultural heart of Nepal.\n", - "\n", - "First, visit Kathmandu Durbar Square, a UNESCO World Heritage Site. This square is filled with ancient architecture, temples, and has an old Royal Palace site.\n", - "\n", - "Next, make your way to Swayambhunath Stupa (Monkey Temple), perched on a hilltop with panoramic views of Kathmandu city. \n", - "\n", - "In the evening, visit Thamel, a bustling district known for its shops, food stalls, and nightlife. Enjoy local Nepali cuisine for dinner to experience the local flavors.\n", - "\n", - "Day 2: Explore Bhaktapur and Patan\n", - "\n", - "Take a scenic drive to Bhaktapur, the city of devotees. Start with Bhaktapur Durbar Square, another UNESCO World Heritage Site, and then explore the local pottery square where traditional ceramics are made.\n", - "\n", - "Then, head to Patan city, renowned for its vibrant arts and crafts tradition. Visit Patan Durbar Square, marvel at its architectural brilliance and learn about the unique mix of Buddhism and Hinduism in Nepal.\n", - "\n", - "Day 3: Visit Pokhara\n", - "\n", - "On your final day, take a domestic flight or a scenic 6-hour drive to Pokhara from Kathmandu. Pokhara is known for its stunning landscapes, charming water body - the Phewa lake, and the Himalayan range including the Annapurna massif.\n", - "\n", - "Swing by the charming Lakeside market for some shopping, savor a relaxing boat ride in Phewa lake and visit the Tal Barahi Temple located in the center of the lake. Take the opportunity to visit Davis Falls, a unique waterfall that directly goes into a narrow and deep tunnel leading into a few kilometers long cave.\n", - "\n", - "Please note that this is just a skeletal plan and actual trip might have variances based on your flight timings, weather, and local conditions.\n", - "\n", - "Don't forget to carry essentials like warm clothing (especially if you're visiting in winters), toiletries, good quality trekking shoes; and remember to check about any visa or entry requirements. ALWAYS respect local customs and traditions when visiting religious places.\n", - "\n", - "Please talk to local travel operators for transport, guide, and tickets to various tourist and heritage spots. Enjoy your trip!\n", - "--------------------------------------------------------------------------------\n", - "language_agent:\n", - "Looking at your plan, it seems very detailed and well-thought-out. However, you have not addressed the critical language or communication aspect of travelling in Nepal. Here are some points to add:\n", - "\n", - "1. The official language of Nepal is Nepali and is spoken by a majority of the population. English is also commonly understood and spoken in urban areas and tourist centres, but do not expect everyone to be fluent or comfortable communicating in English.\n", - "\n", - "2. Before your trip, it might be worth learning a few basic Nepali words or phrases. Things like \"hello\" or \"Namaste\" (greeting), \"dhanyabad\" (thank you), or \"kati ho?\" (how much?) can go a long way.\n", - "\n", - "3. Consider bringing a pocket-sized Nepali phrasebook or downloading a language app on your phone for translation assistance when needed.\n", - "\n", - "4. For guided tours or when hiring taxis, ensure to hire English-speaking guides or drivers, which can usually be arranged through your hotel or local tourism agencies.\n", - "\n", - "By considering these language and communication tips, you can ensure that your trip is both enjoyable and easy to navigate.\n", - "--------------------------------------------------------------------------------\n", - "travel_summary_agent:\n", - "Day 1: Arrival and Kathmandu Sightseeing\n", - "\n", - "After landing at Tribhuvan International Airport, Kathmandu, move to your hotel for a quick refresh. Commence your tour with a visit to Kathmandu Durbar Square, a UNESCO World Heritage Site. Explore the historic architectures and temples here, along with the ancient Royal Palace.\n", - "\n", - "Proceed to Swayambhunath Stupa (Monkey Temple), situated on a hilltop with stunning views of the city. \n", - "\n", - "Afterwards, stroll through the bustling district of Thamel, known for shops and food stalls, and enjoy a dinner with local Nepali flavors.\n", - "\n", - "Day 2: Tour of Bhaktapur and Patan\n", - "\n", - "Embark on a scenic drive to Bhaktapur, often called the city of devotees. Spend your morning at Bhaktapur Durbar Square, another UNESCO World Heritage Site, and take some time to explore the local pottery square.\n", - "\n", - "In the afternoon, visit Patan City, known for its rich tradition of arts and crafts. Visit Patan Durbar Square, marvel at its architectural brilliance, and explore the confluence of Buddhism and Hinduism in Nepal.\n", - "\n", - "Day 3: Discover Pokhara\n", - "\n", - "On your final day, take a domestic flight or a 6-hour drive to Pokhara, a city renowned for its beautiful landscapes and the Phewa Lake overlooked by the towering Annapurna massif.\n", - "\n", - "Visit the lakeside market for shopping, enjoy a calming boat ride on Phewa Lake, and visit the Tal Barahi Temple in the middle of the lake. Conclude your tour with a visit to Davis Falls, a unique waterfall that flows directly into a deep, narrow tunnel.\n", - "\n", - "Please note:\n", - "\n", - "- This is just an indicative plan; actual trip might vary based on your flight timings, weather, and local conditions.\n", - "- Don't forget to pack essentials such as warm clothing (especially for winters), toiletries, and sturdy trekking shoes. Prioritize checking visa or entry requirements, if any.\n", - "- Always respect local customs and traditions, specifically when visiting religious places.\n", - "- Consult with local travel operators for transportation, travel guides, and tickets to heritage spots.\n", - "\n", - "Language Tips:\n", - "\n", - "- The official language is Nepali. English is generally understood and spoken in urban areas and by people associated with tourism, but do not anticipate everyone to be fluent.\n", - "- Familiarize yourself with basic Nepali phrases like \"Namaste\" (Hello), \"Dhanyabad\" (Thank you), \"Kati Ho?\" (How much?), etc.\n", - "- Consider carrying a Nepali phrasebook or installing a language translation app.\n", - "- For guided tours or taxis, hire English-speaking guides or drivers. This can be arranged through your hotel or local tourism agencies. \n", - "\n", - "Enjoy your trip! \n", + "---------- planner_agent ----------\n", + "Nepal is a stunning destination with its rich cultural heritage, breathtaking landscapes, and friendly people. A 3-day trip to Nepal is short, so let's focus on maximizing your experience with a mix of cultural, adventure, and scenic activities. Here’s a suggested itinerary:\n", + "\n", + "### Day 1: Arrival in Kathmandu\n", + "- **Morning:**\n", + " - Arrive at Tribhuvan International Airport in Kathmandu.\n", + " - Check into your hotel and freshen up.\n", + "- **Late Morning:**\n", + " - Visit **Swayambhunath Stupa** (also known as the Monkey Temple). This ancient religious site offers a panoramic view of the Kathmandu Valley.\n", + "- **Afternoon:**\n", + " - Head to **Kathmandu Durbar Square** to explore the old royal palace and various temples. Don’t miss the Kumari Ghar, which is home to the living goddess.\n", + " - Have lunch at a nearby local restaurant and try traditional Nepali cuisine.\n", + "- **Evening:**\n", + " - Explore the vibrant streets of **Thamel**, a popular tourist district with shops, restaurants, and markets.\n", + " - Dinner at a cozy restaurant featuring Nepali or continental dishes.\n", + "\n", + "### Day 2: Day Trip to Patan and Bhaktapur\n", + "- **Morning:**\n", + " - Drive to **Patan (Lalitpur)**, only a few kilometers from Kathmandu. Explore **Patan Durbar Square** with its incredible temples and ancient palaces.\n", + "- **Late Morning:**\n", + " - Visit the **Patan Museum** for its unique collection of artifacts.\n", + " - Optional: Visit the nearby **Golden Temple (Hiranya Varna Mahavihar)**.\n", + "- **Afternoon:**\n", + " - Head to **Bhaktapur**, about an hour's drive from Patan. Visit **Bhaktapur Durbar Square**, known for its medieval art and architecture.\n", + " - Try some local **\"juju dhau\"** (king curd) – a must-taste in Bhaktapur.\n", + "- **Evening:**\n", + " - Return to Kathmandu for an evening of relaxation.\n", + " - Dinner at a restaurant with cultural performances, such as traditional Nepali dance.\n", + "\n", + "### Day 3: Nature Excursion and Departure\n", + "- **Early Morning:**\n", + " - If interested in a short trek, consider a half-day hike to **Nagarkot** for sunrise views over the Himalayas. This requires an early start (leave around 4 AM). You can also enjoy a hearty breakfast with a view.\n", + "- **Late Morning:**\n", + " - Return to Kathmandu. If trekking to Nagarkot isn’t feasible, visit the **Pashupatinath Temple**, a UNESCO World Heritage site, or the nearby **Boudhanath Stupa**.\n", + "- **Afternoon:**\n", + " - Visit the **Garden of Dreams** for some tranquility before departure. It’s a beautifully restored, serene garden.\n", + "- **Evening:**\n", + " - Depending on your flight schedule, enjoy some last-minute shopping or relishing Nepali momos (dumplings) before you head to the airport.\n", + "- **Departure:**\n", + " - Transfer to Tribhuvan International Airport for your onward journey.\n", + "\n", + "### Tips:\n", + "- Check the weather and prepare accordingly, especially if visiting during the monsoon or winter.\n", + "- Respect local customs and traditions, especially when visiting religious sites. Dress modestly and be mindful of photography rules.\n", + "- Consider adjusting this itinerary based on your arrival and departure times and personal interests.\n", + "\n", + "I hope you have an unforgettable experience in Nepal! Safe travels!\n", + "[Prompt tokens: 40, Completion tokens: 712]\n", + "---------- local_agent ----------\n", + "Nepal offers a blend of natural beauty, rich culture, and historical wonders. For a condensed yet fulfilling 3-day trip, the following itinerary focuses on providing a diverse taste of what Nepal has to offer:\n", + "\n", + "### Day 1: Explore Kathmandu\n", + "- **Morning:**\n", + " - Arrive at Tribhuvan International Airport.\n", + " - Check into your hotel and rest or freshen up.\n", + "- **Late Morning:**\n", + " - Visit **Swayambhunath Stupa** (Monkey Temple) for panoramic views and insight into Nepalese spirituality.\n", + "- **Afternoon:**\n", + " - Explore **Kathmandu Durbar Square**, where you can admire historic palaces and temples.\n", + " - Have lunch nearby and try traditional Nepali dishes like dal bhat (lentils and rice).\n", + "- **Evening:**\n", + " - Stroll through **Thamel**, a lively district filled with shops and restaurants.\n", + " - Enjoy dinner at a cultural restaurant featuring a traditional Nepali dance show.\n", + "\n", + "### Day 2: Discover Patan and Bhaktapur\n", + "- **Morning:**\n", + " - Head to **Patan** to explore **Patan Durbar Square**, known for its stunning Newar architecture.\n", + " - Visit the **Patan Museum** to learn about Nepalese history and art.\n", + " - Optional: Visit the **Golden Temple (Hiranya Varna Mahavihar)**.\n", + "- **Afternoon:**\n", + " - Travel to **Bhaktapur**, a medieval town famous for its well-preserved architecture.\n", + " - Visit **Bhaktapur Durbar Square** and enjoy the artistic temples and palaces.\n", + " - Savor local specialties like **\"juju dhau\"** (king curd).\n", + "- **Evening:**\n", + " - Return to Kathmandu for dinner and relaxation.