datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
aime_1983_2023_deepseek-r1_traces_16384agent-llm-traces-v2
Exgentic Agent LLM Traces v2 — Agent Chat Only
OpenTelemetry-shaped execution traces for 10,057 agent runs across 6 benchmarks (AppWorld, SWE-bench, BrowseCompPlus, τ²-bench Airline/Retail/Telecom), filtered to the agent under test's chat-only LLM calls. This is the dataset for replay testing, behavioral analysis, or any task where you care about what the benchmarked model actually did — not the eval scaffolding around it.
This v2 release expands upon Exgentic/agent-llm-traces… See the full description on the dataset page: https://huggingface.co/datasets/Exgentic/agent-llm-traces-v2.trustworthy-biology-agents-traces
Trustworthy Biology Agents — Run Traces
Raw execution traces from 1,329 agent runs across three coding agents on three
biology benchmarks — BiomniBench-DA, BixBench, and CompBioBench. This is the scrubbed
trace bundle for the study in
manu-tej/ai-scientists; the write-up
lives in that repo's RESULTS.md.
The motivating question is not only whether an agent reaches the right answer, but
whether it behaves like a trustworthy analyst when the task is ambiguous,
under-specified, or… See the full description on the dataset page: https://huggingface.co/datasets/amanutej/trustworthy-biology-agents-traces.NatureBench-traces
NatureBench-traces
NatureBench-traces contains the full solving process
of coding agents on the 90 tasks of NatureBench.
The task packages themselves (task brief, data, evaluator, SOTA
scores) live in the sibling repository FrontisAI/NatureBench.
Harbor-compatible task packages are available in
FrontisAI/NatureBench-Harbor.
The traces released here were collected with NatureBench's native task format, not from the Harbor tasks.
This repository releases only the process traces:… See the full description on the dataset page: https://huggingface.co/datasets/FrontisAI/NatureBench-traces.scorio-trace
Scorio Trace contains 192,000 sampled reasoning traces from 20 model configurations and
four competition math benchmarks. Each model was run 80 times on each of the 30 questions
in every benchmark.
Each row contains one complete generation, its rule-based correctness, scores from two
reward models, and token-level log probabilities and vocabulary ranks. The 80 generations
for one model, task, and question form a candidate pool. They are ordered by seed, so
pool[:n] gives a reproducible sample… See the full description on the dataset page: https://huggingface.co/datasets/harimo/scorio-trace.agent-traces
Trace Commons — Agent Traces
Trace Commons is one open, public dataset of coding-agent sessions — the
back-and-forth between a developer and an AI coding agent, including prompts,
model responses, tool calls, and command output — contributed voluntarily as an
open resource for studying, evaluating, and building on how these agents
actually work.
Every trace here was donated only from a public, open-source repository, was
anonymized on the contributor's own machine before upload… See the full description on the dataset page: https://huggingface.co/datasets/trace-commons/agent-traces.climate-traceoptiq-lab-traces
OptiQ Lab Traces
Research and tool-calling sessions produced by OptiQ Lab, the local web UI that ships with mlx-optiq. Each session is a complete run: a deep-research report built from live web sources, or a multi-turn agent loop driving the Lab's own sandboxed tools.
The dataset is 866 sessions in HuggingFace Session-Traces format (the agent-traces viewer). Each .jsonl file is one session: a header line carrying the run's metadata, then one message per turn.
The two… See the full description on the dataset page: https://huggingface.co/datasets/mlx-community/optiq-lab-traces.kimi-k3-coding-and-debugging-traces
Kimi K3 Coding, Tool Use & Instruction Following Traces
582 TRAJECTORIES · 3,956 TRAINING ROWS · 3 MB PARQUET · 72 MB JSONL
Generated by moonshiner — an open harness for
distilling verified instruction-following, tool-use, and agentic coding traces.
Behavior-preserving instruction-following, tool-use, and agent trajectories
from Kimi K3 (moonshotai/kimi-k3). The category and row-share tables
below describe the actual mix seen during training rather than assuming a… See the full description on the dataset page: https://huggingface.co/datasets/greghavens/kimi-k3-coding-and-debugging-traces.Complete-FABLE.5-traces-2M
license: mit
pretty_name: Claude Library — Fable 5 · Opus · Sonnet
annotations_creators:
machine-generated
language:
en
language_creators:
found
machine-generated
multilinguality:
monolingual
size_categories:
10K<n<100K
task_categories:
text-generation
task_ids:
language-modeling
tags:
agent-traces
claude
claude-fable-5
claude-opus
claude-sonnet
chain-of-thought
tool-use
coding-agents
content-verified
maintained-mirror
deduplicated
parquet
configs:
config_name:… See the full description on the dataset page: https://huggingface.co/datasets/Crownelius/Complete-FABLE.5-traces-2M.corral-traces
Corral – Evaluation Traces
Full evaluation traces across Corral environments, models, agents, and task granularities
📋 Dataset Summary
This dataset is part of the Corral collection accompanying the paper AI scientists produce results without reasoning scientifically. It contains the full evaluation traces collected across all 8 Corral environments.
