Open source alternatives to Guidance

Best for: Developers building applications that need reliable structured data extraction from LLMs without validation failures

Best for: Python developers seeking a straightforward way to produce structured outputs from LLM prompts while managing prompt versions.

Best for: Python developers who want to streamline LLM application development with built-in optimization.

Best for: Python developers who want to quickly add LLM capabilities to their code without learning a complex framework.

Best for: Teams who want LLM calls to feel like real typed functions

Best for: Teams who want to optimise their LLM pipelines like code, not edit prompts forever

Best for: Developers building production LLM applications that need runtime guardrails for safety, format, and reliability

Best for: Engineers tired of regexing JSON out of model output

Best for: Developers seeking precise programmatic control over LLM outputs and complex prompt logic

Best for: Developers and researchers guiding LLM behavior without modifying model weights