\n", + "\n", + "### Day 3: Nature and Spirituality\n", + "- **Early Morning:**\n", + " - Depart for **Nagarkot** to catch the sunrise over the Himalayas, offering breathtaking views of peaks including Mount Everest. If possible, enjoy breakfast with the scenic backdrop.\n", + "- **Late Morning:**\n", + " - Return to Kathmandu. Alternatively, visit the **Pashupatinath Temple**, one of Nepal's holiest Hindu sites.\n", + "- **Afternoon:**\n", + " - See the majestic **Boudhanath Stupa**, one of the largest stupas in the world.\n", + " - Relax in the **Garden of Dreams**, a peaceful oasis in the city.\n", + "- **Evening:**\n", + " - Enjoy a final meal featuring local favorites such as momos (dumplings) before heading to the airport.\n", + "- **Departure:**\n", + " - Transfer to Tribhuvan International Airport for your departure.\n", + "\n", + "### Tips:\n", + "- Dress modestly and be respectful when visiting religious sites.\n", + "- Adjust the itinerary based on your interests and flight times.\n", + "- Consider the weather; pack appropriately for the season.\n", + "\n", + "This itinerary offers a snapshot of Nepal's diverse attractions, blending cultural immersion with natural beauty. Wishing you a memorable trip! Safe travels!\n", + "[Prompt tokens: 768, Completion tokens: 604]\n", + "---------- language_agent ----------\n", + "Your 3-day trip to Nepal is well-planned, giving you a taste of the country's vibrant culture, rich history, and breathtaking landscapes. Here are some important language and communication tips that will enhance your experience:\n", + "\n", + "1. **Basic Nepali Phrases**: While English is widely spoken in tourist areas, learning a few basic Nepali phrases can be helpful and appreciated by locals. Here are some to consider:\n", + " - Namaste (नमस्ते) – Hello\n", + " - Dhanyabad (धन्यवाद) – Thank you\n", + " - Mitho cha (मिठो छ) – It's delicious\n", + " - Kripya (कृपया) – Please\n", + " - Maaph garnus (माफ गर्नुहोस्) – Sorry/Excuse me\n", + "\n", + "2. **Gesture Understanding**: In Nepal, the slight tilting head nod means \"yes,\" and shaking your head left to right can mean \"no.\" This might be different from some Western countries where nodding generally signifies agreement.\n", + "\n", + "3. **Respect and Etiquette**: When visiting religious sites, remove shoes and hats before entering. It's respectful to use your right hand when giving or receiving something, as the left hand is considered impure in Nepali culture.\n", + "\n", + "4. **Offline Translation Apps**: Consider downloading an offline translation app or phrasebook in case you find yourself in areas where English might not be as common.\n", + "\n", + "5. **Non-Verbal Communication**: A smile goes a long way in Nepal. If you encounter a language barrier, hand gestures and a friendly demeanor can be very effective.\n", + "\n", + "With these tips in mind, your itinerary seems well-rounded, giving you a rich experience in Nepal. Enjoy your trip and the diverse experiences Nepal has to offer!\n", + "[Prompt tokens: 1403, Completion tokens: 353]\n", + "---------- travel_summary_agent ----------\n", + "Here's your comprehensive and integrated 3-day travel plan for an unforgettable trip to Nepal. This itinerary focuses on delivering a taste of Nepal's culture, history, nature, and hospitality, while incorporating practical language and cultural tips to enhance your experience.\n", + "\n", + "### Day 1: Arrival and Cultural Exploration in Kathmandu\n", + "- **Morning:**\n", + " - Arrive at Tribhuvan International Airport in Kathmandu. Begin your adventure by checking into your hotel to rest and freshen up.\n", + "- **Late Morning:**\n", + " - Explore **Swayambhunath Stupa** (Monkey Temple), a symbolic and spiritual site offering magnificent panoramic views of the Kathmandu Valley. Learn basic Nepali phrases like \"Namaste\" to greet locals warmly.\n", + "- **Afternoon:**\n", + " - Visit the historic **Kathmandu Durbar Square** to admire the old royal palace and the surrounding temples, including the Kumari Ghar, home to the living goddess.\n", + " - Have lunch at a nearby restaurant and try dishes like dal bhat to get a flavor of traditional Nepali cuisine.\n", + "- **Evening:**\n", + " - Stroll through the vibrant streets of **Thamel**, a hub for tourists with many shops and eateries. Use simple gestures and smiles as you interact with local shopkeepers.\n", + " - Enjoy dinner at a restaurant with cultural performances, including traditional Nepali dance. Practice \"Dhanyabad\" to show appreciation.\n", + "\n", + "### Day 2: Discovering Heritage in Patan and Bhaktapur\n", + "- **Morning:**\n", + " - Travel to **Patan** to explore the beautiful **Patan Durbar Square** and the **Patan Museum**, marveling at its rich Newar architecture and extensive collection of artifacts.\n", + " - Optionally, visit the nearby **Golden Temple (Hiranya Varna Mahavihar)**.\n", + "- **Afternoon:**\n", + " - Head to the ancient city of **Bhaktapur**, around an hour's drive from Patan. Visit **Bhaktapur Durbar Square**, known for its well-preserved pagodas and temples.\n", + " - Relish the local specialty, **\"juju dhau\"** (king curd), an unmissable treat in Bhaktapur.\n", + " - Use polite phrases like \"Kripya\" (please) and \"Maaph garnus\" (excuse me) during interactions.\n", + "- **Evening:**\n", + " - Return to Kathmandu for dinner and unwind. Embrace the gentle head nod culture when communicating to show understanding and respect.\n", + "\n", + "### Day 3: Embracing Nature and Spirituality\n", + "- **Early Morning:**\n", + " - Venture to **Nagarkot** early to catch the breathtaking sunrise over the Himalayas. Savor a hearty breakfast amidst the stunning backdrop of peaks, including Mt. Everest, if the weather allows.\n", + "- **Late Morning:**\n", + " - Return to Kathmandu. If not visiting Nagarkot, consider the sacred **Pashupatinath Temple** or the magnificent **Boudhanath Stupa**.\n", + "- **Afternoon:**\n", + " - Relax in the **Garden of Dreams**, a restored historic garden offering serenity and beauty in Kathmandu.\n", + "- **Evening:**\n", + " - Enjoy a final dinner with favorites like momos (dumplings), savoring the flavors of Nepali cuisine one last time. Practice saying \"Mitho cha\" to compliment your meal.\n", + "- **Departure:**\n", + " - Head to Tribhuvan International Airport for your flight, leaving Nepal with cherished memories and perhaps new friendships along the way.\n", + "\n", + "### Tips:\n", + "- Respect local customs by dressing modestly, especially when visiting religious sites.\n", + "- Stay prepared for the weather by dressing accordingly for the season.\n", + "- Consider using offline translation apps if needed in areas with less English proficiency.\n", + "- Make adjustments based on your interests and flight schedule to personalize your adventure.\n", + "\n", + "Enjoy a journey filled with cultural insights, natural wonders, and meaningful connections in Nepal! Safe travels!\n", "\n", "TERMINATE\n", - "TeamRunResult(messages=[TextMessage(source='user', content='Plan a 3 day trip to Nepal.'), TextMessage(source='planner_agent', content=\"Day 1: Arrival and Sightseeing in Kathmandu\\n\\nStart your trip upon arrival at Tribhuvan International Airport in Kathmandu. After checking into a hotel, head off for sightseeing in Kathmandu, the cultural heart of Nepal.\\n\\nFirst, visit Kathmandu Durbar Square, a UNESCO World Heritage Site. This square is filled with ancient architecture, temples, and has an old Royal Palace site.\\n\\nNext, make your way to Swayambhunath Stupa (Monkey Temple), perched on a hilltop with panoramic views of Kathmandu city. \\n\\nIn the evening, visit Thamel, a bustling district known for its shops, food stalls, and nightlife. Enjoy local Nepali cuisine for dinner to experience the local flavors.\\n\\nDay 2: Explore Bhaktapur and Patan\\n\\nTake a scenic drive to Bhaktapur, the city of devotees. Start with Bhaktapur Durbar Square, another UNESCO World Heritage Site, and then explore the local pottery square where traditional ceramics are made.\\n\\nThen, head to Patan city, renowned for its vibrant arts and crafts tradition. Visit Patan Durbar Square, marvel at its architectural brilliance and learn about the unique mix of Buddhism and Hinduism in Nepal.\\n\\nDay 3: Visit Pokhara\\n\\nOn your final day, take a domestic flight or a scenic 6-hour drive to Pokhara from Kathmandu. Pokhara is known for its stunning landscapes, charming water body - the Phewa lake, and the Himalayan range including the Annapurna massif.\\n\\nSwing by the charming Lakeside market for some shopping, savor a relaxing boat ride in Phewa lake and visit the Tal Barahi Temple located in the center of the lake. Take the opportunity to visit Davis Falls, a unique waterfall that directly goes into a narrow and deep tunnel leading into a few kilometers long cave.\\n\\nPlease note that this is just a skeletal plan and actual trip might have variances based on your flight timings, weather, and local conditions.\\n\\nDon't forget to carry essentials like warm clothing (especially if you're visiting in winters), toiletries, good quality trekking shoes; and remember to check about any visa or entry requirements. ALWAYS respect local customs and traditions when visiting religious places.\\n\\nPlease talk to local travel operators for transport, guide, and tickets to various tourist and heritage spots. Enjoy your trip!\"), TextMessage(source='local_agent', content=\"Day 1: Arrival and Sightseeing in Kathmandu\\n\\nStart your trip upon arrival at Tribhuvan International Airport in Kathmandu. After checking into a hotel, head off for sightseeing in Kathmandu, the cultural heart of Nepal.\\n\\nFirst, visit Kathmandu Durbar Square, a UNESCO World Heritage Site. This square is filled with ancient architecture, temples, and has an old Royal Palace site.\\n\\nNext, make your way to Swayambhunath Stupa (Monkey Temple), perched on a hilltop with panoramic views of Kathmandu city. \\n\\nIn the evening, visit Thamel, a bustling district known for its shops, food stalls, and nightlife. Enjoy local Nepali cuisine for dinner to experience the local flavors.\\n\\nDay 2: Explore Bhaktapur and Patan\\n\\nTake a scenic drive to Bhaktapur, the city of devotees. Start with Bhaktapur Durbar Square, another UNESCO World Heritage Site, and then explore the local pottery square where traditional ceramics are made.\\n\\nThen, head to Patan city, renowned for its vibrant arts and crafts tradition. Visit Patan Durbar Square, marvel at its architectural brilliance and learn about the unique mix of Buddhism and Hinduism in Nepal.\\n\\nDay 3: Visit Pokhara\\n\\nOn your final day, take a domestic flight or a scenic 6-hour drive to Pokhara from Kathmandu. Pokhara is known for its stunning landscapes, charming water body - the Phewa lake, and the Himalayan range including the Annapurna massif.\\n\\nSwing by the charming Lakeside market for some shopping, savor a relaxing boat ride in Phewa lake and visit the Tal Barahi Temple located in the center of the lake. Take the opportunity to visit Davis Falls, a unique waterfall that directly goes into a narrow and deep tunnel leading into a few kilometers long cave.\\n\\nPlease note that this is just a skeletal plan and actual trip might have variances based on your flight timings, weather, and local conditions.\\n\\nDon't forget to carry essentials like warm clothing (especially if you're visiting in winters), toiletries, good quality trekking shoes; and remember to check about any visa or entry requirements. ALWAYS respect local customs and traditions when visiting religious places.\\n\\nPlease talk to local travel operators for transport, guide, and tickets to various tourist and heritage spots. Enjoy your trip!\"), TextMessage(source='language_agent', content='Looking at your plan, it seems very detailed and well-thought-out. However, you have not addressed the critical language or communication aspect of travelling in Nepal. Here are some points to add:\\n\\n1. The official language of Nepal is Nepali and is spoken by a majority of the population. English is also commonly understood and spoken in urban areas and tourist centres, but do not expect everyone to be fluent or comfortable communicating in English.\\n\\n2. Before your trip, it might be worth learning a few basic Nepali words or phrases. Things like \"hello\" or \"Namaste\" (greeting), \"dhanyabad\" (thank you), or \"kati ho?\" (how much?) can go a long way.\\n\\n3. Consider bringing a pocket-sized Nepali phrasebook or downloading a language app on your phone for translation assistance when needed.\\n\\n4. For guided tours or when hiring taxis, ensure to hire English-speaking guides or drivers, which can usually be arranged through your hotel or local tourism agencies.\\n\\nBy considering these language and communication tips, you can ensure that your trip is both enjoyable and easy to navigate.'), StopMessage(source='travel_summary_agent', content='Day 1: Arrival and Kathmandu Sightseeing\\n\\nAfter landing at Tribhuvan International Airport, Kathmandu, move to your hotel for a quick refresh. Commence your tour with a visit to Kathmandu Durbar Square, a UNESCO World Heritage Site. Explore the historic architectures and temples here, along with the ancient Royal Palace.\\n\\nProceed to Swayambhunath Stupa (Monkey Temple), situated on a hilltop with stunning views of the city. \\n\\nAfterwards, stroll through the bustling district of Thamel, known for shops and food stalls, and enjoy a dinner with local Nepali flavors.\\n\\nDay 2: Tour of Bhaktapur and Patan\\n\\nEmbark on a scenic drive to Bhaktapur, often called the city of devotees. Spend your morning at Bhaktapur Durbar Square, another UNESCO World Heritage Site, and take some time to explore the local pottery square.\\n\\nIn the afternoon, visit Patan City, known for its rich tradition of arts and crafts. Visit Patan Durbar Square, marvel at its architectural brilliance, and explore the confluence of Buddhism and Hinduism in Nepal.\\n\\nDay 3: Discover Pokhara\\n\\nOn your final day, take a domestic flight or a 6-hour drive to Pokhara, a city renowned for its beautiful landscapes and the Phewa Lake overlooked by the towering Annapurna massif.\\n\\nVisit the lakeside market for shopping, enjoy a calming boat ride on Phewa Lake, and visit the Tal Barahi Temple in the middle of the lake. Conclude your tour with a visit to Davis Falls, a unique waterfall that flows directly into a deep, narrow tunnel.\\n\\nPlease note:\\n\\n- This is just an indicative plan; actual trip might vary based on your flight timings, weather, and local conditions.\\n- Don\\'t forget to pack essentials such as warm clothing (especially for winters), toiletries, and sturdy trekking shoes. Prioritize checking visa or entry requirements, if any.\\n- Always respect local customs and traditions, specifically when visiting religious places.\\n- Consult with local travel operators for transportation, travel guides, and tickets to heritage spots.\\n\\nLanguage Tips:\\n\\n- The official language is Nepali. English is generally understood and spoken in urban areas and by people associated with tourism, but do not anticipate everyone to be fluent.\\n- Familiarize yourself with basic Nepali phrases like \"Namaste\" (Hello), \"Dhanyabad\" (Thank you), \"Kati Ho?\" (How much?), etc.\\n- Consider carrying a Nepali phrasebook or installing a language translation app.\\n- For guided tours or taxis, hire English-speaking guides or drivers. This can be arranged through your hotel or local tourism agencies. \\n\\nEnjoy your trip! \\n\\nTERMINATE')])\n" + "[Prompt tokens: 1780, Completion tokens: 791]\n", + "---------- Summary ----------\n", + "Number of messages: 5\n", + "Finish reason: Text 'TERMINATE' mentioned\n", + "Total prompt tokens: 3991\n", + "Total completion tokens: 2460\n", + "Duration: 28.00 seconds\n" ] + }, + { + "data": { + "text/plain": [ + "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='Plan a 3 day trip to Nepal.', type='TextMessage'), TextMessage(source='planner_agent', models_usage=RequestUsage(prompt_tokens=40, completion_tokens=712), content='Nepal is a stunning destination with its rich cultural heritage, breathtaking landscapes, and friendly people. A 3-day trip to Nepal is short, so let\\'s focus on maximizing your experience with a mix of cultural, adventure, and scenic activities. Here’s a suggested itinerary:\\n\\n### Day 1: Arrival in Kathmandu\\n- **Morning:**\\n - Arrive at Tribhuvan International Airport in Kathmandu.\\n - Check into your hotel and freshen up.\\n- **Late Morning:**\\n - Visit **Swayambhunath Stupa** (also known as the Monkey Temple). This ancient religious site offers a panoramic view of the Kathmandu Valley.\\n- **Afternoon:**\\n - Head to **Kathmandu Durbar Square** to explore the old royal palace and various temples. Don’t miss the Kumari Ghar, which is home to the living goddess.\\n - Have lunch at a nearby local restaurant and try traditional Nepali cuisine.\\n- **Evening:**\\n - Explore the vibrant streets of **Thamel**, a popular tourist district with shops, restaurants, and markets.\\n - Dinner at a cozy restaurant featuring Nepali or continental dishes.\\n\\n### Day 2: Day Trip to Patan and Bhaktapur\\n- **Morning:**\\n - Drive to **Patan (Lalitpur)**, only a few kilometers from Kathmandu. Explore **Patan Durbar Square** with its incredible temples and ancient palaces.\\n- **Late Morning:**\\n - Visit the **Patan Museum** for its unique collection of artifacts.\\n - Optional: Visit the nearby **Golden Temple (Hiranya Varna Mahavihar)**.\\n- **Afternoon:**\\n - Head to **Bhaktapur**, about an hour\\'s drive from Patan. Visit **Bhaktapur Durbar Square**, known for its medieval art and architecture.\\n - Try some local **\"juju dhau\"** (king curd) – a must-taste in Bhaktapur.\\n- **Evening:**\\n - Return to Kathmandu for an evening of relaxation.\\n - Dinner at a restaurant with cultural performances, such as traditional Nepali dance.\\n\\n### Day 3: Nature Excursion and Departure\\n- **Early Morning:**\\n - If interested in a short trek, consider a half-day hike to **Nagarkot** for sunrise views over the Himalayas. This requires an early start (leave around 4 AM). You can also enjoy a hearty breakfast with a view.\\n- **Late Morning:**\\n - Return to Kathmandu. If trekking to Nagarkot isn’t feasible, visit the **Pashupatinath Temple**, a UNESCO World Heritage site, or the nearby **Boudhanath Stupa**.\\n- **Afternoon:**\\n - Visit the **Garden of Dreams** for some tranquility before departure. It’s a beautifully restored, serene garden.\\n- **Evening:**\\n - Depending on your flight schedule, enjoy some last-minute shopping or relishing Nepali momos (dumplings) before you head to the airport.\\n- **Departure:**\\n - Transfer to Tribhuvan International Airport for your onward journey.\\n\\n### Tips:\\n- Check the weather and prepare accordingly, especially if visiting during the monsoon or winter.\\n- Respect local customs and traditions, especially when visiting religious sites. Dress modestly and be mindful of photography rules.\\n- Consider adjusting this itinerary based on your arrival and departure times and personal interests.\\n\\nI hope you have an unforgettable experience in Nepal! Safe travels!', type='TextMessage'), TextMessage(source='local_agent', models_usage=RequestUsage(prompt_tokens=768, completion_tokens=604), content='Nepal offers a blend of natural beauty, rich culture, and historical wonders. For a condensed yet fulfilling 3-day trip, the following itinerary focuses on providing a diverse taste of what Nepal has to offer:\\n\\n### Day 1: Explore Kathmandu\\n- **Morning:**\\n - Arrive at Tribhuvan International Airport.\\n - Check into your hotel and rest or freshen up.\\n- **Late Morning:**\\n - Visit **Swayambhunath Stupa** (Monkey Temple) for panoramic views and insight into Nepalese spirituality.\\n- **Afternoon:**\\n - Explore **Kathmandu Durbar Square**, where you can admire historic palaces and temples.\\n - Have lunch nearby and try traditional Nepali dishes like dal bhat (lentils and rice).\\n- **Evening:**\\n - Stroll through **Thamel**, a lively district filled with shops and restaurants.\\n - Enjoy dinner at a cultural restaurant featuring a traditional Nepali dance show.\\n\\n### Day 2: Discover Patan and Bhaktapur\\n- **Morning:**\\n - Head to **Patan** to explore **Patan Durbar Square**, known for its stunning Newar architecture.\\n - Visit the **Patan Museum** to learn about Nepalese history and art.\\n - Optional: Visit the **Golden Temple (Hiranya Varna Mahavihar)**.\\n- **Afternoon:**\\n - Travel to **Bhaktapur**, a medieval town famous for its well-preserved architecture.\\n - Visit **Bhaktapur Durbar Square** and enjoy the artistic temples and palaces.\\n - Savor local specialties like **\"juju dhau\"** (king curd).\\n- **Evening:**\\n - Return to Kathmandu for dinner and relaxation.\\n\\n### Day 3: Nature and Spirituality\\n- **Early Morning:**\\n - Depart for **Nagarkot** to catch the sunrise over the Himalayas, offering breathtaking views of peaks including Mount Everest. If possible, enjoy breakfast with the scenic backdrop.\\n- **Late Morning:**\\n - Return to Kathmandu. Alternatively, visit the **Pashupatinath Temple**, one of Nepal\\'s holiest Hindu sites.\\n- **Afternoon:**\\n - See the majestic **Boudhanath Stupa**, one of the largest stupas in the world.\\n - Relax in the **Garden of Dreams**, a peaceful oasis in the city.\\n- **Evening:**\\n - Enjoy a final meal featuring local favorites such as momos (dumplings) before heading to the airport.\\n- **Departure:**\\n - Transfer to Tribhuvan International Airport for your departure.\\n\\n### Tips:\\n- Dress modestly and be respectful when visiting religious sites.\\n- Adjust the itinerary based on your interests and flight times.\\n- Consider the weather; pack appropriately for the season.\\n\\nThis itinerary offers a snapshot of Nepal\\'s diverse attractions, blending cultural immersion with natural beauty. Wishing you a memorable trip! Safe travels!', type='TextMessage'), TextMessage(source='language_agent', models_usage=RequestUsage(prompt_tokens=1403, completion_tokens=353), content='Your 3-day trip to Nepal is well-planned, giving you a taste of the country\\'s vibrant culture, rich history, and breathtaking landscapes. Here are some important language and communication tips that will enhance your experience:\\n\\n1. **Basic Nepali Phrases**: While English is widely spoken in tourist areas, learning a few basic Nepali phrases can be helpful and appreciated by locals. Here are some to consider:\\n - Namaste (नमस्ते) – Hello\\n - Dhanyabad (धन्यवाद) – Thank you\\n - Mitho cha (मिठो छ) – It\\'s delicious\\n - Kripya (कृपया) – Please\\n - Maaph garnus (माफ गर्नुहोस्) – Sorry/Excuse me\\n\\n2. **Gesture Understanding**: In Nepal, the slight tilting head nod means \"yes,\" and shaking your head left to right can mean \"no.\" This might be different from some Western countries where nodding generally signifies agreement.