datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
agent-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.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.hermes-agent-reasoning-traces
Hermes Agent Reasoning Traces
Multi-turn tool-calling trajectories for training AI agents using the Hermes Agent harness. Each sample is a real agent conversation with step-by-step reasoning (<think> blocks) and actual tool execution results.
This dataset has two configs, one per source model:
Config
Model
Samples
kimi
Moonshot AI Kimi-K2.5
7,646
glm-5.1
ZhipuAI GLM-5.1-FP8
7,055
Loading
from datasets import load_dataset
# Kimi-K2.5 traces
ds =… See the full description on the dataset page: https://huggingface.co/datasets/lambda/hermes-agent-reasoning-traces.agent-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.hermes-agent-traces-filtered
Hermes Agent Reasoning Traces - Quality Filtered
A structurally filtered subset of lambda/hermes-agent-reasoning-traces, pruned from 7,646 to 3,679 rows using automated quality analysis targeting reasoning depth, structural integrity, and tool-call validity.
Why This Matters for Agent Training
Most agentic datasets teach models what tool to call but not how to reason about tool selection. The difference matters in production: an agent that dispatches tools without… See the full description on the dataset page: https://huggingface.co/datasets/DJLougen/hermes-agent-traces-filtered.swe-bench-lite-agent-traces-v14
AgentBRANE SWE-bench Lite Agent Traces v14
This release contains the 1,890 harness-native agent traces selected by the sealed SWE-bench Lite v14 publication record (1,379/1,890 resolved, 73.0%). It includes Claude Code, Codex, and Pi sessions across seven models and three replicates. No internal research notes are included.
Load the observation table:
from datasets import load_dataset
traces = load_dataset("melissapan/swe-bench-lite-agent-traces-v14", split="train")
Each row… See the full description on the dataset page: https://huggingface.co/datasets/melissapan/swe-bench-lite-agent-traces-v14.fable5-gpt5.5-opus4.7-mixed-agent-traces
Fable5 · GPT-5.5 · Opus-4.7 Mixed Agent Traces
A high-density post-training mixture for agentic reasoning, instruction following, code generation, function calling, and tool-use decision making.
This is the training-data release behind Qwen3.5-9B-Distill-Agent-Instruct, an Agent Instruct model distilled and post-trained from Qwen3.5-9B-Base. The title highlights three of the mixture's principal model-labelled trajectory families—Claude Fable5, GPT-5.5 Agent, and Claude Opus… See the full description on the dataset page: https://huggingface.co/datasets/lzy510016411/fable5-gpt5.5-opus4.7-mixed-agent-traces.open-agent-traces
Open Agent Traces
17,019 LLM-enriched agent trace events across 500 workflow runs in 10 enterprise domains and 3 workflow patterns.
Generated with ocelgen (pip install open-agent-traces) and validated against the OCEL 2.0 standard, PM4Py, and 5 semantic validation layers.
from datasets import load_dataset
ds = load_dataset("juliensimon/open-agent-traces", "incident-response")
for event in ds["train"]:
if event["run_id"] == "run-0000":
print(f"{event['event_type']:25s}… See the full description on the dataset page: https://huggingface.co/datasets/juliensimon/open-agent-traces.read-along-ai-agent-traces
Read-Along AI - Agent Traces
This dataset contains the raw agent traces and conversation logs from the development of Read-Along AI, a submission for the Hugging Face Build Small Hackathon.
Dataset Description
These .jsonl files represent the unedited, behind-the-scenes "agent traces" of the AI coding assistant orchestrating the build of this project.
Sharing these traces fulfills the requirements for the "Sharing is Caring" bonus badge, providing the community… See the full description on the dataset page: https://huggingface.co/datasets/kingkw1/read-along-ai-agent-traces.agent-traces
OpenAI-format Agent Traces SFT
A tokenizer-independent, text-only collection of 62,812 deduplicated agent conversations, normalized from publicly accessible Hugging Face agent-trace datasets. This repository is a derived collection, not an original authorship claim. All upstream authors are credited in REFERENCES.md, with pinned revisions, source license labels, retained dataset cards and source license metadata.