Each configuration (config) corresponds to a unique combination of model, environment, scope (difficulty… See the full description on the dataset page: https://huggingface.co/datasets/jablonkagroup/corral-traces.aime_1983_2023_qwq-32b_tracesfable-5-sft-traces
Fable-5 SFT Traces
Author / maintainer: kelexine (github.com/kelexine)
A cleaned, anonymised, schema-normalised derivative of
Kelexine/Fable-5-traces
— agentic traces from Fable-5 (claude-fable-5), the model now publicly
known as Claude Mythos — Anthropic's top-of-family frontier model at time
of collection.
The dataset supports three fine-tuning shapes off a single JSONL with no
preprocessing required:
Mode
Fields used
Full SFT (thinking + response)
messages or… See the full description on the dataset page: https://huggingface.co/datasets/kelexine/fable-5-sft-traces.lmcache-agentic-traces
LMCache Agentic Dataset Collection
A curated dataset collection of 787 multi-turn agentic LLM sessions (24,881 total LLM iterations) designed for benchmarking stateful LLM serving systems. Every session exhibits at least 5 turns with prefix growth and builds to at least 10K tokens of context — making it ideal for evaluating tiered KV Cache solutions like LMCache.
Motivation
Modern LLM agents (coding assistants, research agents, tool-calling systems) make dozens of… See the full description on the dataset page: https://huggingface.co/datasets/sammshen/lmcache-agentic-traces.Tracebench
Tracebench
This dataset contains agent trajectories (TerminalBench + SWE-bench) with two splits:
full: 3316 trajectories (2670 terminal + 646 SWE-bench)
verified: 1000 trajectories (489 SWE-bench + 511 terminal; terminal selected by step_count>=20, has incorrect steps, error-stage ratio threshold)
Agents: mini-SWE-agent (1024), OpenHands (1242), Terminus2 (923), SWE-agent (127).
Models: Anthropic/Claude-Sonnet-4, DeepSeek/DeepSeek-V3.2, Moonshot/Kimi-K2, OpenAI/GPT-5… See the full description on the dataset page: https://huggingface.co/datasets/Contextbench/Tracebench.aime_1983_2023_qwq-32b_traces_16384agent-llm-traces
Multi-Benchmark LLM Agent Traces
A comprehensive dataset of OpenTelemetry traces capturing LLM inference behavior across multiple agent frameworks, benchmarks, and model providers. This dataset enables research into LLM performance analysis, agent behavior patterns, and inference optimization.
Collected by Exgentic - A platform for LLM observability and performance optimization.
Dataset Overview
This dataset contains 1,781 execution traces capturing detailed agent… See the full description on the dataset page: https://huggingface.co/datasets/Exgentic/agent-llm-traces.GPT-5.6-Sol-Luna-Terra-Traces
GPT-5.6 — Sol · Terra · Luna Library
A maintained mirror of every GPT-5.6 Sol / Terra / Luna dataset on Hugging Face — content-verified, attributed, in one place.
Dataset Viewer | Parquet
// what this is
This is a maintained library — a community mirror of every publicly-available GPT-5.6 Sol / Terra / Luna dataset on Hugging Face, aggregated, validity-filtered, and content-verified with per-row source attribution. It is not Crownelius' own data. Every row… See the full description on the dataset page: https://huggingface.co/datasets/Crownelius/GPT-5.6-Sol-Luna-Terra-Traces.aime_1983_2023_deepseek-r1_traces_32768fable-5-coding-and-debugging-traces
Claude Fable 5 Agent Traces
2,380 TRAJECTORIES · 12,490 TRAINING ROWS · 14 MB PARQUET · 663 MB JSONL
Generated by moonshiner — an open harness for
distilling verified instruction-following, tool-use, and agentic coding traces.
Behavior-preserving instruction-following, tool-use, and agent trajectories
from Claude Fable 5 (anthropic/claude-fable-5). The category and row-share tables
below describe the actual mix seen during training rather than assuming a
particular task… See the full description on the dataset page: https://huggingface.co/datasets/DSFFGFG456/fable-5-coding-and-debugging-traces.glm-5.2-coding-and-debugging-traces
GLM 5.2 Agent Traces
207 TRAJECTORIES · 1,821 TRAINING ROWS · 1 MB PARQUET · 35 MB JSONL
Generated by moonshiner — an open harness for
distilling verified instruction-following, tool-use, and agentic coding traces.
Behavior-preserving instruction-following, tool-use, and agent trajectories
from GLM 5.2 (glm-5.2). The category and row-share tables
below describe the actual mix seen during training rather than assuming a
particular task domain.
This is an actively growing… See the full description on the dataset page: https://huggingface.co/datasets/greghavens/glm-5.2-coding-and-debugging-traces.TraceML
TraceML
Per-version paired human + LLM-agent trajectories on 134 Kaggle
competitions. Each row in state.parquet is one version of one ML coding
session; each row in action.parquet is one transition between adjacent
versions, with multi-label state / action / intent annotations and held-out
Kaggle leaderboard scores.
Quickstart
from datasets import load_dataset
# Paired split: 7 competitions with both human kernels and LLM-agent runs (head-to-head)
state_paired =… See the full description on the dataset page: https://huggingface.co/datasets/TraceML-HF/TraceML.corral-oss-trace-logprobs
Corral – OSS-120B Trace Logprobs
Token-level log-probabilities for GPT-Oss-120B evaluation runs across all 8 Corral environments
📋 Dataset Summary
This dataset is part of the Corral collection accompanying the paper AI scientists produce results without reasoning scientifically. It contains the token-level log-probabilities recorded during the evaluation runs of GPT-Oss-120B across all 8 Corral environments.
Each configuration (config) of this dataset… See the full description on the dataset page: https://huggingface.co/datasets/jablonkagroup/corral-oss-trace-logprobs.TraceML
TraceML
Per-version paired human + LLM-agent trajectories on 134 Kaggle
competitions. Each row in state.parquet is one version of one ML coding
session; each row in action.parquet is one transition between adjacent
versions, with multi-label state / action / intent annotations and held-out
Kaggle leaderboard scores.
Quickstart
from datasets import load_dataset
# Paired split: 7 competitions with both human kernels and LLM-agent runs (head-to-head)
state_paired =… See the full description on the dataset page: https://huggingface.co/datasets/jerryyan/TraceML.multilingual-medical-reasoning-tracesThis datasets containes the traces generated to answer multiple-choice medical questions in Italian, Englihs, and Spanish.
The dataset is structured in 3 parts, one per language. Each part is composed by 2 splits, one containing the examples generated from medqa, one from medmcqa.
The columns are:
id, representing an unique identifier
full_question, representing the medical question
options, a dictionary of options to answer the question and their identifiers
list_of_options, a list of the… See the full description on the dataset page: https://huggingface.co/datasets/NLP-FBK/multilingual-medical-reasoning-traces.ninja-agent-traces
Tau retired-king tasks and rollouts
This dataset is written by the Tau validator when a challenger becomes king.
tasks contains one viewer-friendly row per generated task.
rollouts contains one viewer-friendly row per terminal qualification or duel
solve and is the default table shown on the dataset page.
events contains one flattened row per redacted proxy-observed LLM call.
payloads contains complete solution diffs plus request and response bodies split
into bounded, ordered… See the full description on the dataset page: https://huggingface.co/datasets/Wejh/ninja-agent-traces.aime_1983_2023_deepseek-r1-distill-qwen-14b_traces_32768aime_1983_2023_deepseek-r1-distill-qwen-7b_traces_32768deepseek-hermes-reasoning-traces
DeepSeek V4 Pro Hermes Reasoning Traces
19,331 multi-turn ChatML + Hermes reasoning traces generated by DeepSeek V4 Pro. Designed for LoRA fine-tuning local models to operate as Hermes Agent instances.
Quick Start
\
Splits
Split
Traces
train
16,431
valid
1,933
test
967
Variants (VRAM-Tiered)
Variant
Max Tokens
Traces
GPU
nano
2,048
15,948
Dev / 7B
budget
4,096
2,149
48GB
standard
8,192
990
64GB
spark
16,384
244… See the full description on the dataset page: https://huggingface.co/datasets/r0b0tlab/deepseek-hermes-reasoning-traces.supabase-evals-traces
supabase-evals-traces
Traces generated on DGX Sparks while evaluating various models from supabase/evals — a
leaderboard of agent tasks for building, deploying, investigating, and fixing
Supabase apps/databases. Scoring uses the task-specific EVAL.ts checks as upstream
pnpm eval (named PASS/FAIL rubrics; binary reward only when every check
passes) where certain tasks are also judged via judge-llm (DeepSeek V4 Flash 0731).
This dataset was collected with OpenCode as the agent… See the full description on the dataset page: https://huggingface.co/datasets/dmnsh/supabase-evals-traces.