\\n\\n3. **Respect and Etiquette**: When visiting religious sites, remove shoes and hats before entering. It\\'s respectful to use your right hand when giving or receiving something, as the left hand is considered impure in Nepali culture.\\n\\n4. **Offline Translation Apps**: Consider downloading an offline translation app or phrasebook in case you find yourself in areas where English might not be as common.\\n\\n5. **Non-Verbal Communication**: A smile goes a long way in Nepal. If you encounter a language barrier, hand gestures and a friendly demeanor can be very effective.\\n\\nWith these tips in mind, your itinerary seems well-rounded, giving you a rich experience in Nepal. Enjoy your trip and the diverse experiences Nepal has to offer!', type='TextMessage'), TextMessage(source='travel_summary_agent', models_usage=RequestUsage(prompt_tokens=1780, completion_tokens=791), content='Here\\'s your comprehensive and integrated 3-day travel plan for an unforgettable trip to Nepal. This itinerary focuses on delivering a taste of Nepal\\'s culture, history, nature, and hospitality, while incorporating practical language and cultural tips to enhance your experience.\\n\\n### Day 1: Arrival and Cultural Exploration in Kathmandu\\n- **Morning:**\\n - Arrive at Tribhuvan International Airport in Kathmandu. Begin your adventure by checking into your hotel to rest and freshen up.\\n- **Late Morning:**\\n - Explore **Swayambhunath Stupa** (Monkey Temple), a symbolic and spiritual site offering magnificent panoramic views of the Kathmandu Valley. Learn basic Nepali phrases like \"Namaste\" to greet locals warmly.\\n- **Afternoon:**\\n - Visit the historic **Kathmandu Durbar Square** to admire the old royal palace and the surrounding temples, including the Kumari Ghar, home to the living goddess.\\n - Have lunch at a nearby restaurant and try dishes like dal bhat to get a flavor of traditional Nepali cuisine.\\n- **Evening:**\\n - Stroll through the vibrant streets of **Thamel**, a hub for tourists with many shops and eateries. Use simple gestures and smiles as you interact with local shopkeepers.\\n - Enjoy dinner at a restaurant with cultural performances, including traditional Nepali dance. Practice \"Dhanyabad\" to show appreciation.\\n\\n### Day 2: Discovering Heritage in Patan and Bhaktapur\\n- **Morning:**\\n - Travel to **Patan** to explore the beautiful **Patan Durbar Square** and the **Patan Museum**, marveling at its rich Newar architecture and extensive collection of artifacts.\\n - Optionally, visit the nearby **Golden Temple (Hiranya Varna Mahavihar)**.\\n- **Afternoon:**\\n - Head to the ancient city of **Bhaktapur**, around an hour\\'s drive from Patan. Visit **Bhaktapur Durbar Square**, known for its well-preserved pagodas and temples.\\n - Relish the local specialty, **\"juju dhau\"** (king curd), an unmissable treat in Bhaktapur.\\n - Use polite phrases like \"Kripya\" (please) and \"Maaph garnus\" (excuse me) during interactions.\\n- **Evening:**\\n - Return to Kathmandu for dinner and unwind. Embrace the gentle head nod culture when communicating to show understanding and respect.\\n\\n### Day 3: Embracing Nature and Spirituality\\n- **Early Morning:**\\n - Venture to **Nagarkot** early to catch the breathtaking sunrise over the Himalayas. Savor a hearty breakfast amidst the stunning backdrop of peaks, including Mt. Everest, if the weather allows.\\n- **Late Morning:**\\n - Return to Kathmandu. If not visiting Nagarkot, consider the sacred **Pashupatinath Temple** or the magnificent **Boudhanath Stupa**.\\n- **Afternoon:**\\n - Relax in the **Garden of Dreams**, a restored historic garden offering serenity and beauty in Kathmandu.\\n- **Evening:**\\n - Enjoy a final dinner with favorites like momos (dumplings), savoring the flavors of Nepali cuisine one last time. Practice saying \"Mitho cha\" to compliment your meal.\\n- **Departure:**\\n - Head to Tribhuvan International Airport for your flight, leaving Nepal with cherished memories and perhaps new friendships along the way.\\n\\n### Tips:\\n- Respect local customs by dressing modestly, especially when visiting religious sites.\\n- Stay prepared for the weather by dressing accordingly for the season.\\n- Consider using offline translation apps if needed in areas with less English proficiency.\\n- Make adjustments based on your interests and flight schedule to personalize your adventure.\\n\\nEnjoy a journey filled with cultural insights, natural wonders, and meaningful connections in Nepal! Safe travels!\\n\\nTERMINATE', type='TextMessage')], stop_reason=\"Text 'TERMINATE' mentioned\")" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -198,8 +267,7 @@ "group_chat = RoundRobinGroupChat(\n", " [planner_agent, local_agent, language_agent, travel_summary_agent], termination_condition=termination\n", ")\n", - "result = await group_chat.run(task=\"Plan a 3 day trip to Nepal.\")\n", - "print(result)" + "await Console(group_chat.run_stream(task=\"Plan a 3 day trip to Nepal.\"))" ] } ], @@ -219,7 +287,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.6" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/tutorial/selector-group-chat.ipynb b/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/tutorial/selector-group-chat.ipynb index 98b52b1ee69e..fb71cdc2a913 100644 --- a/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/tutorial/selector-group-chat.ipynb +++ b/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/tutorial/selector-group-chat.ipynb @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -94,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -129,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -215,7 +215,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -239,7 +239,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -249,62 +249,77 @@ "---------- user ----------\n", "Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?\n", "---------- PlanningAgent ----------\n", - "To answer your question, we need to separate this task into several subtasks:\n", + "To address this request, we will divide the task into manageable subtasks. \n", "\n", - "1. Web search agent: Find out who was the Miami Heat player with the highest points in the 2006-2007 NBA season.\n", - "2. Web search agent: Find the total rebounds for that player in the 2007-2008 NBA season.\n", - "3. Web search agent: Find the total rebounds for that player in the 2008-2009 NBA season.\n", - "4. Data analyst: Calculate the percentage change in the player's total rebounds between the 2007-2008 and 2008-2009 seasons.\n", - "\n", - "Let's start with these tasks.\n", - "[Prompt tokens: 159, Completion tokens: 130]\n", + "1. Web search agent: Identify the Miami Heat player with the highest points in the 2006-2007 season.\n", + "2. Web search agent: Gather the total rebounds for the identified player during the 2007-2008 season.\n", + "3. Web search agent: Gather the total rebounds for the identified player during the 2008-2009 season.\n", + "4. Data analyst: Calculate the percentage change in total rebounds for the identified player between the 2007-2008 and 2008-2009 seasons.\n", + "[Prompt tokens: 159, Completion tokens: 122]\n", "---------- WebSearchAgent ----------\n", - "[FunctionCall(id='call_js7ogBp0UDmHfvLo6BmWFpM1', arguments='{\"query\":\"Miami Heat player highest points 2006-2007 season\"}', name='search_web_tool')]\n", - "[Prompt tokens: 279, Completion tokens: 26]\n", + "[FunctionCall(id='call_xdYlGP2lsqDeWdSiOlwOBNiO', arguments='{\"query\":\"Miami Heat highest points player 2006-2007 season\"}', name='search_web_tool')]\n", + "[Prompt tokens: 271, Completion tokens: 26]\n", "---------- WebSearchAgent ----------\n", - "[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', call_id='call_js7ogBp0UDmHfvLo6BmWFpM1')]\n", + "[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', call_id='call_xdYlGP2lsqDeWdSiOlwOBNiO')]\n", "---------- WebSearchAgent ----------\n", - "[FunctionCall(id='call_G7ATvIq0rSjc8fqLdKQ5uWI4', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 season\"}', name='search_web_tool'), FunctionCall(id='call_Xzw9bAvgfo40EjILophG5pnl', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 season\"}', name='search_web_tool')]\n", - "[Prompt tokens: 371, Completion tokens: 70]\n", + "Tool calls:\n", + "search_web_tool({\"query\":\"Miami Heat highest points player 2006-2007 season\"}) = Here are the total points scored by Miami Heat players in the 2006-2007 season:\n", + " Udonis Haslem: 844 points\n", + " Dwayne Wade: 1397 points\n", + " James Posey: 550 points\n", + " ...\n", + " \n", + "---------- DataAnalystAgent ----------\n", + "[FunctionCall(id='call_asjxKtAVGfqrYl0jMpvwCrvV', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 season\"}', name='WebSearchAgent'), FunctionCall(id='call_8L91Kizt0KU6RNwUgvNx7S0s', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 season\"}', name='WebSearchAgent')]\n", + "[Prompt tokens: 345, Completion tokens: 68]\n", + "---------- DataAnalystAgent ----------\n", + "[FunctionExecutionResult(content=\"Error: The tool 'WebSearchAgent' is not available.\", call_id='call_asjxKtAVGfqrYl0jMpvwCrvV'), FunctionExecutionResult(content=\"Error: The tool 'WebSearchAgent' is not available.\", call_id='call_8L91Kizt0KU6RNwUgvNx7S0s')]\n", + "---------- DataAnalystAgent ----------\n", + "Tool calls:\n", + "WebSearchAgent({\"query\": \"Dwyane Wade total rebounds 2007-2008 season\"}) = Error: The tool 'WebSearchAgent' is not available.\n", + "WebSearchAgent({\"query\": \"Dwyane Wade total rebounds 2008-2009 season\"}) = Error: The tool 'WebSearchAgent' is not available.\n", "---------- WebSearchAgent ----------\n", - "[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', call_id='call_G7ATvIq0rSjc8fqLdKQ5uWI4'), FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', call_id='call_Xzw9bAvgfo40EjILophG5pnl')]\n", + "[FunctionCall(id='call_imvRJ2jhpPdovBbx8MFjlFVS', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 season\"}', name='search_web_tool'), FunctionCall(id='call_U30KVmFG1aeXPbqGJjDmJ6iJ', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 season\"}', name='search_web_tool')]\n", + "[Prompt tokens: 445, Completion tokens: 70]\n", "---------- WebSearchAgent ----------\n", - "The Miami Heat player with the highest points in the 2006-2007 season was Dwyane Wade, with 1,397 points.\n", - "\n", - "Between the 2007-2008 and 2008-2009 seasons, Dwyane Wade's total rebounds increased from 214 to 398. To calculate the percentage change:\n", - "\n", - "Percentage Change = \\(\\frac{(398 - 214)}{214} \\times 100\\)\n", - "\n", - "Now, a data analyst would calculate the actual percentage change based on these numbers.\n", - "[Prompt tokens: 506, Completion tokens: 107]\n", + "[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', call_id='call_imvRJ2jhpPdovBbx8MFjlFVS'), FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', call_id='call_U30KVmFG1aeXPbqGJjDmJ6iJ')]\n", + "---------- WebSearchAgent ----------\n", + "Tool calls:\n", + "search_web_tool({\"query\": \"Dwyane Wade total rebounds 2007-2008 season\"}) = The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.\n", + "search_web_tool({\"query\": \"Dwyane Wade total rebounds 2008-2009 season\"}) = The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.