GEPA vs PromptEval

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.

PromptEval is a web and API SaaS platform for evaluating and optimizing large-language-model prompts. It assigns a reproducible score from 0 to 100, with diagnostics covering clarity, specificity, structure and robustness, then identifies issues, warnings, strengths and targeted fixes. A token optimizer compresses prompts while preserving intent and reports the reduction percentage. Its versioned library keeps prompt history, score changes and diffs. The A/B Playground compares two prompts across as many as seven criteria using a user-provided API key. An official GitHub Action can fail pull requests after score drops, contradictions or production regressions. Pro and Team users can serve prompts by slug through an API endpoint, with changes taking effect in about 60 seconds. Plans include a free tier, Basic at $9 a month, Pro at $19 a month, and custom-priced Team.