to the 0.34 power is 2.7212987634680084.        > Finished chain.            > Entering new AgentExecutor chain...     I need to find out who Beyonce's husband is and then calculate his age raised to the 0.19 power.    Action: Google Serper    Action Input: "Who is Beyonce's husband?"    Observation: Jay-Z    Thought: I need to find out Jay-Z's age    Action: Google Serper    Action Input: "How old is Jay-Z?"    Observation: 53 years    Thought: I need to calculate 53 raised to the 0.19 power    Action: Calculator    Action Input: 53^0.19    Observation: Answer: 2.12624064206896    Thought: I now know the final answer    Final Answer: Jay-Z is Beyonce's husband and his age raised to the 0.19 power is 2.12624064206896.        > Finished chain.    Serial executed in 89.97 seconds.llm = OpenAI(temperature=0)tools = load_tools(["google-serper", "llm-math"], llm=llm)agent = initialize_agent(    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)s = time.perf_counter()# If running this outside of Jupyter, use asyncio.run or loop.run_until_completetasks = [agent.arun(q) for q in questions]await asyncio.gather(*tasks)elapsed = time.perf_counter() - sprint(f"Concurrent executed in {elapsed:0.2f} seconds.")            > Entering new AgentExecutor chain...            > Entering new AgentExecutor chain...            > Entering new AgentExecutor chain...            > Entering new AgentExecutor chain...            > Entering new AgentExecutor chain...     I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.    Action: Google Serper    Action Input: "Olivia Wilde boyfriend" I need to find out who Beyonce's husband is and then calculate his age raised to the 0.19 power.    Action: Google Serper    Action Input: "Who is Beyonce's husband?" I need to find out who won the most recent formula 1 grand prix and then calculate their age raised to the 0.23 power.    Action: Google Serper    Action Input: "most recent
LangChain provides async support for Agents by leveraging the asyncio library.
LangChain provides async support for Agents by leveraging the asyncio library. ->: to the 0.34 power is 2.7212987634680084.        > Finished chain.            > Entering new AgentExecutor chain...     I need to find out who Beyonce's husband is and then calculate his age raised to the 0.19 power.    Action: Google Serper    Action Input: "Who is Beyonce's husband?"    Observation: Jay-Z    Thought: I need to find out Jay-Z's age    Action: Google Serper    Action Input: "How old is Jay-Z?"    Observation: 53 years    Thought: I need to calculate 53 raised to the 0.19 power    Action: Calculator    Action Input: 53^0.19    Observation: Answer: 2.12624064206896    Thought: I now know the final answer    Final Answer: Jay-Z is Beyonce's husband and his age raised to the 0.19 power is 2.12624064206896.        > Finished chain.    Serial executed in 89.97 seconds.llm = OpenAI(temperature=0)tools = load_tools(["google-serper", "llm-math"], llm=llm)agent = initialize_agent(    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)s = time.perf_counter()# If running this outside of Jupyter, use asyncio.run or loop.run_until_completetasks = [agent.arun(q) for q in questions]await asyncio.gather(*tasks)elapsed = time.perf_counter() - sprint(f"Concurrent executed in {elapsed:0.2f} seconds.")            > Entering new AgentExecutor chain...            > Entering new AgentExecutor chain...            > Entering new AgentExecutor chain...            > Entering new AgentExecutor chain...            > Entering new AgentExecutor chain...     I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.    Action: Google Serper    Action Input: "Olivia Wilde boyfriend" I need to find out who Beyonce's husband is and then calculate his age raised to the 0.19 power.    Action: Google Serper    Action Input: "Who is Beyonce's husband?" I need to find out who won the most recent formula 1 grand prix and then calculate their age raised to the 0.23 power.    Action: Google Serper    Action Input: "most recent