Compared to other popular Python LLM libraries, chatlas focuses on making the barrier to entry as low as possible. It also makes certain design decisions that make common tasks less tedious and generally help you be more productive and prototype faster.

A few good examples of this are:

1. The `Chat` class retains conversation history for you. 
2. Streaming output "just works" out of the box -- no extra code needed.
3. `chatlas` handles the details of tool calling for you -- just provide a function with a docstring + type hints. You'll even get smart default streaming output of tool requests/results for free.

To drive these points home, let's walk through a few examples:

### Conversation history

Take, for example, a simple multi-turn conversation:

```python
from chatlas import ChatOpenAI

chat = ChatOpenAI(
    model="gpt-5.4",
    system_prompt="You are a terse assistant.",
)

chat.chat("What is the capital of the moon?")
chat.chat("Are you sure?")
```

Implementing the same functionality with LangChain requires something much more complex:


```python
from langchain_core.chat_history import InMemoryChatMessageHistory
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_openai import ChatOpenAI

# The underlying chat model. It doesn't manage any state, so we need to wrap it.
model = ChatOpenAI(model="gpt-5.4-mini")

# This is how you provide a system message in Langchain. Surprisingly
# complicated, isn't it?
prompt = ChatPromptTemplate.from_messages(
    [
        SystemMessage("You are a terse assistant."),
        MessagesPlaceholder(variable_name="messages"),
    ]
)

# Wrap the model and prompt up with some history.
history = InMemoryChatMessageHistory()
client = RunnableWithMessageHistory(prompt | model, lambda: history)

# We're ready to chat with the model now. For this example we'll make a blocking
# call, but there are ways to do async, streaming, and async streaming as well.
response = client.invoke("What is the capital of the moon?")
print(response.content)

# The input of invoke() can be a message object as well, or a list of messages.
response2 = client.invoke(HumanMessage("Are you sure?"))
print(response2.content)
```

Other frameworks like Pydantic AI and llm do a better 


It also:

  * Designs for the developer experience, with a focus on rich streaming output at the console, in notebooks, and in web apps.
