langchain/docs/snippets/modules/memory/get_started.mdx

Let's take a look at how to use `ConversationBufferMemory` in chains.
`ConversationBufferMemory` is an extremely simple form of memory that just keeps a list of chat messages in a buffer
and passes those into the prompt template.

```python
from langchain.memory import ConversationBufferMemory

memory = ConversationBufferMemory()
memory.chat_memory.add_user_message("hi!")
memory.chat_memory.add_ai_message("whats up?")
```

When using memory in a chain, there are a few key concepts to understand.
Note that here we cover general concepts that are useful for most types of memory.
Each individual memory type may very well have its own parameters and concepts that are necessary to understand.

### What variables get returned from memory
Before going into the chain, various variables are read from memory.
These have specific names which need to align with the variables the chain expects.
You can see what these variables are by calling `memory.load_memory_variables({})`.
Note that the empty dictionary that we pass in is just a placeholder for real variables.
If the memory type you are using is dependent upon the input variables, you may need to pass some in.

```python
memory.load_memory_variables({})
```

<CodeOutputBlock lang="python">

```
    {'history': "Human: hi!\nAI: whats up?"}
```

</CodeOutputBlock>

In this case, you can see that `load_memory_variables` returns a single key, `history`.
This means that your chain (and likely your prompt) should expect an input named `history`.
You can usually control this variable through parameters on the memory class.
For example, if you want the memory variables to be returned in the key `chat_history` you can do:

```python
memory = ConversationBufferMemory(memory_key="chat_history")
memory.chat_memory.add_user_message("hi!")
memory.chat_memory.add_ai_message("whats up?")
```
<CodeOutputBlock lang="python">

```
    {'chat_history': "Human: hi!\nAI: whats up?"}
```

</CodeOutputBlock>

The parameter name to control these keys may vary per memory type, but it's important to understand that (1) this is controllable, and (2) how to control it.

### Whether memory is a string or a list of messages

One of the most common types of memory involves returning a list of chat messages.
These can either be returned as a single string, all concatenated together (useful when they will be passed into LLMs)
or a list of ChatMessages (useful when passed into ChatModels).

By default, they are returned as a single string.
In order to return as a list of messages, you can set `return_messages=True`

```python
memory = ConversationBufferMemory(return_messages=True)
memory.chat_memory.add_user_message("hi!")
memory.chat_memory.add_ai_message("whats up?")
```
<CodeOutputBlock lang="python">

```
    {'history': [HumanMessage(content='hi!', additional_kwargs={}, example=False),
  AIMessage(content='whats up?', additional_kwargs={}, example=False)]}
```

</CodeOutputBlock>

### What keys are saved to memory

Often times chains take in or return multiple input/output keys.
In these cases, how can we know which keys we want to save to the chat message history?
This is generally controllable by `input_key` and `output_key` parameters on the memory types.
These default to `None` - and if there is only one input/output key it is known to just use that.
However, if there are multiple input/output keys then you MUST specify the name of which one to use.

### End to end example

Finally, let's take a look at using this in a chain.
We'll use an `LLMChain`, and show working with both an LLM and a ChatModel.

#### Using an LLM


```python
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory


llm = OpenAI(temperature=0)
# Notice that "chat_history" is present in the prompt template
template = """You are a nice chatbot having a conversation with a human.

Previous conversation:
{chat_history}

New human question: {question}
Response:"""
prompt = PromptTemplate.from_template(template)
# Notice that we need to align the `memory_key`
memory = ConversationBufferMemory(memory_key="chat_history")
conversation = LLMChain(
    llm=llm,
    prompt=prompt,
    verbose=True,
    memory=memory
)
```


```python
# Notice that we just pass in the `question` variables - `chat_history` gets populated by memory
conversation({"question": "hi"})
```


#### Using a ChatModel


```python
from langchain.chat_models import ChatOpenAI
from langchain.prompts import (
    ChatPromptTemplate,
    MessagesPlaceholder,
    SystemMessagePromptTemplate,
    HumanMessagePromptTemplate,
)
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory


llm = ChatOpenAI()
prompt = ChatPromptTemplate(
    messages=[
        SystemMessagePromptTemplate.from_template(
            "You are a nice chatbot having a conversation with a human."
        ),
        # The `variable_name` here is what must align with memory
        MessagesPlaceholder(variable_name="chat_history"),
        HumanMessagePromptTemplate.from_template("{question}")
    ]
)
# Notice that we `return_messages=True` to fit into the MessagesPlaceholder
# Notice that `"chat_history"` aligns with the MessagesPlaceholder name.
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
conversation = LLMChain(
    llm=llm,
    prompt=prompt,
    verbose=True,
    memory=memory
)
```


```python
# Notice that we just pass in the `question` variables - `chat_history` gets populated by memory
conversation({"question": "hi"})
```