Promptify and Prompture
These are complements: Promptify focuses on prompt engineering and versioning workflows, while Prompture specializes in structured output validation and comparative model testing—capabilities that would naturally be used together in a production LLM pipeline.
About Promptify
promptslab/Promptify
Prompt Engineering | Prompt Versioning | Use GPT or other prompt based models to get structured output. Join our discord for Prompt-Engineering, LLMs and other latest research
Provides task-specific NLP classes (NER, classification, QA, relation extraction) that return type-safe Pydantic models instead of raw text, eliminating parsing brittleness. Abstracts away LLM provider differences through LiteLLM, allowing seamless switching between OpenAI, Anthropic, Ollama, and 100+ other backends with a single model string. Includes built-in evaluation metrics (precision, recall, F1) and cost tracking, plus batch/async processing for production workloads.
About Prompture
jhd3197/Prompture
Prompture is an API-first library for requesting structured JSON output from LLMs (or any structure), validating it against a schema, and running comparative tests between models.
Built on Pydantic models with native schema validation, Prompture supports 12 LLM providers (OpenAI, Claude, Groq, Ollama, etc.) through a unified `provider/model` routing system. It includes TOON input conversion for 45-60% token savings, per-field stepwise extraction with smart type coercion, caching backends (memory/SQLite/Redis), and automatic JSON repair via secondary LLM passes. The tool use layer simulates function calling for providers without native support, while batch testing enables side-by-side model comparison with integrated usage tracking and cost calculation.
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