Promptify and pydantic-prompter
These are complementary tools: Promptify provides the broader prompt engineering framework with versioning and multi-model support, while pydantic-prompter specializes in the structured output extraction layer by guaranteeing Pydantic schema validation that Promptify would need to implement separately.
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 pydantic-prompter
helmanofer/pydantic-prompter
A lightweight tool that lets you simply build prompts and get Pydantic objects as outputs
Supports multiple LLM providers (OpenAI, Cohere, Bedrock) with pluggable backends and uses Jinja2-templated YAML prompts defined via Python decorators. Automatically validates and parses LLM responses into Pydantic models, eliminating manual JSON parsing while providing built-in logging and debugging utilities for prompt introspection.
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