\n", "---------- DataAnalystAgent ----------\n", - "[FunctionCall(id='call_76VkQ2nnKrwtuI1dmjLQ7G5P', arguments='{\"start\":214,\"end\":398}', name='percentage_change_tool')]\n", - "[Prompt tokens: 383, Completion tokens: 20]\n", + "[FunctionCall(id='call_CtAnvcbitN0JiwBfiLVzb5Do', arguments='{\"start\":214,\"end\":398}', name='percentage_change_tool')]\n", + "[Prompt tokens: 562, Completion tokens: 20]\n", "---------- DataAnalystAgent ----------\n", - "[FunctionExecutionResult(content='85.98130841121495', call_id='call_76VkQ2nnKrwtuI1dmjLQ7G5P')]\n", + "[FunctionExecutionResult(content='85.98130841121495', call_id='call_CtAnvcbitN0JiwBfiLVzb5Do')]\n", "---------- DataAnalystAgent ----------\n", - "The Miami Heat player with the highest points in the 2006-2007 season was Dwyane Wade. His total rebounds increased by approximately 85.98% between the 2007-2008 and 2008-2009 seasons.\n", - "[Prompt tokens: 424, Completion tokens: 52]\n", + "Tool calls:\n", + "percentage_change_tool({\"start\":214,\"end\":398}) = 85.98130841121495\n", "---------- PlanningAgent ----------\n", - "The Miami Heat player with the highest points in the 2006-2007 season was Dwyane Wade, scoring 1,397 points. Between the 2007-2008 and 2008-2009 seasons, Dwyane Wade's total rebounds increased by approximately 85.98%. \n", + "Summary of Findings:\n", + "\n", + "1. Dwyane Wade was the Miami Heat player with the highest points in the 2006-2007 season, scoring a total of 1,397 points.\n", + "2. Dwyane Wade's total rebounds during the 2007-2008 season were 214.\n", + "3. Dwyane Wade's total rebounds during the 2008-2009 season were 398.\n", + "4. The percentage change in Dwyane Wade's total rebounds between the 2007-2008 and 2008-2009 seasons was approximately 85.98%.\n", "\n", "TERMINATE\n", - "[Prompt tokens: 470, Completion tokens: 66]\n", + "[Prompt tokens: 590, Completion tokens: 122]\n", "---------- Summary ----------\n", - "Number of messages: 11\n", + "Number of messages: 15\n", "Finish reason: Text 'TERMINATE' mentioned\n", - "Total prompt tokens: 2592\n", - "Total completion tokens: 471\n", - "Duration: 11.95 seconds\n" + "Total prompt tokens: 2372\n", + "Total completion tokens: 428\n", + "Duration: 9.21 seconds\n" ] }, { "data": { "text/plain": [ - "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=159, completion_tokens=130), content=\"To answer your question, we need to separate this task into several subtasks:\\n\\n1. Web search agent: Find out who was the Miami Heat player with the highest points in the 2006-2007 NBA season.\\n2. Web search agent: Find the total rebounds for that player in the 2007-2008 NBA season.\\n3. Web search agent: Find the total rebounds for that player in the 2008-2009 NBA season.\\n4. Data analyst: Calculate the percentage change in the player's total rebounds between the 2007-2008 and 2008-2009 seasons.\\n\\nLet's start with these tasks.\"), ToolCallMessage(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=279, completion_tokens=26), content=[FunctionCall(id='call_js7ogBp0UDmHfvLo6BmWFpM1', arguments='{\"query\":\"Miami Heat player highest points 2006-2007 season\"}', name='search_web_tool')]), ToolCallResultMessage(source='WebSearchAgent', models_usage=None, content=[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', call_id='call_js7ogBp0UDmHfvLo6BmWFpM1')]), ToolCallMessage(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=371, completion_tokens=70), content=[FunctionCall(id='call_G7ATvIq0rSjc8fqLdKQ5uWI4', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 season\"}', name='search_web_tool'), FunctionCall(id='call_Xzw9bAvgfo40EjILophG5pnl', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 season\"}', name='search_web_tool')]), ToolCallResultMessage(source='WebSearchAgent', models_usage=None, content=[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', call_id='call_G7ATvIq0rSjc8fqLdKQ5uWI4'), FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', call_id='call_Xzw9bAvgfo40EjILophG5pnl')]), TextMessage(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=506, completion_tokens=107), content=\"The Miami Heat player with the highest points in the 2006-2007 season was Dwyane Wade, with 1,397 points.\\n\\nBetween the 2007-2008 and 2008-2009 seasons, Dwyane Wade's total rebounds increased from 214 to 398. To calculate the percentage change:\\n\\nPercentage Change = \\\\(\\\\frac{(398 - 214)}{214} \\\\times 100\\\\)\\n\\nNow, a data analyst would calculate the actual percentage change based on these numbers.\"), ToolCallMessage(source='DataAnalystAgent', models_usage=RequestUsage(prompt_tokens=383, completion_tokens=20), content=[FunctionCall(id='call_76VkQ2nnKrwtuI1dmjLQ7G5P', arguments='{\"start\":214,\"end\":398}', name='percentage_change_tool')]), ToolCallResultMessage(source='DataAnalystAgent', models_usage=None, content=[FunctionExecutionResult(content='85.98130841121495', call_id='call_76VkQ2nnKrwtuI1dmjLQ7G5P')]), TextMessage(source='DataAnalystAgent', models_usage=RequestUsage(prompt_tokens=424, completion_tokens=52), content='The Miami Heat player with the highest points in the 2006-2007 season was Dwyane Wade. His total rebounds increased by approximately 85.98% between the 2007-2008 and 2008-2009 seasons.'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=470, completion_tokens=66), content=\"The Miami Heat player with the highest points in the 2006-2007 season was Dwyane Wade, scoring 1,397 points. Between the 2007-2008 and 2008-2009 seasons, Dwyane Wade's total rebounds increased by approximately 85.98%. \\n\\nTERMINATE\")], stop_reason=\"Text 'TERMINATE' mentioned\")" + "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?', type='TextMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=159, completion_tokens=122), content='To address this request, we will divide the task into manageable subtasks. \\n\\n1. Web search agent: Identify the Miami Heat player with the highest points in the 2006-2007 season.\\n2. Web search agent: Gather the total rebounds for the identified player during the 2007-2008 season.\\n3. Web search agent: Gather the total rebounds for the identified player during the 2008-2009 season.\\n4. Data analyst: Calculate the percentage change in total rebounds for the identified player between the 2007-2008 and 2008-2009 seasons.', type='TextMessage'), ToolCallMessage(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=271, completion_tokens=26), content=[FunctionCall(id='call_xdYlGP2lsqDeWdSiOlwOBNiO', arguments='{\"query\":\"Miami Heat highest points player 2006-2007 season\"}', name='search_web_tool')], type='ToolCallMessage'), ToolCallResultMessage(source='WebSearchAgent', models_usage=None, content=[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', call_id='call_xdYlGP2lsqDeWdSiOlwOBNiO')], type='ToolCallResultMessage'), TextMessage(source='WebSearchAgent', models_usage=None, content='Tool calls:\\nsearch_web_tool({\"query\":\"Miami Heat highest points player 2006-2007 season\"}) = Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', type='TextMessage'), ToolCallMessage(source='DataAnalystAgent', models_usage=RequestUsage(prompt_tokens=345, completion_tokens=68), content=[FunctionCall(id='call_asjxKtAVGfqrYl0jMpvwCrvV', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 season\"}', name='WebSearchAgent'), FunctionCall(id='call_8L91Kizt0KU6RNwUgvNx7S0s', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 season\"}', name='WebSearchAgent')], type='ToolCallMessage'), ToolCallResultMessage(source='DataAnalystAgent', models_usage=None, content=[FunctionExecutionResult(content=\"Error: The tool 'WebSearchAgent' is not available.\", call_id='call_asjxKtAVGfqrYl0jMpvwCrvV'), FunctionExecutionResult(content=\"Error: The tool 'WebSearchAgent' is not available.\", call_id='call_8L91Kizt0KU6RNwUgvNx7S0s')], type='ToolCallResultMessage'), TextMessage(source='DataAnalystAgent', models_usage=None, content='Tool calls:\\nWebSearchAgent({\"query\": \"Dwyane Wade total rebounds 2007-2008 season\"}) = Error: The tool \\'WebSearchAgent\\' is not available.\\nWebSearchAgent({\"query\": \"Dwyane Wade total rebounds 2008-2009 season\"}) = Error: The tool \\'WebSearchAgent\\' is not available.', type='TextMessage'), ToolCallMessage(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=445, completion_tokens=70), content=[FunctionCall(id='call_imvRJ2jhpPdovBbx8MFjlFVS', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 season\"}', name='search_web_tool'), FunctionCall(id='call_U30KVmFG1aeXPbqGJjDmJ6iJ', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 season\"}', name='search_web_tool')], type='ToolCallMessage'), ToolCallResultMessage(source='WebSearchAgent', models_usage=None, content=[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', call_id='call_imvRJ2jhpPdovBbx8MFjlFVS'), FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', call_id='call_U30KVmFG1aeXPbqGJjDmJ6iJ')], type='ToolCallResultMessage'), TextMessage(source='WebSearchAgent', models_usage=None, content='Tool calls:\\nsearch_web_tool({\"query\": \"Dwyane Wade total rebounds 2007-2008 season\"}) = The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.\\nsearch_web_tool({\"query\": \"Dwyane Wade total rebounds 2008-2009 season\"}) = The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', type='TextMessage'), ToolCallMessage(source='DataAnalystAgent', models_usage=RequestUsage(prompt_tokens=562, completion_tokens=20), content=[FunctionCall(id='call_CtAnvcbitN0JiwBfiLVzb5Do', arguments='{\"start\":214,\"end\":398}', name='percentage_change_tool')], type='ToolCallMessage'), ToolCallResultMessage(source='DataAnalystAgent', models_usage=None, content=[FunctionExecutionResult(content='85.98130841121495', call_id='call_CtAnvcbitN0JiwBfiLVzb5Do')], type='ToolCallResultMessage'), TextMessage(source='DataAnalystAgent', models_usage=None, content='Tool calls:\\npercentage_change_tool({\"start\":214,\"end\":398}) = 85.98130841121495', type='TextMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=590, completion_tokens=122), content=\"Summary of Findings:\\n\\n1. Dwyane Wade was the Miami Heat player with the highest points in the 2006-2007 season, scoring a total of 1,397 points.\\n2. Dwyane Wade's total rebounds during the 2007-2008 season were 214.\\n3. Dwyane Wade's total rebounds during the 2008-2009 season were 398.\\n4. The percentage change in Dwyane Wade's total rebounds between the 2007-2008 and 2008-2009 seasons was approximately 85.98%.\\n\\nTERMINATE\", type='TextMessage')], stop_reason=\"Text 'TERMINATE' mentioned\")" ] }, - "execution_count": 12, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -345,7 +360,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -355,65 +370,89 @@ "---------- user ----------\n", "Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?\n", "---------- PlanningAgent ----------\n", - "To solve this inquiry, let's break it down into smaller tasks again:\n", + "To address this query, we'll need to break it down into a few specific tasks:\n", "\n", - "1. Find out who was the Miami Heat player with the highest points in the 2006-2007 NBA season.\n", - "2. Find that player's total rebounds for the 2007-2008 NBA season.\n", - "3. Find that player's total rebounds for the 2008-2009 NBA season.\n", - "4. Calculate the percentage change in the player's total rebounds from the 2007-2008 to the 2008-2009 season.\n", + "1. Web search agent: Identify the Miami Heat player with the highest points in the 2006-2007 NBA season.\n", + "2. Web search agent: Find the total number of rebounds by this player in the 2007-2008 NBA season.\n", + "3. Web search agent: Find the total number of rebounds by this player in the 2008-2009 NBA season.\n", + "4. Data analyst: Calculate the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons.\n", "\n", - "Let's proceed with these tasks and find the necessary information.\n", - "[Prompt tokens: 595, Completion tokens: 115]\n", + "Let's get started with these tasks.