ConversationBufferMemory Core Functions

  • Conversation History Storage: Saves complete conversation records, including user inputs and system responses
  • Context Maintenance: Provides complete conversation context for subsequent conversation turns
  • Buffer Management: Saves the latest messages, with configurable maximum capacity limits

Application Scenarios

  • Chatbots: Maintain conversation context so the bot can understand and respond to multi-turn conversations
  • Customer Service Systems: Save complete conversation history between customers and agents
  • Diagnostic Systems: Record complete interaction processes of symptom descriptions and diagnostic suggestions
  • Teaching Systems: Save Q&A history between students and teaching systems

Code Example

from langchain.memory import ConversationBufferMemory
from langchain_core.runnables import RunnableLambda

memory = ConversationBufferMemory(return_messages=True)
memory.save_context({"input": "hi im wzk"}, {"output": "Hello Wzk!"})
# Subsequent conversations can access the previous context

Running Results

  • First time asking “What is my name?”: The model doesn’t know
  • After saving the memory and asking again: The model answers “Your name is ‘wzk’”

This demonstrates how ConversationBufferMemory enables persistent conversation context for LLM applications.