llmtestr 2025.12.21135641

A new package that helps developers integration-test AI and LLM applications by validating structured outputs. It takes a user's test scenario or prompt as input, sends it to an LLM, and uses pattern

LLM Test Helper (llmtestr)

llmtestr is a Python package designed to assist developers in integration-testing AI and Language Model applications by validating structured outputs. It provides a simple interface to send a prompt or test scenario to an LLM, then verifies that the response matches predefined patterns using pattern matching mechanisms. This helps ensure that your LLM outputs adhere to expected formats such as code snippets, JSON structures, or tagged responses, making it easier to catch formatting errors, regressions, or inconsistencies during development and testing.

Installation

Install the package via pip:

pip install llmtestr

Usage

Here's a basic example of how to use llmtestr in your Python code:

from llmtestr import llmtestr

response = llmtestr(user_input="Your test prompt here")
print(response)

Function Parameters

• user_input (str): The prompt or test scenario you want to evaluate.
• llm (Optional[BaseChatModel]): An optional langchain LLM instance to use. If not provided, llmtestr defaults to using ChatLLM7.
• api_key (Optional[str]): Your API key for LLM7. If not provided, it will attempt to fetch from the environment variable LLM7_API_KEY.

Underlying LLM

By default, llmtestr uses the ChatLLM7 class from langchain_llm7, which you can find on PyPI: langchain_llm7. You can also pass your custom LLM instance based on the langchain interface for different providers like OpenAI, Anthropic, Google, etc.

Examples:

Using OpenAI:

from langchain_openai import ChatOpenAI
from llmtestr import llmtestr

llm = ChatOpenAI()
response = llmtestr(user_input="Test prompt", llm=llm)

Using Anthropic:

from langchain_anthropic import ChatAnthropic
from llmtestr import llmtestr

llm = ChatAnthropic()
response = llmtestr(user_input="Test prompt", llm=llm)

Using Google Generative AI:

from langchain_google_genai import ChatGoogleGenerativeAI
from llmtestr import llmtestr

llm = ChatGoogleGenerativeAI()
response = llmtestr(user_input="Test prompt", llm=llm)

Rate Limits & API Keys

The default free tier for LLM7 provides sufficient rate limits for most development needs. To get higher limits, you can:

• Set the environment variable LLM7_API_KEY.
• Pass your API key directly:

response = llmtestr(user_input="Test prompt", api_key="your_api_key")

Register for a free API key at https://token.llm7.io/.

Support & Issues

For issues, bugs, or feature requests, please visit the GitHub repo issues page: https://github.com/chigwell/llmtestr/issues.

Author

Eugene Evstafev
Email: [e-mail]
GitHub: chigwell