animtoreal 2025.12.21143241

This new package enables users to input a description or details about an animated character and receive a structured summary or step-by-step plan to bring that character into the real world, such as

animtoreal

animtoreal is a lightweight Python package that turns a textual description of an animated character into a structured, step‑by‑step plan for bringing that character into the real world.
It extracts key information such as props, costumes, interactive experiences and other production details using a large language model, without requiring any media files.

Why use animtoreal?

• Quickly generate a production‑ready spec from a creative brief.
• Leverage any LLM – the package ships with a default free‑tier LLM (ChatLLM7) but you can plug in OpenAI, Anthropic, Google Gemini, or any LangChain‑compatible model.
• No heavy dependencies or UI required – just a single function call.

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📦 Installation

pip install animtoreal

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🚀 Quick Start

from animtoreal import animtoreal

user_input = """
I want to create a small floating silver robot that can interact with children.
It should have smooth metal panels, a small LED face that displays simple emotions,
and a lightweight adjustable arm that can point at objects. The robot must be
battery powered, easy to clean, and have a friendly safety char.
"""

# Using the built‑in default LLM (ChatLLM7)
response = animtoreal(user_input)
print(response)

response will be a list of strings, each representing a structured element of the production plan.

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📚 Function Signature

animtoreal(
    user_input: str,
    api_key: Optional[str] = None,
    llm: Optional[BaseChatModel] = None
) -> List[str]

Parameter	Type	Description
user_input	str	Text description of the character.
llm	Optional[BaseChatModel]	A LangChain ChatModel instance. If omitted, the default ChatLLM7 is used.
api_key	Optional[str]	API key for LLM7. If omitted, the package will look for LLM7_API_KEY in the environment, and fall back to "None" (for the free tier).

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🔗 Using Your Own LLM

animtoreal is agnostic to the underlying LLM.
Below are quick examples with popular providers.

OpenAI

from langchain_openai import ChatOpenAI
from animtoreal import animtoreal

llm = ChatOpenAI()  # defaults to your OPENAI_API_KEY env var
response = animtoreal(user_input, llm=llm)

Anthropic

from langchain_anthropic import ChatAnthropic
from animtoreal import animtoreal

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

Google Gemini

from langchain_google_genai import ChatGoogleGenerativeAI
from animtoreal import animtoreal

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

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🔒 LLM7 API Key & Rate Limits

• The default free tier of LLM7 is usually sufficient for most small‑to‑medium projects.

• If you need higher limits, provide your key via an environment variable:

  export LLM7_API_KEY="your_real_api_key"
  

  or pass it directly:

  response = animtoreal(user_input, api_key="your_real_api_key")
  

• Obtain a free key at: https://token.llm7.io/

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🤝 Contributing & Issues

• GitHub Issues: https://github.com/chigwell/animtoreal/issues

Feel free to open an issue if you encounter bugs or have feature requests.

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📄 Licensing

This project is open source under the MIT License. (Adapt as appropriate.)

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👤 Maintainer

• Eugene Evstafev
  [e-mail]
  GitHub: @chigwell

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Happy creating! 🎨🦾