REACT_PROMPT = """Answer the following questions as best you can. You have access to the following tools:

{tools}

Use the following format:

Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question

Question: {input}
Thought: {agent_scratchpad}"""

PLANNER_PROMPT = """You are a task planning assistant. Given a task, create a detailed plan.

Task: {input}

Create a plan with the following format:
1. First step
2. Second step
...

Plan:"""

EXECUTOR_PROMPT = """You are a task executor. Follow the plan and execute each step using available tools:

{tools}

Plan:
{plan}

Current step: {current_step}
Previous results: {previous_results}

Use the following format:
Thought: think about the current step
Action: the action to take
Action Input: the input for the action"""

from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.chat_models import ChatOpenAI

def create_react_agent(tools, llm):
    return initialize_agent(
        tools=tools,
        llm=llm,
        agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
        verbose=True
    )

# Usage example
llm = ChatOpenAI(temperature=0)
tools = [
    Tool(
        name="Search",
        func=search_tool,
        description="Useful for searching information"
    ),
    Tool(
        name="Calculator",
        func=calculator_tool,
        description="Useful for doing calculations"
    )
]

agent = create_react_agent(tools, llm)
result = agent.run("What is the population of China multiplied by 2?")

from langchain.agents import PlanAndExecute
from langchain.chat_models import ChatOpenAI

def create_plan_and_execute_agent(tools, llm):
    return PlanAndExecute(
        planner=create_planner(llm),
        executor=create_executor(llm, tools),
        verbose=True
    )

# Usage example
llm = ChatOpenAI(temperature=0)
agent = create_plan_and_execute_agent(tools, llm)
result = agent.run("What is the population of China multiplied by 2?")

from langchain.agents import create_csv_agent
from langchain.chat_models import ChatOpenAI

def analyze_with_react():
    agent = create_csv_agent(
        ChatOpenAI(temperature=0),
        'sales_data.csv',
        verbose=True
    )

    return agent.run("""
        1. Calculate the total sales
        2. Find the best performing product
        3. Generate a summary report
    """)

from langchain.agents import PlanAndExecute
from langchain.tools import PythonAstREPLTool

def analyze_with_plan_execute():
    agent = create_plan_and_execute_agent(
        llm=ChatOpenAI(temperature=0),
        tools=[
            PythonAstREPLTool(),
            CSVTool('sales_data.csv')
        ]
    )

    return agent.run("""
        1. Calculate the total sales
        2. Find the best performing product
        3. Generate a summary report
    """)