Designing Agentic Systems with LangChain
Dilini K. Sumanapala, PhD
Founder & AI Engineer, Genverv, Ltd.
Agent: The 5th of November is famous for the Gunpowder treason and
plot...
string == string[::-1]
Agent: Yes, "madam" is a palindrome...
# Use a decorator to label the tool and set the input format to string @tool
def date_checker(date: str) -> str:
"""Provide a list of important historical events for a given date in any format."""
try: # Invoke the LLM to interpret the date and generate historical events answer = llm.invoke(f"List important historical events that occurred on {date}.")
# Return the response return answer.content
# Set an exception block for errors in retrieval except Exception as e: return f"Error retrieving events: {str(e)}"
@tool
# Set input format to string def check_palindrome(text: str):
"""Check if a word or phrase is a palindrome."""
# Remove non-alphanumeric characters and convert to lowercase cleaned = ''.join(char.lower() for char in text if char.isalnum())
# Check if the reversed text is the same as original text if cleaned == cleaned[::-1]: return f"The phrase or word '{text}' is a palindrome." else: return f"The phrase or word '{text}' is not a palindrome."
# Import modules required for defining tool nodes from langgraph.prebuilt import ToolNode
# List of tools tools = [wikipedia_tool, date_checker, check_palindrome]
# Pass the tools to the ToolNode() tool_node = ToolNode(tools)
# Bind tools to the LLM model_with_tools = llm.bind_tools(tools)
Designing Agentic Systems with LangChain