time-travel-math-parser 2025.12.20185553

The package lets users input a textual description of a claim about paradox‑free time travel backed by mathematics. It then uses an LLM together with llmatch‑messages to parse the text, extract the co

time-travel-math-parser

Time‑Travel Math Parser transforms free‑form textual claims about paradox‑free time‑travel into a clean, structured JSON format that downstream tools can consume reliably.

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Highlights

• LLM‑powered extraction using the llmatch-messages library to enforce a strict output format.
• Works out‑of‑the‑box with the free tier of LLM7 thanks to a sensible default implementation (ChatLLM7 from langchain_llm7).
• Easy to swap LLMs – you can pass any langchain compatible BaseChatModel (OpenAI, Anthropic, Google, etc.) to replace the default.

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Installation

pip install time-travel-math-parser

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Quick Start

from time_travel_math_parser import time_travel_math_parser

user_input = """
The paper proves that any universe with a closed timelike curve will eventually resolve paradoxes by introducing a feedback loop that nullifies causal inconsistencies. 
Key assumptions: (1) spacetime is differentiable, (2) causality violation is limited to the region within the wormhole throat, and (3) quantum fluctuations are suppressed.
"""

# Using the default ChatLLM7
output = time_travel_math_parser(user_input)

print(output)

output will be a list of extracted JSON objects, for example:

[
  {
    "claim": "any universe with a closed timelike curve will eventually resolve paradoxes ...",
    "theorem_summary": "...feedback loop that nullifies causal inconsistencies",
    "assumptions": [
      "spacetime is differentiable",
      "causality violation is limited to the region within the wormhole throat",
      "quantum fluctuations are suppressed"
    ],
    "paradox_handling": "...",
    "confidence_score": 0.92
  }
]

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Using Your Own LLM

time_travel_math_parser accepts an optional llm argument that should be an instance of langchain BaseChatModel.
Below are examples for the three most common LLM back‑ends.

OpenAI

from langchain_openai import ChatOpenAI
from time_travel_math_parser import time_travel_math_parser

llm = ChatOpenAI()          # configure API key through environment variable or kwargs
result = time_travel_math_parser(user_input, llm=llm)

Anthropic

from langchain_anthropic import ChatAnthropic
from time_travel_math_parser import time_travel_math_parser

llm = ChatAnthropic()
result = time_travel_math_parser(user_input, llm=llm)

Google Gemini

from langchain_google_genai import ChatGoogleGenerativeAI
from time_travel_math_parser import time_travel_math_parser

llm = ChatGoogleGenerativeAI()
result = time_travel_math_parser(user_input, llm=llm)

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Parameters

Parameter	Type	Description
user_input	str	The free‑form text describing the time‑travel claim to parse.
llm	Optional[BaseChatModel]	A LangChain chat model instance. If omitted, ChatLLM7 (free tier) is used.
api_key	Optional[str]	LLM7 API key. If not supplied, the function looks for LLM7_API_KEY in the environment; if still missing it falls back to the default public key.

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Rate Limits

LLM7’s free tier provides ample throughput for most development and testing workloads.
If you require higher limits, supply a personal API key:

export LLM7_API_KEY=YOUR_KEY

or:

time_travel_math_parser(user_input, api_key="YOUR_KEY")

Free keys are available from https://token.llm7.io/.

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Contributing & Issues

If you encounter bugs or have feature requests, please open an issue on GitHub: https://github.com/chigwell/time-travel-math-parser/issues

Feel free to fork and submit pull requests!

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License

MIT © Eugene Evstafev – [e-mail]

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Author

Eugene Evstafev (chigwell)
https://github.com/chigwell
LinkedIn: https://linkedin.com/in/chigwell