# Conversation Buffer

This notebook shows how to use `ConversationBufferMemory`. This memory allows for storing messages and then extracts the messages in a variable.

We can first extract it as a string.

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


```python
memory = ConversationBufferMemory()
memory.save_context({"input": "hi"}, {"output": "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 (this is useful if you are using this with a chat model).


```python
memory = ConversationBufferMemory(return_messages=True)
memory.save_context({"input": "hi"}, {"output": "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>
