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Fructose

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Fructose is a python package to create a dependable, strongly-typed interface around an LLM call. ![GitHub Repo stars](https://img.shields.io/github/stars/bananaml/fructose?style=s

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Fructose

Added 1 June 2026

Overview

Fructose is a Python package that uses decorators and type hints to create a strongly-typed interface for LLM calls. It wraps any function with a typed signature, converting it into a structured LLM invocation that returns predictable outputs.

Best for

Best for
Python developers who want reliable, typed interfaces for LLM calls in their applications

Use cases

  • Defining typed LLM functions for data extraction and classification
  • Integrating LLM calls into existing Python codebases with type safety
  • Building reliable LLM pipelines that produce structured outputs

Notes

Fructose is a Python package that uses decorators and type hints to create a strongly-typed interface for LLM calls. It wraps any function with a typed signature, converting it into a structured LLM invocation that returns predictable outputs.

750 stars on GitHub. Last updated 2024-04-17. Licensed Apache-2.0.

Use cases

  • Defining typed LLM functions for data extraction and classification
  • Integrating LLM calls into existing Python codebases with type safety
  • Building reliable LLM pipelines that produce structured outputs

Pros

  • Strong typing ensures predictable and verifiable outputs
  • Simple decorator-based API minimizes boilerplate
  • Works seamlessly with Python type checkers for early error detection

Cons

  • Limited to Python; no direct support for other languages
  • May restrict flexibility for complex or dynamic prompting patterns
  • Depends on the LLM’s ability to correctly follow structured output instructions

Indexed from awesome-langchain and enriched against its public facts.

Pros

  • Strong typing ensures predictable and verifiable outputs
  • Simple decorator-based API minimizes boilerplate
  • Works seamlessly with Python type checkers for early error detection

Cons

  • Limited to Python; no direct support for other languages
  • May restrict flexibility for complex or dynamic prompting patterns
  • Depends on the LLM's ability to correctly follow structured output instructions

Pairs with

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