We will walk through the simplest form of memory: "buffer" memory, which just involves keeping a buffer of all prior messages. We will show how to use the modular utility functions here, then show how it can be used in a chain (both returning a string as well as a list of messages).

## ChatMessageHistory
One of the core utility classes underpinning most (if not all) memory modules is the `ChatMessageHistory` class. This is a super lightweight wrapper which exposes convenience methods for saving Human messages, AI messages, and then fetching them all.

You may want to use this class directly if you are managing memory outside of a chain.




```python
from langchain.memory import ChatMessageHistory

history = ChatMessageHistory()

history.add_user_message("hi!")

history.add_ai_message("whats up?")
```


```python
history.messages
```

<CodeOutputBlock lang="python">

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

</CodeOutputBlock>

## ConversationBufferMemory

We now show how to use this simple concept in a chain. We first showcase `ConversationBufferMemory` which is just a wrapper around ChatMessageHistory that extracts the messages in a variable.

We can first extract it as a string.


```python
from langchain.memory import ConversationBufferMemory
```


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


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

<CodeOutputBlock lang="python">

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

</CodeOutputBlock>

We can also get the history as a list of messages


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


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

<CodeOutputBlock lang="python">

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

</CodeOutputBlock>

## Using in a chain
Finally, let's take a look at using this in a chain (setting `verbose=True` so we can see the prompt).


```python
from langchain.llms import OpenAI
from langchain.chains import ConversationChain


llm = OpenAI(temperature=0)
conversation = ConversationChain(
    llm=llm,
    verbose=True,
    memory=ConversationBufferMemory()
)
```


```python
conversation.predict(input="Hi there!")
```

<CodeOutputBlock lang="python">

```


    > Entering new ConversationChain chain...
    Prompt after formatting:
    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

    Current conversation:

    Human: Hi there!
    AI:

    > Finished chain.





    " Hi there! It's nice to meet you. How can I help you today?"
```

</CodeOutputBlock>


```python
conversation.predict(input="I'm doing well! Just having a conversation with an AI.")
```

<CodeOutputBlock lang="python">

```


    > Entering new ConversationChain chain...
    Prompt after formatting:
    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

    Current conversation:
    Human: Hi there!
    AI:  Hi there! It's nice to meet you. How can I help you today?
    Human: I'm doing well! Just having a conversation with an AI.
    AI:

    > Finished chain.





    " That's great! It's always nice to have a conversation with someone new. What would you like to talk about?"
```

</CodeOutputBlock>


```python
conversation.predict(input="Tell me about yourself.")
```

<CodeOutputBlock lang="python">

```


    > Entering new ConversationChain chain...
    Prompt after formatting:
    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

    Current conversation:
    Human: Hi there!
    AI:  Hi there! It's nice to meet you. How can I help you today?
    Human: I'm doing well! Just having a conversation with an AI.
    AI:  That's great! It's always nice to have a conversation with someone new. What would you like to talk about?
    Human: Tell me about yourself.
    AI:

    > Finished chain.





    " Sure! I'm an AI created to help people with their everyday tasks. I'm programmed to understand natural language and provide helpful information. I'm also constantly learning and updating my knowledge base so I can provide more accurate and helpful answers."
```

</CodeOutputBlock>

## Saving Message History

You may often have to save messages, and then load them to use again. This can be done easily by first converting the messages to normal python dictionaries, saving those (as json or something) and then loading those. Here is an example of doing that.


```python
import json

from langchain.memory import ChatMessageHistory
from langchain.schema import messages_from_dict, messages_to_dict

history = ChatMessageHistory()

history.add_user_message("hi!")

history.add_ai_message("whats up?")
```


```python
dicts = messages_to_dict(history.messages)
```


```python
dicts
```

<CodeOutputBlock lang="python">

```
    [{'type': 'human', 'data': {'content': 'hi!', 'additional_kwargs': {}}},
     {'type': 'ai', 'data': {'content': 'whats up?', 'additional_kwargs': {}}}]
```

</CodeOutputBlock>


```python
new_messages = messages_from_dict(dicts)
```


```python
new_messages
```

<CodeOutputBlock lang="python">

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

</CodeOutputBlock>

And that's it for the getting started! There are plenty of different types of memory, check out our examples to see them all
