#141141st of 142 in Agent frameworks and SDKs

promptify

Python library for typed NLP tasks like NER and classification via LLMs

Stars
4.6k
License
Apache-2.0
Last commit
Mar 2026
Language
Python
Stars, last 29 weeks+90
22 Apr 2026: 4,558 stars29 Apr 2026: 4,564 stars6 May 2026: 4,568 stars13 May 2026: 4,574 stars20 May 2026: 4,580 stars27 May 2026: 4,585 stars3 Jun 2026: 4,588 stars10 Jun 2026: 4,590 stars17 Jun 2026: 4,592 stars24 Jun 2026: 4,593 stars1 Jul 2026: 4,594 stars8 Jul 2026: 4,598 stars15 Jul 2026: 4,600 stars22 Jul 2026: 4,603 stars29 Jul 2026: 4,606 stars5 Aug 2026: 4,609 stars12 Aug 2026: 4,612 stars19 Aug 2026: 4,616 stars26 Aug 2026: 4,620 stars2 Sept 2026: 4,624 stars9 Sept 2026: 4,625 stars16 Sept 2026: 4,626 stars23 Sept 2026: 4,628 stars30 Sept 2026: 4,635 stars7 Oct 2026: 4,637 stars14 Oct 2026: 4,638 stars21 Oct 2026: 4,641 stars28 Oct 2026: 4,642 stars4 Nov 2026: 4,646 stars11 Nov 2026: 4,648 stars

4,558 on 22 Apr 20264,648 on 11 Nov 2026

From GitHub's daily star counts.

Checked on GitHub.

Overview

Promptify is a Python library that wraps LLM calls into task classes (NER, Classify, QA, Summarize, relation extraction, SQL generation) that return Pydantic objects. It routes requests through LiteLLM, so changing the model string switches between OpenAI, Anthropic, Ollama and other providers. It also includes batch and async calls, an evaluation module (precision, recall, F1, ROUGE) and token cost tracking.

Who it is for: Python developers extracting structured data from text with LLMs

Strengths

  • Task classes return Pydantic objects instead of raw strings
  • LiteLLM backend: switching provider is a model string change, including ollama/
  • Built-in evaluation: precision, recall, F1, accuracy, exact match, ROUGE
  • Batch with max_concurrent, async acall(), and get_cost_summary() for token cost

Weaknesses

  • No GitHub release published; install is from PyPI or a git URL
  • Evaluation metrics need the separate promptify[eval] extra
  • No web UI, server or hosted API; use is in-process Python only
  • No benchmark results given; accuracy depends on the chosen LLM

What it needs

  • no GPU
  • Needs LiteLLM, Pydantic, LLM provider API key or local Ollama
  • Models: OpenAI, Anthropic, Google, Azure, Ollama

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