How do you use ChatModels in LangChain?

ChatModels in LangChain are LLM wrappers that communicate using a message-based format. Instead of passing a raw string, you pass a list of typed messages: SystemMessage, HumanMessage, and AIMessage. This maps directly to the roles used by OpenAI, Anthropic, and similar APIs.

from langchain_openai import ChatOpenAI
from langchain_core.messages import SystemMessage, HumanMessage

llm = ChatOpenAI(
    model="gpt-4o",
    temperature=0.7,
    max_tokens=512,
)

messages = [
    SystemMessage(content="You are a helpful Python tutor."),
    HumanMessage(content="Explain list comprehensions in Python."),
]
response = llm.invoke(messages)
print(response.content)   # AIMessage with text response
print(response.usage_metadata)  # token counts

ChatModels also support streaming so you can print tokens as they arrive:

for chunk in llm.stream(messages):
    print(chunk.content, end="", flush=True)

Other providers follow the same API: ChatAnthropic, ChatGoogleGenerativeAI, ChatMistralAI. Switching providers requires only changing the import and class name; the rest of the chain remains identical.

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