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AI Agents with LangChain & Autonomous Workflows · Lesson

Conversation Buffer Memory

Implement basic conversational memory to store and retrieve past interactions within an agent's context.

Conversation Buffer Memory is a free AI Agents with LangChain & Autonomous Workflows lesson on CoddyKit — lesson 2 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the AI Agents with LangChain & Autonomous Workflows learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Why Agents Need Memory

Imagine having a conversation where you instantly forget everything said just a moment ago. That's what happens to an AI agent without memory!

Agents often need to recall past interactions to maintain context. Without memory, each turn is a fresh start, leading to repetitive or nonsensical conversations.

What is Buffer Memory?

ConversationBufferMemory is LangChain's simplest memory type. It stores the raw, unsummarized conversation history directly.

Think of it like keeping a full transcript of everything said, in the exact order it was said. It's straightforward and easy to use for basic conversational recall.

Initializing Buffer Memory

To use ConversationBufferMemory, you simply import it and create an instance. It's often integrated into a Chain, but we can explore it standalone first.

Let's initialize a memory object and peek at its initial (empty) state:

from langchain.memory import ConversationBufferMemory

# Initialize the memory
memory = ConversationBufferMemory()

print("Memory initialized!")

# You can see its content (it will be empty at first)
print(memory.load_memory_variables({}))

Saving Conversation Context

The save_context method is how you add new user inputs and AI outputs to the memory. It takes two dictionaries: one for inputs and one for outputs.

This method is crucial because it's how the memory 'learns' from the ongoing conversation and builds its history.

Code Demo: Saving Context

Let's add a simple interaction to our memory object and see how it stores the conversation turn.

Notice how the 'input' and 'output' are paired and stored as 'Human' and 'AI' messages.

from langchain.memory import ConversationBufferMemory

memory = ConversationBufferMemory()

# Simulate a user input and an AI response
memory.save_context(
    {"input": "Hi there!"},
    {"output": "Hello! How can I help you?"}
)

print("Memory after one turn:")
print(memory.load_memory_variables({}))

Retrieving Stored History

To access the stored conversation history, you use the load_memory_variables method. It returns a dictionary containing the memory content.

By default, the conversation history is stored under the key 'history'. This key is what you'll typically pass into your LLM's prompt template.

Customizing the Memory Key

By default, ConversationBufferMemory stores history under the key 'history'. However, your prompt template might expect a different variable name, like 'chat_history'.

You can change this using the memory_key parameter during initialization:

from langchain.memory import ConversationBufferMemory

# Initialize with a custom memory_key
memory = ConversationBufferMemory(memory_key="my_chat_history")

memory.save_context(
    {"input": "What's up?"},
    {"output": "Not much, just coding!"}
)

print("Memory with custom key:")
print(memory.load_memory_variables({}))

Integrating with an LLM Chain

Here's how you integrate ConversationBufferMemory into an LLMChain. The memory_key in the ConversationBufferMemory must match the variable name in your PromptTemplate (e.g., chat_history).

Remember to replace 'YOUR_API_KEY' with your actual OpenAI key if you want to run this example fully.

from langchain.memory import ConversationBufferMemory
from langchain.chains import LLMChain
from langchain_openai import OpenAI
from langchain.prompts import PromptTemplate
import os

# Set your OpenAI API key (replace with your actual key or env var)
# os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"

# Initialize LLM (using a dummy if API key not set)
llm = OpenAI(temperature=0) # You might need to set openai_api_key=os.environ.get("OPENAI_API_KEY")

# Initialize memory with a key matching the prompt template
memory = ConversationBufferMemory(memory_key="chat_history")

# Define a prompt template that expects 'chat_history'
template = """You are a friendly chatbot.
{chat_history}
Human: {human_input}
AI:"""

prompt = PromptTemplate(
    input_variables=["chat_history", "human_input"],
    template=template
)

# Create an LLMChain with the memory
conversation = LLMChain(
    llm=llm,
    prompt=prompt,
    verbose=False,
    memory=memory
)

# First turn
print("Human: What is your capital?")
response1 = conversation.predict(human_input="What is your capital?")
print(f"AI: {response1}")

# Second turn - the AI should remember the context
print("\nHuman: And what about its population?")
response2 = conversation.predict(human_input="And what about its population?")
print(f"AI: {response2}")

When to Use Buffer Memory

ConversationBufferMemory is excellent for short, direct conversations where you need exact recall of recent turns.

  • Pros: Simple to implement, stores full, unedited conversation details.
  • Cons: Can quickly exceed the LLM's context window for longer chats, no summarization or filtering, leading to higher token usage and costs.

Quick Check

Let's test your understanding of ConversationBufferMemory.

Buffer Memory Summary

We've successfully explored ConversationBufferMemory, LangChain's simplest way to give agents a basic form of memory:

  • It stores raw conversation history.
  • You use save_context to add turns and load_memory_variables to retrieve them.
  • It's ideal for short, direct conversations but can quickly hit LLM context limits with longer chats.
  • Remember to set memory_key if your prompt expects a different variable name for history.

Next, we'll dive into more advanced memory solutions that handle longer conversations better!

Frequently asked questions

Is the “Conversation Buffer Memory” lesson free?

Yes — the full text of “Conversation Buffer Memory” is free to read here on the web, and the AI Agents with LangChain & Autonomous Workflows course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the AI Agents with LangChain & Autonomous Workflows course, upgrade to CoddyKit PRO.

What will I learn in “Conversation Buffer Memory”?

Implement basic conversational memory to store and retrieve past interactions within an agent's context. You practise AI Agents with LangChain & Autonomous Workflows with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start AI Agents with LangChain & Autonomous Workflows?

No prior experience is required. AI Agents with LangChain & Autonomous Workflows on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Conversation Buffer Memory” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this AI Agents with LangChain & Autonomous Workflows lesson?

Yes. Every AI Agents with LangChain & Autonomous Workflows lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

  1. Agent Memory Concepts
  2. Conversation Buffer Memory
  3. Advanced Memory Solutions
  4. Entity & Summary Memory Strategies
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