AI Agent Development: Framework, Tools & Best Practices for 2025

from langchain.agents import initialize_agent, Tool
from langchain.llms import OpenAI
from langchain.memory import ConversationBufferMemory

# Define custom tools
def web_search(query: str) -> str:
    """Search the web for information."""
    # Implementation here
    return f"Search results for: {query}"

def calculator(expression: str) -> str:
    """Perform mathematical calculations."""
    try:
        result = eval(expression)
        return f"Result: {result}"
    except:
        return "Invalid expression"

# Set up tools and memory
tools = [
    Tool(name="WebSearch", func=web_search, 
         description="Search for current information"),
    Tool(name="Calculator", func=calculator,
         description="Perform math calculations")
]

memory = ConversationBufferMemory(
    memory_key="chat_history",
    return_messages=True
)

# Initialize agent
llm = OpenAI(temperature=0)
agent = initialize_agent(
    tools=tools,
    llm=llm,
    agent="conversational-react-description",
    memory=memory,
    verbose=True
)

# Run the agent
response = agent.run("What's 15 * 24 and find recent news about AI?")
print(response)