challengeturn 2025.12.21183136

A new package that analyzes user input text to determine if it expresses a challenge or a statement of inability, and returns a structured, encouraging response. It uses pattern matching to identify p

Challeneturn

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A package that generates encouraging and strategic responses to user input, identifying challenges and offering tips, reframing, or positive reinforcement.

Installation

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pip install challengeturn

Usage

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from challengeturn import challengeturn

response = challengeturn(user_input="I can't do this")

Documentation

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Parameters

• user_input: the user's input text (str)
• llm: an instance of BaseChatModel (langchain.core.language_models.BaseChatModel), defaulting to ChatLLM7 from langchain_llm7.
• api_key: the API key for LLM7, defaulting to the LLM7_API_KEY environment variable or "None" if not provided.

Passing your own LLM

You can safely pass your own LLM instance if desired. For example, to use the OpenAI LLM:

from langchain_openai import ChatOpenAI
from challengeturn import challengeturn

llm = ChatOpenAI()
response = challengeturn(user_input, llm=llm)

Or the Anthropic LLM:

from langchain_anthropic import ChatAnthropic
from challengeturn import challengeturn

llm = ChatAnthropic()
response = challengeturn(user_input, llm=llm)

Or the Google Generative AI LLM:

from langchain_google_genai import ChatGoogleGenerativeAI
from challengeturn import challengeturn

llm = ChatGoogleGenerativeAI()
response = challengeturn(user_input, llm=llm)

LLM7 API Key

The default rate limits for LLM7 free tier are sufficient for most use cases of this package. If you require higher rate limits, you can pass your own api_key via environment variable LLM7_API_KEY or via the challengeturn function:

response = challengeturn(user_input, api_key="your_api_key")

You can get a free API key by registering at https://token.llm7.io/.

GitHub

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https://github.com/chigwell/challengeturn

Author

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Eugene Evstafev [e-mail]