\n", + "[Prompt tokens: 159, Completion tokens: 132]\n", "---------- WebSearchAgent ----------\n", - "The Miami Heat player with the highest points in the 2006-2007 season was Dwyane Wade, who scored a total of 1,397 points.\n", - "\n", - "In terms of his rebound statistics:\n", - "- In the 2007-2008 season, Dwyane Wade recorded 214 total rebounds.\n", - "- In the 2008-2009 season, he recorded 398 total rebounds.\n", - "\n", - "To find the percentage change in his total rebounds, a data analyst would perform the following calculation:\n", - "\n", - "\\[\n", - "\\text{Percentage Change} = \\left( \\frac{398 - 214}{214} \\right) \\times 100\n", - "\\]\n", - "\n", - "A data analyst would use the above numbers to determine the percentage change in his total rebounds between these two seasons.\n", - "[Prompt tokens: 794, Completion tokens: 154]\n", + "[FunctionCall(id='call_TSUHOBKhpHmTNoYeJzwSP5V4', arguments='{\"query\":\"Miami Heat highest points player 2006-2007 season\"}', name='search_web_tool')]\n", + "[Prompt tokens: 281, Completion tokens: 26]\n", + "---------- WebSearchAgent ----------\n", + "[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', call_id='call_TSUHOBKhpHmTNoYeJzwSP5V4')]\n", + "---------- WebSearchAgent ----------\n", + "Tool calls:\n", + "search_web_tool({\"query\":\"Miami Heat highest points player 2006-2007 season\"}) = Here are the total points scored by Miami Heat players in the 2006-2007 season:\n", + " Udonis Haslem: 844 points\n", + " Dwayne Wade: 1397 points\n", + " James Posey: 550 points\n", + " ...\n", + " \n", + "---------- PlanningAgent ----------\n", + "1. Web search agent: Find the total number of rebounds by Dwayne Wade in the 2007-2008 NBA season.\n", + "2. Web search agent: Find the total number of rebounds by Dwayne Wade in the 2008-2009 NBA season.\n", + "[Prompt tokens: 382, Completion tokens: 54]\n", + "---------- DataAnalystAgent ----------\n", + "[FunctionCall(id='call_BkPBFkpuTG6c3eeoACrrRX7V', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 season\"}', name='search_web_tool'), FunctionCall(id='call_5LQquT7ZUAAQRf7gvckeTVdQ', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 season\"}', name='search_web_tool')]\n", + "[Prompt tokens: 416, Completion tokens: 68]\n", + "---------- DataAnalystAgent ----------\n", + "[FunctionExecutionResult(content=\"Error: The tool 'search_web_tool' is not available.\", call_id='call_BkPBFkpuTG6c3eeoACrrRX7V'), FunctionExecutionResult(content=\"Error: The tool 'search_web_tool' is not available.\", call_id='call_5LQquT7ZUAAQRf7gvckeTVdQ')]\n", + "---------- DataAnalystAgent ----------\n", + "Tool calls:\n", + "search_web_tool({\"query\": \"Dwyane Wade total rebounds 2007-2008 season\"}) = Error: The tool 'search_web_tool' is not available.\n", + "search_web_tool({\"query\": \"Dwyane Wade total rebounds 2008-2009 season\"}) = Error: The tool 'search_web_tool' is not available.\n", "---------- PlanningAgent ----------\n", - "The Miami Heat player with the highest points in the 2006-2007 season was Dwyane Wade, with a total of 1,397 points. His total rebounds increased from 214 in the 2007-2008 season to 398 in the 2008-2009 season.\n", + "It seems there was a miscommunication in task assignment. Let me reassess and reassign the tasks correctly.\n", "\n", - "Let's have a data analyst calculate the percentage change: \n", + "1. Web search agent: Find the total number of rebounds by Dwayne Wade in the 2007-2008 NBA season.\n", + "2. Web search agent: Find the total number of rebounds by Dwayne Wade in the 2008-2009 NBA season.\n", + "[Prompt tokens: 525, Completion tokens: 76]\n", + "---------- WebSearchAgent ----------\n", + "[FunctionCall(id='call_buIWOtu1dJqPaxJmqMyuRkpj', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 season\"}', name='search_web_tool'), FunctionCall(id='call_qcnHKdoPsNAzMlPvoBvqmt8n', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 season\"}', name='search_web_tool')]\n", + "[Prompt tokens: 599, Completion tokens: 70]\n", + "---------- WebSearchAgent ----------\n", + "[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', call_id='call_buIWOtu1dJqPaxJmqMyuRkpj'), FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', call_id='call_qcnHKdoPsNAzMlPvoBvqmt8n')]\n", + "---------- WebSearchAgent ----------\n", + "Tool calls:\n", + "search_web_tool({\"query\": \"Dwyane Wade total rebounds 2007-2008 season\"}) = The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.\n", + "search_web_tool({\"query\": \"Dwyane Wade total rebounds 2008-2009 season\"}) = The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.\n", + "---------- PlanningAgent ----------\n", + "With this information, we can proceed to calculate the percentage change in Dwyane Wade's total rebounds from the 2007-2008 season to the 2008-2009 season.\n", "\n", - "1. Data analyst: Calculate the percentage change in Dwyane Wade's total rebounds between the 2007-2008 and 2008-2009 seasons using the formula provided by the Web search agent.\n", - "[Prompt tokens: 878, Completion tokens: 116]\n", + "1. Data analyst: Calculate the percentage change in Dwyane Wade's total rebounds between the 2007-2008 (214 rebounds) and the 2008-2009 (398 rebounds) NBA seasons.\n", + "[Prompt tokens: 711, Completion tokens: 83]\n", "---------- DataAnalystAgent ----------\n", - "[FunctionCall(id='call_Fh84DXp5MxFzutmKVvclw5Cz', arguments='{\"start\":214,\"end\":398}', name='percentage_change_tool')]\n", - "[Prompt tokens: 942, Completion tokens: 20]\n", + "[FunctionCall(id='call_RjbFpLCehz1Nlk5kYmyMUenB', arguments='{\"start\":214,\"end\":398}', name='percentage_change_tool')]\n", + "[Prompt tokens: 806, Completion tokens: 20]\n", "---------- DataAnalystAgent ----------\n", - "[FunctionExecutionResult(content='85.98130841121495', call_id='call_Fh84DXp5MxFzutmKVvclw5Cz')]\n", + "[FunctionExecutionResult(content='85.98130841121495', call_id='call_RjbFpLCehz1Nlk5kYmyMUenB')]\n", "---------- DataAnalystAgent ----------\n", - "The Miami Heat player with the highest points in the 2006-2007 season was Dwyane Wade. The percentage change in his total rebounds between the 2007-2008 season and the 2008-2009 season was approximately 85.98%.\n", - "[Prompt tokens: 983, Completion tokens: 56]\n", + "Tool calls:\n", + "percentage_change_tool({\"start\":214,\"end\":398}) = 85.98130841121495\n", "---------- PlanningAgent ----------\n", - "The Miami Heat player with the highest points in the 2006-2007 season was Dwyane Wade, with a total of 1,397 points. Between the 2007-2008 and 2008-2009 seasons, his total rebounds increased by approximately 85.98%. \n", + "Based on the data collected, Dwyane Wade was the Miami Heat player with the highest points in the 2006-2007 NBA season, scoring a total of 1,397 points. Between the 2007-2008 and 2008-2009 seasons, Dwyane Wade's total rebounds increased from 214 to 398. This represents an approximate 85.98% increase in his total rebounds.\n", "\n", "TERMINATE\n", - "[Prompt tokens: 1065, Completion tokens: 65]\n", + "[Prompt tokens: 834, Completion tokens: 90]\n", "---------- Summary ----------\n", - "Number of messages: 8\n", + "Number of messages: 18\n", "Finish reason: Text 'TERMINATE' mentioned\n", - "Total prompt tokens: 5257\n", - "Total completion tokens: 526\n", - "Duration: 11.98 seconds\n" + "Total prompt tokens: 4713\n", + "Total completion tokens: 619\n", + "Duration: 11.72 seconds\n" ] }, { "data": { "text/plain": [ - "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=595, completion_tokens=115), content=\"To solve this inquiry, let's break it down into smaller tasks again:\\n\\n1. Find out who was the Miami Heat player with the highest points in the 2006-2007 NBA season.\\n2. Find that player's total rebounds for the 2007-2008 NBA season.\\n3. Find that player's total rebounds for the 2008-2009 NBA season.\\n4. Calculate the percentage change in the player's total rebounds from the 2007-2008 to the 2008-2009 season.\\n\\nLet's proceed with these tasks and find the necessary information.\"), TextMessage(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=794, completion_tokens=154), content='The Miami Heat player with the highest points in the 2006-2007 season was Dwyane Wade, who scored a total of 1,397 points.\\n\\nIn terms of his rebound statistics:\\n- In the 2007-2008 season, Dwyane Wade recorded 214 total rebounds.\\n- In the 2008-2009 season, he recorded 398 total rebounds.\\n\\nTo find the percentage change in his total rebounds, a data analyst would perform the following calculation:\\n\\n\\\\[\\n\\\\text{Percentage Change} = \\\\left( \\\\frac{398 - 214}{214} \\\\right) \\\\times 100\\n\\\\]\\n\\nA data analyst would use the above numbers to determine the percentage change in his total rebounds between these two seasons.'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=878, completion_tokens=116), content=\"The Miami Heat player with the highest points in the 2006-2007 season was Dwyane Wade, with a total of 1,397 points. His total rebounds increased from 214 in the 2007-2008 season to 398 in the 2008-2009 season.\\n\\nLet's have a data analyst calculate the percentage change: \\n\\n1. Data analyst: Calculate the percentage change in Dwyane Wade's total rebounds between the 2007-2008 and 2008-2009 seasons using the formula provided by the Web search agent.\"), ToolCallMessage(source='DataAnalystAgent', models_usage=RequestUsage(prompt_tokens=942, completion_tokens=20), content=[FunctionCall(id='call_Fh84DXp5MxFzutmKVvclw5Cz', arguments='{\"start\":214,\"end\":398}', name='percentage_change_tool')]), ToolCallResultMessage(source='DataAnalystAgent', models_usage=None, content=[FunctionExecutionResult(content='85.98130841121495', call_id='call_Fh84DXp5MxFzutmKVvclw5Cz')]), TextMessage(source='DataAnalystAgent', models_usage=RequestUsage(prompt_tokens=983, completion_tokens=56), content='The Miami Heat player with the highest points in the 2006-2007 season was Dwyane Wade. The percentage change in his total rebounds between the 2007-2008 season and the 2008-2009 season was approximately 85.98%.'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=1065, completion_tokens=65), content='The Miami Heat player with the highest points in the 2006-2007 season was Dwyane Wade, with a total of 1,397 points. Between the 2007-2008 and 2008-2009 seasons, his total rebounds increased by approximately 85.98%. \\n\\nTERMINATE')], stop_reason=\"Text 'TERMINATE' mentioned\")" + "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?', type='TextMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=159, completion_tokens=132), content=\"To address this query, we'll need to break it down into a few specific tasks:\\n\\n1. Web search agent: Identify the Miami Heat player with the highest points in the 2006-2007 NBA season.\\n2. Web search agent: Find the total number of rebounds by this player in the 2007-2008 NBA season.\\n3. Web search agent: Find the total number of rebounds by this player in the 2008-2009 NBA season.\\n4. Data analyst: Calculate the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons.\\n\\nLet's get started with these tasks.\", type='TextMessage'), ToolCallMessage(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=281, completion_tokens=26), content=[FunctionCall(id='call_TSUHOBKhpHmTNoYeJzwSP5V4', arguments='{\"query\":\"Miami Heat highest points player 2006-2007 season\"}', name='search_web_tool')], type='ToolCallMessage'), ToolCallResultMessage(source='WebSearchAgent', models_usage=None, content=[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', call_id='call_TSUHOBKhpHmTNoYeJzwSP5V4')], type='ToolCallResultMessage'), TextMessage(source='WebSearchAgent', models_usage=None, content='Tool calls:\\nsearch_web_tool({\"query\":\"Miami Heat highest points player 2006-2007 season\"}) = Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', type='TextMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=382, completion_tokens=54), content='1. Web search agent: Find the total number of rebounds by Dwayne Wade in the 2007-2008 NBA season.