perfbenchify 2025.12.21145744

A new package that transforms unstructured text descriptions of software performance benchmarks into standardized, comparable metrics. Users input text descriptions of performance tests, and the packa

perfbenchify

A lightweight package that converts unstructured text descriptions of software performance benchmarks into standardized, comparable metrics. The library uses the llmatch-messages framework to extract key performance indicators—like speed improvements, latency reductions, and throughput increases—from natural‑language benchmark descriptions. Once extracted, the results are returned in a consistent format, enabling developers to easily compare different software frameworks or libraries and make informed performance optimization decisions.

📦 Installation

pip install perfbenchify

🚀 Quick Start

from perfbenchify import perfbenchify

user_input = """ Compared to version 2.1, the new algorithm processes fifty thousand transactions per second while reducing the average latency from 12ms to 7ms. The speed improvement is a 15% increase. """

results = perfbenchify(user_input)
print(results)
# Example output:
# ['Speed improvement: 15%']

✨ Parameters

Parameter	Type	Description
user_input	str	The free‑form text containing benchmark information
llm	Optional[BaseChatModel]	A LangChain LLM instance to use. If omitted, the package defaults to ChatLLM7 from langchain_llm7.
api_key	Optional[str]	API key for ChatLLM7. If omitted, the package checks the environment variable LLM7_API_KEY; otherwise it falls back to a placeholder "None" (which triggers the free tier of LLM7).

🛠️ Using a Custom LLM

perfbenchify can work with any LabChain-compliant LLM. Below are examples for three popular providers.

OpenAI

from langchain_openai import ChatOpenAI
from perfbenchify import perfbenchify

llm = ChatOpenAI()
# Uses your OpenAI API key from environment
response = perfbenchify(user_input, llm=llm)
print(response)

Anthropic

from langchain_anthropic import ChatAnthropic
from perfbenchify import perfbenchify

llm = ChatAnthropic()
# Uses your Anthropic API key from environment
response = perfbenchify(user_input, llm=llm)
print(response)

Google Generative AI

from langchain_google_genai import ChatGoogleGenerativeAI
from perfbenchify import perfbenchify

llm = ChatGoogleGenerativeAI()
# Uses your Google API key from environment
response = perfbenchify(user_input, llm=llm)
print(response)

🔐 API Key and Rate Limits

• Free tier of LLM7 is sufficient for most use cases.
• To increase rate limits, supply your own key:

export LLM7_API_KEY="your_key_here"

or pass it directly:

response = perfbenchify(user_input, api_key="your_key_here")

Free keys can be obtained by registering at https://token.llm7.io/.

💡 Features

• LLM-agnostic: Works with any LangChain LLM (OpenAI, Anthropic, Google, etc.) or the default ChatLLM7.
• Pattern-based validation: Uses a compiled regex to guarantee the extracted metrics match a predefined format.
• Automatic retries: Handles unreliable LLM responses by retrying until the output matches the expected pattern.
• Easy integration: Returned data is a simple Python list of strings, ready for downstream processing or visualisation.

📄 License

MIT License – see LICENSE file for details.

📬 Support & Issues

Have questions or encountered a bug? Report them on the GitHub issue tracker: https://github.com/chigwell/perfbenchify

👤 Author

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