Build a personalized Customer Support AI Agent using LangGraph for conversation flow and Mem0 for memory retention. This integration enables context-aware and efficient support experiences.
Overview
In this guide, we’ll create a Customer Support AI Agent that:
- Uses LangGraph to manage conversation flow
- Leverages Mem0 to store and retrieve relevant information from past interactions
- Provides personalized responses based on user history
Setup and Configuration
Install necessary libraries:
pip install langgraph langchain-openai mem0ai
Import required modules and set up configurations:
from typing import Annotated, TypedDict, List
from langgraph.graph import StateGraph, START
from langgraph.graph.message import add_messages
from langchain_openai import ChatOpenAI
from mem0 import MemoryClient
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
OPENAI_API_KEY = 'sk-xxx'
MEM0_API_KEY = 'your-mem0-key'
llm = ChatOpenAI(model="gpt-4", api_key=OPENAI_API_KEY)
mem0 = MemoryClient(api_key=MEM0_API_KEY)
Define State and Graph
Set up the conversation state and LangGraph structure:
class State(TypedDict):
messages: Annotated[List[HumanMessage | AIMessage], add_messages]
mem0_user_id: str
graph = StateGraph(State)
Create Chatbot Function
Define the core logic for the Customer Support AI Agent:
def chatbot(state: State):
messages = state["messages"]
user_id = state["mem0_user_id"]
memories = mem0.search(messages[-1].content, user_id=user_id)
context = "Relevant information from previous conversations:\n"
for memory in memories:
context += f"- {memory['memory']}\n"
system_message = SystemMessage(content=f"""You are a helpful customer support assistant. Use the provided context to personalize your responses and remember user preferences and past interactions.
{context}""")
full_messages = [system_message] + messages
response = llm.invoke(full_messages)
mem0.add(f"User: {messages[-1].content}\nAssistant: {response.content}", user_id=user_id)
return {"messages": [response]}
Set Up Graph Structure
Configure the LangGraph with appropriate nodes and edges:
graph.add_node("chatbot", chatbot)
graph.add_edge(START, "chatbot")
graph.add_edge("chatbot", "chatbot")
compiled_graph = graph.compile()
Create Conversation Runner
Implement a function to manage the conversation flow:
def run_conversation(user_input: str, mem0_user_id: str):
config = {"configurable": {"thread_id": mem0_user_id}}
state = {"messages": [HumanMessage(content=user_input)], "mem0_user_id": mem0_user_id}
for event in compiled_graph.stream(state, config):
for value in event.values():
if value.get("messages"):
print("Customer Support:", value["messages"][-1].content)
return
Main Interaction Loop
Set up the main program loop for user interaction:
if __name__ == "__main__":
print("Welcome to Customer Support! How can I assist you today?")
mem0_user_id = "customer_123"
while True:
user_input = input("You: ")
if user_input.lower() in ['quit', 'exit', 'bye']:
print("Customer Support: Thank you for contacting us. Have a great day!")
break
run_conversation(user_input, mem0_user_id)
Key Features
- Memory Integration: Uses Mem0 to store and retrieve relevant information from past interactions.
- Personalization: Provides context-aware responses based on user history.
- Flexible Architecture: LangGraph structure allows for easy expansion of the conversation flow.
- Continuous Learning: Each interaction is stored, improving future responses.
Conclusion
By integrating LangGraph with Mem0, you can build a personalized Customer Support AI Agent that can maintain context across interactions and provide personalized assistance.
- For more details on LangGraph, visit the LangChain documentation.
- For Mem0 documentation, refer to the Mem0 Platform.
- If you need further assistance, please feel free to reach out to us through following methods:
