/**
 * List of supported Evaluation Frameworks supported in the `eval.yaml` file in benchmarks datasets.
 */
export const EVALUATION_FRAMEWORKS = {
	exgentic: {
		name: "exgentic",
		description:
			"Exgentic is an open evaluation framework for general-purpose AI agents across diverse domains and benchmarks.",
		url: "https://github.com/Exgentic/exgentic",
	},
	"inspect-ai": {
		name: "inspect-ai",
		description: "Inspect AI is an open-source framework for large language model evaluations.",
		url: "https://inspect.aisi.org.uk/",
	},
	"math-arena": {
		name: "math-arena",
		description: "MathArena is a platform for evaluation of LLMs on latest math competitions and olympiads.",
		url: "https://github.com/eth-sri/matharena",
	},
	mteb: {
		name: "mteb",
		description: "Multimodal toolbox for evaluating embeddings and retrieval systems.",
		url: "https://github.com/embeddings-benchmark/mteb",
	},
	"olmocr-bench": {
		name: "olmocr-bench",
		description: "olmOCR-Bench is a framework for evaluating document-level OCR of various tools.",
		url: "https://github.com/allenai/olmocr/tree/main/olmocr/bench",
	},
	harbor: {
		name: "harbor",
		description: "Harbor is a framework for evaluating and optimizing agents and language models.",
		url: "https://github.com/laude-institute/harbor",
	},
	ifstruct: {
		name: "ifstruct",
		description:
			"IFStruct is a benchmark for structured-output compliance: whether a model produces valid JSON/YAML that follows a requested schema, scored without constrained decoding.",
		url: "https://github.com/Liquid4All/ifstruct",
	},
	pier: {
		name: "pier",
		description:
			"Pier is a Harbor fork built for DeepSWE, with stronger support for CLI agents in no-internet tasks and more faithful, consistent agent trajectories.",
		url: "https://github.com/datacurve-ai/pier",
	},
	"redline-bench": {
		name: "redline-bench",
		description:
			"RedlineBench measures multi-turn contract redlining: agents produce tracked-change .docx edits that are graded against attorney-authored weighted rubrics by an LLM judge panel across five dimensions. Report: https://intelligence.crosby.ai/benchmark/",
		url: "https://github.com/crosbylegal/redline-bench",
	},
	archipelago: {
		name: "archipelago",
		description: "Archipelago is a system for running and evaluating AI agents against MCP applications.",
		url: "https://github.com/Mercor-Intelligence/archipelago",
	},
	benchflow: {
		name: "benchflow",
		description:
			"BenchFlow is an evaluation framework for AI agents on professional, skill-aware workflows. It powers SkillsBench and runs containerized agent trials with paired with-skills / without-skills configurations.",
		url: "https://github.com/benchflow-ai/benchflow",
	},
	"apex-evals": {
		name: "apex-evals",
		description: "APEX Evals is a benchmark suite and evaluation harness for evaluating large language models.",
		url: "https://github.com/Mercor-Intelligence/apex-evals",
	},
	"screenspot-pro": {
		name: "screenspot-pro",
		description:
			"ScreenSpot-Pro is a GUI grounding benchmark designed to evaluate how well AI agents can locate and identify UI elements across professional software applications in high-resolution screenshots, covering 1,585 annotated images from 26 professional tools.",
		url: "https://github.com/likaixin2000/ScreenSpot-Pro-GUI-Grounding",
	},
	"swe-bench": {
		name: "swe-bench",
		description: "SWE Bench is a framework for evaluating the performance of LLMs on software engineering tasks.",
		url: "https://github.com/swe-bench/swe-bench",
	},
	"swe-bench-pro": {
		name: "swe-bench-pro",
		description:
			"SWE-Bench Pro is a challenging benchmark evaluating LLMs/Agents on long-horizon software engineering tasks.",
		url: "https://github.com/scaleapi/SWE-bench_Pro-os",
	},
	"nemo-evaluator": {
		name: "nemo-evaluator",
		description:
			"NeMo Evaluator is an open-source platform for robust, reproducible, and scalable evaluation of Large Language Models across 100+ benchmarks.",
		url: "https://github.com/NVIDIA-NeMo/Evaluator",
	},
	"yc-bench": {
		name: "yc-bench",
		description:
			"YC Bench is a long-horizon deterministic benchmark for LLM agents. The agent plays CEO of an AI startup over a simulated 1–3 year run.",
		url: "https://github.com/collinear-ai/yc-bench",
	},
	"open-asr-leaderboard": {
		name: "open-asr-leaderboard",
		description: "The Open ASR Leaderboard ranks and evaluates speech recognition models.",
		url: "https://github.com/huggingface/open_asr_leaderboard",
	},
	mdpbench: {
		name: "mdpbench",
		description:
			"MDPBench is a benchmark for evaluating multilingual document parsing across digital, photographed, Latin, and non-Latin document subsets.",
		url: "https://github.com/Yuliang-Liu/MultimodalOCR",
	},
	parsebench: {
		name: "parsebench",
		description:
			"ParseBench is a benchmark for evaluating document parsing systems on real-world enterprise documents across tables, charts, content faithfulness, semantic formatting, and visual grounding.",
		url: "https://github.com/run-llama/ParseBench",
	},
	"video-mme-v2": {
		name: "video-mme-v2",
		description:
			"Video-MME-v2 is a benchmark for evaluating the next stage of video understanding capabilities of multimodal large language models.",
		url: "https://github.com/MME-Benchmarks/Video-MME-v2",
	},
	"claw-eval": {
		name: "claw-eval",
		description:
			"CLAW-Eval is an evaluation framework for assessing LLMs as autonomous agents across 300 human-verified tasks covering communication, finance, and productivity domains.",
		url: "https://github.com/claw-eval/claw-eval",
	},
	researchclawbench: {
		name: "researchclawbench",
		description:
			"ResearchClawBench is a benchmark for evaluating AI agents on end-to-end scientific research tasks, from reading data and related work to producing code, figures, and publication-style reports.",
		url: "https://github.com/InternScience/ResearchClawBench",
	},
	pbench: {
		name: "pbench",
		description:
			"PBench is a multi-level referring expression segmentation benchmark for evaluating vision-language perception across a structured hierarchy of skills.",
		url: "https://github.com/tiiuae/Falcon-Perception",
	},
	wildclawbench: {
		name: "wildclawbench",
		description:
			"WildClawBench is an in-the-wild benchmark for evaluating AI agents in the OpenClaw environment across 60 hand-built, end-to-end tasks spanning productivity, code intelligence, social interaction, search, creative synthesis, and safety domains.",
		url: "https://github.com/InternLM/WildClawBench",
	},
	wbench: {
		name: "wbench",
		description:
			"WBench is a comprehensive multi-turn benchmark for interactive video world model evaluation, assessing models across 5 dimensions (video quality, setting adherence, interaction adherence, consistency, physics compliance) and 22 metrics over 289 multi-turn interaction cases.",
		url: "https://github.com/meituan-longcat/WBench",
	},
	nanofold: {
		name: "nanofold",
		description:
			"nanoFold is a data-efficiency benchmark for protein structure prediction. Its goal is to evaluate models on scenarios with scarce data.",
		url: "https://github.com/ChrisHayduk/nanoFold-Competition",
	},
	mmmu: {
		name: "mmmu",
		description:
			"MMMU is a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning.",
		url: "https://mmmu-benchmark.github.io/",
	},
} as const;