Build conversational AI agents with memory capabilities. This integration combines AutoGen for creating AI agents with Zentry for memory management, enabling context-aware and personalized interactions.
📢 Announcing our research paper: Zentry achieves 26% higher accuracy than OpenAI Memory, 91% lower latency, and 90% token savings! Read the paper to learn how we're revolutionizing AI agent memory.

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Overview

In this guide, we’ll explore an example of creating a conversational AI system with memory:
  • A customer service bot that can recall previous interactions and provide personalized responses.

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Setup and Configuration

Install necessary libraries: 
pip install pyautogen Zentryai openai
First, we’ll import the necessary libraries and set up our configurations.
Remember to get the Zentry API key from Zentry Platform.
import os
from autogen import ConversableAgent
from Zentry import MemoryClient
from openai import OpenAI

# Configuration
OPENAI_API_KEY = 'sk-xxx'  # Replace with your actual OpenAI API key
Zentry_API_KEY = 'your-Zentry-key'  # Replace with your actual Zentry API key from https://app.zentry.gg
USER_ID = "customer_service_bot"

# Set up OpenAI API key
os.environ['OPENAI_API_KEY'] = OPENAI_API_KEY
os.environ['Zentry_API_KEY'] = Zentry_API_KEY

# Initialize Zentry and AutoGen agents
memory_client = MemoryClient()
agent = ConversableAgent(
    "chatbot",
    llm_config={"config_list": [{"model": "gpt-4", "api_key": OPENAI_API_KEY}]},
    code_execution_config=False,
    human_input_mode="NEVER",
)

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Storing Conversations in Memory

Add conversation history to Zentry for future reference: 
conversation = [
    {"role": "assistant", "content": "Hi, I'm Best Buy's chatbot! How can I help you?"},
    {"role": "user", "content": "I'm seeing horizontal lines on my TV."},
    {"role": "assistant", "content": "I'm sorry to hear that. Can you provide your TV model?"},
    {"role": "user", "content": "It's a Sony - 77\" Class BRAVIA XR A80K OLED 4K UHD Smart Google TV"},
    {"role": "assistant", "content": "Thank you for the information. Let's troubleshoot this issue..."}
]

memory_client.add(messages=conversation, user_id=USER_ID)
print("Conversation added to memory.")

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Retrieving and Using Memory

Create a function to get context-aware responses based on user’s question and previous interactions: 
def get_context_aware_response(question):
    relevant_memories = memory_client.search(question, user_id=USER_ID)
    context = "\n".join([m["memory"] for m in relevant_memories])

    prompt = f"""Answer the user question considering the previous interactions:
    Previous interactions:
    {context}

    Question: {question}
    """

    reply = agent.generate_reply(messages=[{"content": prompt, "role": "user"}])
    return reply

# Example usage
question = "What was the issue with my TV?"
answer = get_context_aware_response(question)
print("Context-aware answer:", answer)

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Multi-Agent Conversation

For more complex scenarios, you can create multiple agents: 
manager = ConversableAgent(
    "manager",
    system_message="You are a manager who helps in resolving complex customer issues.",
    llm_config={"config_list": [{"model": "gpt-4", "api_key": OPENAI_API_KEY}]},
    human_input_mode="NEVER"
)

def escalate_to_manager(question):
    relevant_memories = memory_client.search(question, user_id=USER_ID)
    context = "\n".join([m["memory"] for m in relevant_memories])

    prompt = f"""
    Context from previous interactions:
    {context}

    Customer question: {question}

    As a manager, how would you address this issue?
    """

    manager_response = manager.generate_reply(messages=[{"content": prompt, "role": "user"}])
    return manager_response

# Example usage
complex_question = "I'm not satisfied with the troubleshooting steps. What else can be done?"
manager_answer = escalate_to_manager(complex_question)
print("Manager's response:", manager_answer)

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Conclusion

By integrating AutoGen with Zentry, you’ve created a conversational AI system with memory capabilities. This example demonstrates a customer service bot that can recall previous interactions and provide context-aware responses, with the ability to escalate complex issues to a manager agent. This integration enables the creation of more intelligent and personalized AI agents for various applications, such as customer support, virtual assistants, and interactive chatbots.

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