{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/11"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"}],"enrichment":{"capability":"Verifiers provides environments and evaluation harnesses for training and assessing large language models using reinforcement learning, with integration into the Prime ecosystem.","skillfed_tags":["llm-training","reinforcement-learning","agent-evaluation"],"use_cases":["Train agents using reinforcement learning (GRPO, agentic RL) with multi-turn environments and tool-use capabilities.","Evaluate LLM outputs against verification criteria in a structured harness integrated with the Prime platform.","Build custom evaluation environments for agent reasoning and decision-making tasks.","Integrate LLM training pipelines with the Prime CLI and Environments Hub for collaborative model development.","Benchmark multi-turn agent behavior in controlled, reproducible environments."],"what_it_does":"Verifiers is a library for creating and managing training and evaluation environments for large language models, built around reinforcement learning workflows. It is designed to work within the Prime ecosystem\u2014specifically the Environments Hub, the prime-rl training framework, and the Hosted Training platform\u2014but can also be used as a standalone harness for multi-turn agent interactions, tool-use training, and LLM verification tasks.\n\nThe package brings together environment management, evaluation harnesses, and agent integration under a single interface. It depends on a substantial set of libraries for async I/O (aiohttp, httpx), LLM provider clients (anthropic, openai), data handling (datasets, numpy, pydantic), and distributed compute (pyzmq, uvloop). The library is actively maintained, recently released, and targets Python 3.11\u20133.13.","worth_installing":"Yes, if you are training or evaluating LLMs with reinforcement learning and plan to use the Prime ecosystem. The package is actively maintained, has no known vulnerabilities, and integrates tightly with prime-rl and Hosted Training. If you need a standalone environment harness without Prime integration, verify that the 26 dependencies and ecosystem lock-in align with your workflow first."},"id":"verifiers","links":{"html":"https://skillfed.io/packages/verifiers","md":"https://skillfed.io/packages/verifiers.md","pypi":"https://pypi.org/project/verifiers/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-07","license_spdx":"MIT","license_treatment":"permissive","name":"verifiers","python_support":"supports_current","summary":"Verifiers: Environments for LLM Reinforcement Learning"},"popularity":{"monthly_downloads":554110,"position":6037,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.3.0"}