Master Prompt Engineering & AI Agents

import os
from langchain.llms import OpenAI
from langchain.memory import ConversationBufferMemory
from langchain.tools import Tool
from langchain.agents import initialize_agent, AgentType
# Define the AI Agent class
class AIAgent:
def __init__(self, name, description, tools=None):
self.name = name
self.description = description
# Initialize the language model
self.llm = OpenAI(temperature=0.7)
# Set up memory system
self.memory = ConversationBufferMemory(memory_key="chat_history")
# Configure available tools
self.tools = tools or []
# Initialize the agent
self.agent = initialize_agent(
tools=self.tools,
llm=self.llm,
agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
memory=self.memory,
verbose=True
)
def add_tool(self, tool):
"""Add a new tool to the agent's capabilities"""
self.tools.append(tool)
self.agent = initialize_agent(
tools=self.tools,
llm=self.llm,
agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
memory=self.memory,
verbose=True
)
def process(self, user_input):
"""Process user input and generate a response"""
return self.agent.run(user_input)
# Example usage
if __name__ == "__main__":
# Define some example tools
search_tool = Tool(
name="Search",
func=lambda x: f"Search results for: {x}",
description="Useful for searching the web for information."
)
calculator_tool = Tool(
name="Calculator",
func=lambda x: f"Result: {eval(x)}",
description="Useful for performing mathematical calculations."
)
# Create an AI assistant agent
assistant = AIAgent(
name="Research Assistant",
description="An AI assistant that helps with research tasks.",
tools=[search_tool, calculator_tool]
)
# Test the agent
response = assistant.process("I need information about prompt engineering techniques.")
print(response)