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GEPA vs OpenPrompt

GEPA

gepa-ai.github.io

OpenPrompt

github.com

Side by side

FigureGEPAOpenPrompt
Price fromFreeFree
Free planYesYes
Free trial——
Open source——
Windows—Yes
macOS—Yes
Linux—Yes
APIYes—
Self-hostedYesYes
Plans published11
Spec score · AI Prompt Generators8.6 (#4)8.1 (#8)
InstallationThe project’s documented install command is `pip install gepa`.The README provides pip installation with `pip install openprompt` and instructions to install from the GitHub source.
LicenseThe GitHub repository lists the project under the MIT license.The repository identifies its license as Apache-2.0.
PurposeGEPA is a framework for optimizing prompts, code, agent architectures, configurations, and other text parameters against evaluation metrics.OpenPrompt is an open-source framework for building prompt-learning pipelines for downstream NLP tasks.

Plans

GEPA

  • GEPAFree

GEPA pricing →

OpenPrompt

  • OpenPromptFree

OpenPrompt pricing →

In their own words

GEPA

GEPA is a free, open-source Python package for optimizing prompts, code, configurations, agent architectures, policies, and other text-based artifacts. Its optimize_anything API connects to any system through an evaluator you write, so DSPy or another framework is not required. GEPA examines execution traces and evaluator feedback, uses language-model reflection to diagnose problems and propose targeted mutations, then selects candidates with Pareto-aware criteria. It can optimize API-only GPT, Claude, and Gemini models without model-weight access. Human-readable traces explain changes and support agent debugging. Built-in adapters cover Default, Confidence, DSPy Full Program, Generic RAG, MCP, TerminalBench, AnyMaths, and LangChain, with integrations including MLflow, Comet ML Opik, Pydantic AI, and cookbook projects. You can limit metric calls and set several stopping conditions. GEPA works through API or self-hosted deployment, installs with pip, and uses the MIT license.

Visit gepa-ai.github.io

OpenPrompt

OpenPrompt is a free, open-source framework for building prompt-learning pipelines for downstream natural language processing tasks. It brings methods for prompt templating, verbalizing, and optimization into one framework, while allowing users to devise and test their own methods. It can load pretrained language models from Hugging Face Transformers. A PromptModel combines such a model with one or more templates and verbalizers for training and inference. Documented processors cover text classification, entity typing, relation classification, language inference, and conditional generation. Template options include manual, prefix, P-tuning, PTR, and mixed approaches; verbalizer choices include one-to-one, manual, automatic, knowledgeable, PTR, generation, and soft forms. The project provides scripts for downloading benchmark datasets and installation instructions for pip or its GitHub source. Its listed tested requirements are Python 3.8+ and PyTorch 1.8.1+. OpenPrompt is licensed under Apache-2.0 and supports Linux, macOS, Windows, and self-hosted use. The README cautions that some documentation may no longer reflect major project changes.

Visit github.com