AI Agent Development

# Example code for implementing a ReAct agent architecture
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_react_agent
from langchain_core.tools import Tool

# Define the agent's system prompt with the ReAct pattern
SYSTEM_PROMPT = """You are an autonomous AI assistant tasked with helping users solve problems.
For each user request, follow these steps:
1. Thought: Think about what needs to be done to complete the task.
2. Action: Select a tool to use from the available tools. 
3. Action Input: Provide the input for the tool.
4. Observation: After receiving the tool's output, analyze the information.
5. Thought: Reason about the observation and what to do next.
6. Action or Response: Either use another tool or respond to the user."""

# Define the agent's prompt template
prompt = ChatPromptTemplate.from_messages([
    ("system", SYSTEM_PROMPT),
    MessagesPlaceholder(variable_name="chat_history"),
    ("human", "{input}"),
    MessagesPlaceholder(variable_name="agent_scratchpad"),
])

# Create example tools
def calculator(expression: str) -> str:
    """Evaluate a mathematical expression."""
    try:
        return str(eval(expression))
    except Exception as e:
        return f"Error: {str(e)}"

def search_web(query: str) -> str:
    """Search the web for information."""
    return f"Found results for: {query}. [Simulated search results]"

# Define the agent's tools
tools = [
    Tool(name="Calculator", func=calculator, description="For mathematical calculations"),
    Tool(name="Search", func=search_web, description="Search the web for information")
]

# Initialize LLM and agent
llm = ChatOpenAI(temperature=0, model="gpt-4o")
agent = create_react_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)