\\n2. Web search agent: Find the total number of rebounds by Dwayne Wade in the 2008-2009 NBA season.', type='TextMessage'), ToolCallMessage(source='DataAnalystAgent', models_usage=RequestUsage(prompt_tokens=416, completion_tokens=68), content=[FunctionCall(id='call_BkPBFkpuTG6c3eeoACrrRX7V', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 season\"}', name='search_web_tool'), FunctionCall(id='call_5LQquT7ZUAAQRf7gvckeTVdQ', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 season\"}', name='search_web_tool')], type='ToolCallMessage'), ToolCallResultMessage(source='DataAnalystAgent', models_usage=None, content=[FunctionExecutionResult(content=\"Error: The tool 'search_web_tool' is not available.\", call_id='call_BkPBFkpuTG6c3eeoACrrRX7V'), FunctionExecutionResult(content=\"Error: The tool 'search_web_tool' is not available.\", call_id='call_5LQquT7ZUAAQRf7gvckeTVdQ')], type='ToolCallResultMessage'), TextMessage(source='DataAnalystAgent', models_usage=None, content='Tool calls:\\nsearch_web_tool({\"query\": \"Dwyane Wade total rebounds 2007-2008 season\"}) = Error: The tool \\'search_web_tool\\' is not available.\\nsearch_web_tool({\"query\": \"Dwyane Wade total rebounds 2008-2009 season\"}) = Error: The tool \\'search_web_tool\\' is not available.', type='TextMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=525, completion_tokens=76), content='It seems there was a miscommunication in task assignment. Let me reassess and reassign the tasks correctly.\\n\\n1. Web search agent: Find the total number of rebounds by Dwayne Wade in the 2007-2008 NBA season.\\n2. Web search agent: Find the total number of rebounds by Dwayne Wade in the 2008-2009 NBA season.', type='TextMessage'), ToolCallMessage(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=599, completion_tokens=70), content=[FunctionCall(id='call_buIWOtu1dJqPaxJmqMyuRkpj', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 season\"}', name='search_web_tool'), FunctionCall(id='call_qcnHKdoPsNAzMlPvoBvqmt8n', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 season\"}', name='search_web_tool')], type='ToolCallMessage'), ToolCallResultMessage(source='WebSearchAgent', models_usage=None, content=[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', call_id='call_buIWOtu1dJqPaxJmqMyuRkpj'), FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', call_id='call_qcnHKdoPsNAzMlPvoBvqmt8n')], type='ToolCallResultMessage'), TextMessage(source='WebSearchAgent', models_usage=None, content='Tool calls:\\nsearch_web_tool({\"query\": \"Dwyane Wade total rebounds 2007-2008 season\"}) = The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.\\nsearch_web_tool({\"query\": \"Dwyane Wade total rebounds 2008-2009 season\"}) = The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', type='TextMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=711, completion_tokens=83), content=\"With this information, we can proceed to calculate the percentage change in Dwyane Wade's total rebounds from the 2007-2008 season to the 2008-2009 season.\\n\\n1. Data analyst: Calculate the percentage change in Dwyane Wade's total rebounds between the 2007-2008 (214 rebounds) and the 2008-2009 (398 rebounds) NBA seasons.\", type='TextMessage'), ToolCallMessage(source='DataAnalystAgent', models_usage=RequestUsage(prompt_tokens=806, completion_tokens=20), content=[FunctionCall(id='call_RjbFpLCehz1Nlk5kYmyMUenB', arguments='{\"start\":214,\"end\":398}', name='percentage_change_tool')], type='ToolCallMessage'), ToolCallResultMessage(source='DataAnalystAgent', models_usage=None, content=[FunctionExecutionResult(content='85.98130841121495', call_id='call_RjbFpLCehz1Nlk5kYmyMUenB')], type='ToolCallResultMessage'), TextMessage(source='DataAnalystAgent', models_usage=None, content='Tool calls:\\npercentage_change_tool({\"start\":214,\"end\":398}) = 85.98130841121495', type='TextMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=834, completion_tokens=90), content=\"Based on the data collected, Dwyane Wade was the Miami Heat player with the highest points in the 2006-2007 NBA season, scoring a total of 1,397 points. Between the 2007-2008 and 2008-2009 seasons, Dwyane Wade's total rebounds increased from 214 to 398. This represents an approximate 85.98% increase in his total rebounds.\\n\\nTERMINATE\", type='TextMessage')], stop_reason=\"Text 'TERMINATE' mentioned\")" ] }, - "execution_count": 13, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -425,6 +464,8 @@ " return None\n", "\n", "\n", + "# Reset the previous team and run the chat again with the selector function.\n", + "await team.reset()\n", "team = SelectorGroupChat(\n", " [planning_agent, web_search_agent, data_analyst_agent],\n", " model_client=OpenAIChatCompletionClient(model=\"gpt-4o-mini\"),\n", @@ -459,7 +500,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.6" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/tutorial/swarm.ipynb b/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/tutorial/swarm.ipynb index c1583848a503..480ca60d8b07 100644 --- a/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/tutorial/swarm.ipynb +++ b/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/tutorial/swarm.ipynb @@ -89,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -130,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -174,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -184,57 +184,54 @@ "---------- user ----------\n", "I need to refund my flight.\n", "---------- travel_agent ----------\n", - "[FunctionCall(id='call_epnozsBbe9i4swPaaBIR4Enl', arguments='{}', name='transfer_to_flights_refunder')]\n", - "[Prompt tokens: 327, Completion tokens: 14]\n", + "[FunctionCall(id='call_ZQ2rGjq4Z29pd0yP2sNcuyd2', arguments='{}', name='transfer_to_flights_refunder')]\n", + "[Prompt tokens: 119, Completion tokens: 14]\n", "---------- travel_agent ----------\n", - "[FunctionExecutionResult(content='Transferred to flights_refunder, adopting the role of flights_refunder immediately.', call_id='call_epnozsBbe9i4swPaaBIR4Enl')]\n", + "[FunctionExecutionResult(content='Transferred to flights_refunder, adopting the role of flights_refunder immediately.', call_id='call_ZQ2rGjq4Z29pd0yP2sNcuyd2')]\n", "---------- travel_agent ----------\n", "Transferred to flights_refunder, adopting the role of flights_refunder immediately.\n", "---------- flights_refunder ----------\n", - "I can help you with that. Could you please provide me with your flight reference number so I can process the refund?\n", - "[Prompt tokens: 450, Completion tokens: 25]\n", + "Could you please provide me with the flight reference number so I can process the refund for you?\n", + "[Prompt tokens: 191, Completion tokens: 20]\n", "---------- flights_refunder ----------\n", - "[FunctionCall(id='call_giMQVbQ7mXahC5G3eC0wvnCv', arguments='{}', name='transfer_to_user')]\n", - "[Prompt tokens: 483, Completion tokens: 11]\n", + "[FunctionCall(id='call_1iRfzNpxTJhRTW2ww9aQJ8sK', arguments='{}', name='transfer_to_user')]\n", + "[Prompt tokens: 219, Completion tokens: 11]\n", "---------- flights_refunder ----------\n", - "[FunctionExecutionResult(content='Transferred to user, adopting the role of user immediately.', call_id='call_giMQVbQ7mXahC5G3eC0wvnCv')]\n", + "[FunctionExecutionResult(content='Transferred to user, adopting the role of user immediately.', call_id='call_1iRfzNpxTJhRTW2ww9aQJ8sK')]\n", "---------- flights_refunder ----------\n", "Transferred to user, adopting the role of user immediately.\n", "---------- Summary ----------\n", "Number of messages: 8\n", "Finish reason: Handoff to user from flights_refunder detected.\n", - "Total prompt tokens: 1260\n", - "Total completion tokens: 50\n", - "Duration: 1.79 seconds\n", + "Total prompt tokens: 529\n", + "Total completion tokens: 45\n", + "Duration: 2.05 seconds\n", "---------- user ----------\n", "Sure, it's 507811\n", "---------- flights_refunder ----------\n", - "[FunctionCall(id='call_ACcFykJ3fPzanMwy1YGxG4L4', arguments='{\"flight_id\":\"507811\"}', name='refund_flight')]\n", - "[Prompt tokens: 530, Completion tokens: 18]\n", + "[FunctionCall(id='call_UKCsoEBdflkvpuT9Bi2xlvTd', arguments='{\"flight_id\":\"507811\"}', name='refund_flight')]\n", + "[Prompt tokens: 266, Completion tokens: 18]\n", "---------- flights_refunder ----------\n", - "[FunctionExecutionResult(content='Flight 507811 refunded', call_id='call_ACcFykJ3fPzanMwy1YGxG4L4')]\n", + "[FunctionExecutionResult(content='Flight 507811 refunded', call_id='call_UKCsoEBdflkvpuT9Bi2xlvTd')]\n", "---------- flights_refunder ----------\n", - "Your flight with the reference number 507811 has been successfully refunded. If there is anything else you need help with, feel free to ask!\n", - "[Prompt tokens: 488, Completion tokens: 30]\n", + "Tool calls:\n", + "refund_flight({\"flight_id\":\"507811\"}) = Flight 507811 refunded\n", "---------- flights_refunder ----------\n", - "[FunctionCall(id='call_9NjAP8yD1qgwNL4Zfntt4dVb', arguments='{}', name='transfer_to_travel_agent')]\n", - "[Prompt tokens: 605, Completion tokens: 13]\n", + "[FunctionCall(id='call_MQ2CXR8UhVtjNc6jG3wSQp2W', arguments='{}', name='transfer_to_travel_agent')]\n", + "[Prompt tokens: 303, Completion tokens: 13]\n", "---------- flights_refunder ----------\n", - "[FunctionExecutionResult(content='Transferred to travel_agent, adopting the role of travel_agent immediately.', call_id='call_9NjAP8yD1qgwNL4Zfntt4dVb')]\n", + "[FunctionExecutionResult(content='Transferred to travel_agent, adopting the role of travel_agent immediately.', call_id='call_MQ2CXR8UhVtjNc6jG3wSQp2W')]\n", "---------- flights_refunder ----------\n", "Transferred to travel_agent, adopting the role of travel_agent immediately.\n", "---------- travel_agent ----------\n", - "If you need further assistance with travel planning or any other inquiries, just let me know. Have a wonderful day!\n", - "[Prompt tokens: 495, Completion tokens: 24]\n", - "---------- travel_agent ----------\n", - "TERMINATE\n", - "[Prompt tokens: 525, Completion tokens: 4]\n", + "Your flight with reference number 507811 has been successfully refunded. If you need anything else, feel free to let me know. Safe travels! TERMINATE\n", + "[Prompt tokens: 272, Completion tokens: 32]\n", "---------- Summary ----------\n", - "Number of messages: 9\n", + "Number of messages: 8\n", "Finish reason: Text 'TERMINATE' mentioned\n", - "Total prompt tokens: 2643\n", - "Total completion tokens: 89\n", - "Duration: 6.63 seconds\n" + "Total prompt tokens: 841\n", + "Total completion tokens: 63\n", + "Duration: 1.64 seconds\n" ] } ], @@ -299,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -331,7 +328,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -350,7 +347,7 @@ " - News Analyst: For news gathering and analysis\n", " - Writer: For compiling final report\n", " Always send your plan first, then handoff to appropriate agent.\n", - " Handoff to a single agent at a time.\n", + " Always handoff to a single agent at a time.\n", " Use TERMINATE when research is complete.