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Open Agent Leaderboard Results
Open Agent Leaderboard Results is the Hugging Face dataset behind the Open Agent Leaderboard, with tabular evaluation results for general-purpose AI agents across multiple benchmark and model combinations.
The dataset card describes detailed evaluation results for general-purpose AI agents across diverse real-world benchmarks, links the leaderboard Space, Exgentic website, Exgentic GitHub framework, and arXiv paper, and exposes fields for scores, completion, errors, action counts, and run costs. Use this as a first read, not a recommendation. Open the original project before trusting details like terms, limits, privacy, cost, setup, or safety.
What it is
A results dataset for an agent leaderboard
The dataset is the inspectable result layer for the Open Agent Leaderboard, with rows that connect agents, models, benchmarks, scores, completion behavior, errors, action counts, and cost fields.
Why readers may notice it
More than a single rank number
Agent leaderboard results can look simple until costs, unfinished sessions, benchmark mix, model pairings, and failure patterns are visible. This dataset gives readers the table behind those comparisons.
Availability
Dataset, leaderboard, framework, and paper links
The official materials include the Hugging Face dataset, leaderboard Space, Exgentic website, Exgentic evaluation framework on GitHub, and the General Agent Evaluation arXiv paper.
Why it matters
Why readers may notice it
Agent comparisons are easy to overread when only the top rank is visible. A results dataset gives readers a better way to inspect what was measured, which benchmarks were included, how often runs finished, and what the reported costs looked like.
What readers may want to know
Where it fits
This belongs in the benchmark and dataset layer rather than the agent-framework layer. It is most useful for readers comparing evaluation methodology, agent-model pairings, and benchmark coverage, not for readers looking for a deployable assistant.
Reporting note
What the source materials list
The dataset card lists 150 rows, parquet format, benchmark and leaderboard tags, and fields such as average score, benchmark score, completed sessions, successful sessions, unfinished sessions, invalid action counts, total agent cost, total benchmark cost, and total run cost. The linked Exgentic materials describe benchmark coverage including AppWorld, BrowseCompPlus, SWE-bench, and Tau Bench 2 domains.
Before using
What readers may want to review
The linked paper, leaderboard FAQ, and Exgentic framework notes before treating any ranking as settled.
Which agent, model, benchmark, subset, and session-count fields apply to the comparison being made.
Whether cost, unfinished sessions, invalid actions, or error rates matter more than the headline score for the intended use case.
Model-version drift, benchmark sampling, nondeterminism, and methodology changes when comparing results over time.
Reader fit
Who may find it relevant
Readers comparing AI agent systems across multiple benchmarks.
Builders who want benchmark result data rather than only a leaderboard screenshot.
Researchers and toolmakers checking cost, completion, and failure signals across agent-model combinations.
Less relevant for readers who only want a ready-to-use agent app or a model checkpoint.
Editorial note
Why it is included here
Open Agent Leaderboard Results is useful as a source table for agent-evaluation literacy: it helps readers look past a simple rank and inspect the scores, costs, completion behavior, and benchmark mix behind the leaderboard.
Source links
Original materials
Reader note
Before relying on this entry
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