{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"}],"enrichment":{"capability":"GEPA optimizes textual system components\u2014prompts, code, agent architectures, configurations\u2014using LLM-based reflection and Pareto-efficient evolutionary search to improve performance against any measurable metric.","skillfed_tags":["prompt-optimization","evolutionary-search","llm-reflection"],"use_cases":["Optimize system prompts for classification or reasoning tasks to improve accuracy without manual prompt engineering.","Evolve entire DSPy programs including signatures and control flow to discover better architectures automatically.","Improve RAG retrieval and generation by optimizing prompts across vector store backends like ChromaDB or Weaviate.","Discover cloud scheduling policies or other configuration parameters that beat expert heuristics on your workload.","Tune agent tool descriptions and system prompts for tool-using agents in LangChain or MCP environments.","Automatically learn and refine coding agent skills to improve task resolution rates on specialized domains."],"what_it_does":"GEPA is a framework for automatically improving textual system components by treating optimization as a search problem guided by LLM reflection. Rather than collapsing execution outcomes into a single scalar reward, it reads full execution traces\u2014error messages, profiling data, reasoning logs\u2014to diagnose why a candidate failed and propose targeted improvements. The system uses Pareto-efficient evolutionary search to evolve high-performing variants with minimal evaluations, supporting optimization of prompts, code, agent architectures, scheduling policies, and other text-based artifacts.\n\nThe framework integrates with major AI platforms including DSPy, MLflow, Comet ML Opik, and Pydantic, offering both a standalone API and adapters for specialized tasks like RAG optimization, MCP tool descriptions, and LangChain pipelines. It requires Python 3.10+ and access to LLM APIs for reflection; the base package has no runtime dependencies, making installation straightforward.","worth_installing":"Yes. GEPA is actively maintained, has no runtime dependencies, and offers a permissive MIT license. It solves a real problem\u2014systematic optimization of textual system components\u2014with a novel LLM-reflection approach that avoids the evaluation overhead of traditional RL methods. Integrations with DSPy, MLflow, and other major frameworks lower adoption friction. Primary constraint is the need for external LLM API access; evaluate whether your use case justifies the API costs."},"id":"gepa","links":{"html":"https://skillfed.io/packages/gepa","md":"https://skillfed.io/packages/gepa.md","pypi":"https://pypi.org/project/gepa/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-15","license_spdx":null,"license_treatment":"permissive","name":"gepa","python_support":"supports_current","summary":"A framework for optimizing textual system components (AI prompts, code snippets, etc.) using LLM-based reflection and Pareto-efficient evolutionary search."},"popularity":{"monthly_downloads":6628234,"position":1880,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.1.4"}