gameconcept-analyzer 2025.12.21143849

A new package that processes user-submitted text descriptions of local multiplayer party game concepts and returns structured feedback or suggestions. It helps game developers or enthusiasts by analyz

Game Concept Analyzer

Game Concept Analyzer is a Python package designed to assist game developers and enthusiasts in analyzing and refining local multiplayer party game ideas. By processing user-submitted text descriptions of game mechanics, player interactions, and party dynamics, it provides structured feedback and suggestions to enhance game designs. The tool ensures the output is formatted for easy integration into design documents or brainstorming sessions, streamlining the creative process.

Installation

You can install the package using pip:

pip install gameconcept_analyzer

Usage

Here's a basic example of how to use the package:

from gameconcept_analyzer import gameconcept_analyzer

# Example user input describing a game concept
user_input = "A fast-paced party game where players compete in mini-challenges sitting around a table."

# Call the analyzer function
response = gameconcept_analyzer(user_input)

# Print the feedback or suggestions
print(response)

Customizing the Language Model

The package uses the ChatLLM7 from langchain_llm7 by default, which you can configure or replace to suit your preferences. You can pass your own LLM instance to the gameconcept_analyzer function, such as models from OpenAI, Anthropic, or Google.

Example: Using OpenAI

from langchain_openai import ChatOpenAI
from gameconcept_analyzer import gameconcept_analyzer

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

Example: Using Anthropic

from langchain_anthropic import ChatAnthropic
from gameconcept_analyzer import gameconcept_analyzer

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

Example: Using Google Generative AI

from langchain_google_genai import ChatGoogleGenerativeAI
from gameconcept_analyzer import gameconcept_analyzer

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

API Key Configuration

For the default ChatLLM7, you can provide your API key via the api_key parameter or set the environment variable LLM7_API_KEY. For higher rate limits, obtain a free API key at https://token.llm7.io/.

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

Parameters

• user_input (str): The descriptive text of your game concept.
• llm (Optional[BaseChatModel]): An instance of a language model compatible with langchain. Defaults to ChatLLM7.
• api_key (Optional[str]): API key for ChatLLM7. If not provided, the environment variable or default will be used.

Support and Issues

If you encounter any problems or have suggestions, please open an issue on the GitHub repository: https://github.com/chigwell/gameconcept-analyzer

Author

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