\"\"\",\n", ")\n", "\n", @@ -388,7 +385,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -398,117 +395,96 @@ "---------- user ----------\n", "Conduct market research for TSLA stock\n", "---------- planner ----------\n", - "[FunctionCall(id='call_IXFe9RcGbYGNf0V7B2hvDNJI', arguments='{}', name='transfer_to_financial_analyst')]\n", - "[Prompt tokens: 168, Completion tokens: 149]\n", + "[FunctionCall(id='call_BX5QaRuhmB8CxTsBlqCUIXPb', arguments='{}', name='transfer_to_financial_analyst')]\n", + "[Prompt tokens: 169, Completion tokens: 166]\n", "---------- planner ----------\n", - "[FunctionExecutionResult(content='Transferred to financial_analyst, adopting the role of financial_analyst immediately.', call_id='call_IXFe9RcGbYGNf0V7B2hvDNJI')]\n", + "[FunctionExecutionResult(content='Transferred to financial_analyst, adopting the role of financial_analyst immediately.', call_id='call_BX5QaRuhmB8CxTsBlqCUIXPb')]\n", "---------- planner ----------\n", "Transferred to financial_analyst, adopting the role of financial_analyst immediately.\n", "---------- financial_analyst ----------\n", - "[FunctionCall(id='call_2IYcTAXiufX1SBmnMJOG9HPq', arguments='{\"symbol\":\"TSLA\"}', name='get_stock_data')]\n", + "[FunctionCall(id='call_SAXy1ebtA9mnaZo4ztpD2xHA', arguments='{\"symbol\":\"TSLA\"}', name='get_stock_data')]\n", "[Prompt tokens: 136, Completion tokens: 16]\n", "---------- financial_analyst ----------\n", - "[FunctionExecutionResult(content=\"{'price': 180.25, 'volume': 1000000, 'pe_ratio': 65.4, 'market_cap': '700B'}\", call_id='call_2IYcTAXiufX1SBmnMJOG9HPq')]\n", + "[FunctionExecutionResult(content=\"{'price': 180.25, 'volume': 1000000, 'pe_ratio': 65.4, 'market_cap': '700B'}\", call_id='call_SAXy1ebtA9mnaZo4ztpD2xHA')]\n", "---------- financial_analyst ----------\n", - "Here's the market research for TSLA (Tesla) stock:\n", - "\n", - "- **Current Price**: $180.25\n", - "- **Trading Volume**: 1,000,000 shares\n", - "- **Price to Earnings (P/E) Ratio**: 65.4\n", - "- **Market Capitalization**: $700 Billion\n", - "\n", - "These metrics can help evaluate Tesla's stock performance, value in the market, and overall investment appeal. If you need a specific analysis or additional data, feel free to let me know!\n", - "[Prompt tokens: 162, Completion tokens: 103]\n", + "Tool calls:\n", + "get_stock_data({\"symbol\":\"TSLA\"}) = {'price': 180.25, 'volume': 1000000, 'pe_ratio': 65.4, 'market_cap': '700B'}\n", "---------- financial_analyst ----------\n", - "[FunctionCall(id='call_ji8SdlXI1uga2SNenIZMvPOR', arguments='{}', name='transfer_to_planner')]\n", - "[Prompt tokens: 310, Completion tokens: 12]\n", + "[FunctionCall(id='call_IsdcFUfBVmtcVzfSuwQpeAwl', arguments='{}', name='transfer_to_planner')]\n", + "[Prompt tokens: 199, Completion tokens: 337]\n", "---------- financial_analyst ----------\n", - "[FunctionExecutionResult(content='Transferred to planner, adopting the role of planner immediately.', call_id='call_ji8SdlXI1uga2SNenIZMvPOR')]\n", + "[FunctionExecutionResult(content='Transferred to planner, adopting the role of planner immediately.', call_id='call_IsdcFUfBVmtcVzfSuwQpeAwl')]\n", "---------- financial_analyst ----------\n", "Transferred to planner, adopting the role of planner immediately.\n", "---------- planner ----------\n", - "[FunctionCall(id='call_aQUm1B1jzvnWF9aWLwfn2VxS', arguments='{}', name='transfer_to_news_analyst')]\n", - "[Prompt tokens: 346, Completion tokens: 14]\n", + "[FunctionCall(id='call_tN5goNFahrdcSfKnQqT0RONN', arguments='{}', name='transfer_to_news_analyst')]\n", + "[Prompt tokens: 291, Completion tokens: 14]\n", "---------- planner ----------\n", - "[FunctionExecutionResult(content='Transferred to news_analyst, adopting the role of news_analyst immediately.', call_id='call_aQUm1B1jzvnWF9aWLwfn2VxS')]\n", + "[FunctionExecutionResult(content='Transferred to news_analyst, adopting the role of news_analyst immediately.', call_id='call_tN5goNFahrdcSfKnQqT0RONN')]\n", "---------- planner ----------\n", "Transferred to news_analyst, adopting the role of news_analyst immediately.\n", "---------- news_analyst ----------\n", - "[FunctionCall(id='call_n5RmgbQgdyfE7EX5NUsKwApq', arguments='{\"query\":\"Tesla stock performance\"}', name='get_news')]\n", - "[Prompt tokens: 291, Completion tokens: 16]\n", + "[FunctionCall(id='call_Owjw6ZbiPdJgNWMHWxhCKgsp', arguments='{\"query\":\"Tesla market news\"}', name='get_news')]\n", + "[Prompt tokens: 235, Completion tokens: 16]\n", + "---------- news_analyst ----------\n", + "[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')]\n", "---------- news_analyst ----------\n", - "[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_n5RmgbQgdyfE7EX5NUsKwApq')]\n", + "Tool calls:\n", + "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.\"}]\n", "---------- news_analyst ----------\n", - "Here are some recent news articles related to TSLA (Tesla) stock that may influence its market performance:\n", + "Here are some of the key market insights regarding Tesla (TSLA):\n", "\n", - "1. **Tesla Expands Cybertruck Production** (March 20, 2024)\n", - " - Tesla has ramped up its Cybertruck manufacturing capacity at the Gigafactory in Texas, aiming to meet the strong demand for this vehicle.\n", + "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.\n", "\n", - "2. **Tesla FSD Beta Shows Promise** (March 19, 2024)\n", - " - The latest Full Self-Driving (FSD) beta update demonstrates notable improvements in urban navigation and safety features, suggesting potential advancements in Tesla's autonomous driving technology.\n", + "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.\n", "\n", - "3. **Model Y Dominates Global EV Sales** (March 18, 2024)\n", - " - Tesla's Model Y has become the best-selling electric vehicle worldwide, capturing a significant share of the market, which could positively impact the company's revenue streams.\n", + "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.\n", "\n", - "If you'd like a more detailed analysis or further information, please let me know!\n", - "[Prompt tokens: 414, Completion tokens: 192]\n", + "These developments reflect Tesla's ongoing innovation and ability to capture market demand, which could positively influence its stock performance and market position. \n", + "\n", + "I will now hand off back to the planner.\n", + "[Prompt tokens: 398, Completion tokens: 203]\n", "---------- news_analyst ----------\n", - "[FunctionCall(id='call_7Ka5f5k2yZ8flfvZWKNXDQjL', arguments='{}', name='transfer_to_planner')]\n", - "[Prompt tokens: 654, Completion tokens: 12]\n", + "[FunctionCall(id='call_pn7y6PKsBspWA17uOh3AKNMT', arguments='{}', name='transfer_to_planner')]\n", + "[Prompt tokens: 609, Completion tokens: 12]\n", "---------- news_analyst ----------\n", - "[FunctionExecutionResult(content='Transferred to planner, adopting the role of planner immediately.', call_id='call_7Ka5f5k2yZ8flfvZWKNXDQjL')]\n", + "[FunctionExecutionResult(content='Transferred to planner, adopting the role of planner immediately.', call_id='call_pn7y6PKsBspWA17uOh3AKNMT')]\n", "---------- news_analyst ----------\n", "Transferred to planner, adopting the role of planner immediately.\n", "---------- planner ----------\n", - "[FunctionCall(id='call_zl0E18TZWoCPykYqG7jpR2mr', arguments='{}', name='transfer_to_writer')]\n", - "[Prompt tokens: 611, Completion tokens: 11]\n", + "[FunctionCall(id='call_MmXyWuD2uJT64ZdVI5NfhYdX', arguments='{}', name='transfer_to_writer')]\n", + "[Prompt tokens: 722, Completion tokens: 11]\n", "---------- planner ----------\n", - "[FunctionExecutionResult(content='Transferred to writer, adopting the role of writer immediately.', call_id='call_zl0E18TZWoCPykYqG7jpR2mr')]\n", + "[FunctionExecutionResult(content='Transferred to writer, adopting the role of writer immediately.', call_id='call_MmXyWuD2uJT64ZdVI5NfhYdX')]\n", "---------- planner ----------\n", "Transferred to writer, adopting the role of writer immediately.\n", "---------- writer ----------\n", - "### Market Research Report: Tesla (TSLA) Stock\n", - "\n", - "#### Stock Performance Overview\n", - "\n", - "- **Current Price**: $180.25\n", - "- **Trading Volume**: 1,000,000 shares\n", - "- **Price to Earnings (P/E) Ratio**: 65.4\n", - "- **Market Capitalization**: $700 Billion\n", - "\n", - "Tesla's stock is currently valued at $180.25 per share, with a high trading volume reflecting active investor interest. The P/E ratio of 65.4 indicates that the stock might be viewed as overvalued compared to traditional industries, but it is common for tech and innovation-driven companies. The market capitalization of $700 billion underscores Tesla’s significant presence in the automotive and technology markets.\n", - "\n", - "#### Recent News Impacting Tesla (TSLA)\n", - "\n", - "1. **Expansion of Cybertruck Production**: Tesla has increased its Cybertruck production at the Texas Gigafactory, responding to heightened demand. This expansion strategy could drive future revenues and solidify Tesla's innovative image.\n", - "\n", - "2. **Advancements in Full Self-Driving (FSD) Technology**: The latest Tesla FSD beta update highlights promising improvements. Enhanced safety and urban navigation may bolster Tesla's reputation in the autonomous vehicle domain, potentially increasing its market value.\n", - "\n", - "3. **Model Y's Global Sales Leadership**: The Model Y has emerged as the leading global electric vehicle in terms of sales. This achievement not only boosts Tesla's revenue streams but also cements its position as a leader in the EV segment.\n", - "\n", - "### Conclusion\n", - "\n", - "Tesla’s market dynamics show strong innovation and consumer interest, which reflect positively in its stock market valuation and significant news coverage. The company continues to lead in the electric vehicle market and expand its capabilities in autonomous driving, thereby potentially increasing its financial performance and market leadership.\n", - "\n", - "For further analysis or inquiries, feel free to reach out.\n", - "[Prompt tokens: 489, Completion tokens: 371]\n", - "---------- writer ----------\n", - "[FunctionCall(id='call_9buNd5ud2MTRyX50X2EjQJqp', arguments='{}', name='transfer_to_planner')]\n", - "[Prompt tokens: 865, Completion tokens: 12]\n", + "[FunctionCall(id='call_Pdgu39O6GMYplBiB8jp3uyN3', arguments='{}', name='transfer_to_planner')]\n", + "[Prompt tokens: 599, Completion tokens: 323]\n", "---------- writer ----------\n", - "[FunctionExecutionResult(content='Transferred to planner, adopting the role of planner immediately.', call_id='call_9buNd5ud2MTRyX50X2EjQJqp')]\n", + "[FunctionExecutionResult(content='Transferred to planner, adopting the role of planner immediately.', call_id='call_Pdgu39O6GMYplBiB8jp3uyN3')]\n", "---------- writer ----------\n", "Transferred to planner, adopting the role of planner immediately.\n", "---------- planner ----------\n", "TERMINATE\n", - "[Prompt tokens: 1037, Completion tokens: 4]\n", + "[Prompt tokens: 772, Completion tokens: 4]\n", "---------- Summary ----------\n", "Number of messages: 27\n", "Finish reason: Text 'TERMINATE' mentioned\n", - "Total prompt tokens: 5483\n", - "Total completion tokens: 912\n", - "Duration: 15.26 seconds\n" + "Total prompt tokens: 4130\n", + "Total completion tokens: 1102\n", + "Duration: 17.74 seconds\n" ] + }, + { + "data": { + "text/plain": [ + "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='Conduct market research for TSLA stock', type='TextMessage'), ToolCallMessage(source='planner', models_usage=RequestUsage(prompt_tokens=169, completion_tokens=166), content=[FunctionCall(id='call_BX5QaRuhmB8CxTsBlqCUIXPb', arguments='{}', name='transfer_to_financial_analyst')], type='ToolCallMessage'), 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