LangChain
LangChain, the most widely adopted of these libraries, provides developers with powerful tools and patterns for managing complex prompts and conversational state.
Prerequisites
Sign up for an account at Featherless
Subscribe to a plan and get your API key from API Keys
Setup
First, let's import the required libraries and set up our API key.
pip install langchain-featherless-aiCreating a LangChain Chat Model
Create a ChatFeatherlessAi instance using the Featherless LangChain integration. Set the FEATHERLESS_API_KEY environment variable to your Featherless API key before running this example. The example selects deepseek-ai/DeepSeek-V4.1-Flash-1017 and translates a sentence from English to French.
import os
from langchain_featherless_ai import ChatFeatherlessAi
llm = ChatFeatherlessAi(
api_key=os.environ["FEATHERLESS_API_KEY"],
base_url="https://api.featherless.ai/v1",
model="deepseek-ai/DeepSeek-V4.1-Flash-1017",
)
messages = [
("system", "Translate the user sentence from English to French. Reply with only the translation."),
("human", "I love programming."),
]
ai_msg = llm.invoke(messages)
print(ai_msg.content)
We’ve created a simple conversation that asks the model to translate English to French. Now we can send our messages to the model and get the translation. The invoke() method handles the API call and returns the model’s response.
Resources
For more detailed information check out the langchain docs
Our langchain cookbooks