Contents
Split
Conversations
Train
62… See the full description on the dataset page: https://huggingface.co/datasets/beomi/agent-traces.agent-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/DiscoPosse/agent-llm-traces.pi-agent-traces-sft
pi-agent-traces-sft
Merged supervised-fine-tuning dataset of coding-agent session traces (the "pi"
harness format), converted to conversational format for Unsloth SFT of Qwen3-family
models.
Files
pi_sft_qwen3_24k.jsonl — recommended training file. Every session fits in
24,576 tokens (Qwen3 tokenizer). 5,717 sessions, max length 24,564 tokens.
pi_sft_qwen3.jsonl — full unfiltered set (6,929 sessions, up to ~632k tokens).
Sources (merged)… See the full description on the dataset page: https://huggingface.co/datasets/vojtavlas2/pi-agent-traces-sft.hermes-agent-reasoning-traces
Hermes Agent Reasoning Traces
Multi-turn tool-calling trajectories for training AI agents using the Hermes Agent harness. Each sample is a real agent conversation with step-by-step reasoning (<think> blocks) and actual tool execution results.
This dataset has two configs, one per source model:
Config
Model
Samples
kimi
Moonshot AI Kimi-K2.5
7,646
glm-5.1
ZhipuAI GLM-5.1-FP8
7,055
Loading
from datasets import load_dataset
# Kimi-K2.5 traces
ds =… See the full description on the dataset page: https://huggingface.co/datasets/insym/hermes-agent-reasoning-traces.soc-agent-traces-10k
SOC-Agent-Traces-10K
Multi-step SOC investigation agent traces in session-trace format. Each
record is a complete investigation session: an alert arrives, an analyst agent
gathers evidence through nine read-only tools, and closes with a structured
JSON triage report.
Instead of single-turn alert → answer pairs, every record captures the full
reasoning trajectory:
alert → get_surrounding_events → get_process_tree → lookup_attack
→ search_sigma → get_asset_context →… See the full description on the dataset page: https://huggingface.co/datasets/alirezaaminzadeh/soc-agent-traces-10k.soc-agent-traces-100k
SOC-Agent-Traces-100K
Multi-step SOC investigation agent traces in session-trace format. Each
record is a complete investigation session: an alert arrives, an analyst agent
gathers evidence through nine read-only tools, and closes with a structured
JSON triage report.
Instead of single-turn alert → answer pairs, every record captures the full
reasoning trajectory:
alert → get_surrounding_events → get_process_tree → lookup_attack
→ search_sigma → get_asset_context →… See the full description on the dataset page: https://huggingface.co/datasets/alirezaaminzadeh/soc-agent-traces-100k.agent-coding-traces-public
Description
Public agentic coding traces (Claude Code & Codex on SWE-bench-Pro / R2E-Gym), normalised to text/source/lang.
Derived dataset. Source material retains its original per-item licence (see source/repo columns); treat as other / mixed. Provided as-is.
Usage
from datasets import load_dataset
ds = load_dataset("PotatoHD/agent-coding-traces-public")
betterwright-agent-traces
BetterWright Agent Traces
Full, untruncated agent trajectories recorded inside the BetterWright harness (v2.8.7, runAgentTask — the same loop behind betterwright exec) while two open models drove a real BetterChromium browser against the live web. Every row is one complete run: the harness system prompt and tool schemas, every model turn with its full reasoning, every browser tool call (JavaScript/Playwright-style snippets executed by the harness), every tool result exactly as… See the full description on the dataset page: https://huggingface.co/datasets/ProCreations/betterwright-agent-traces.hermes-agent-traces-filtered
Hermes Agent Reasoning Traces - Quality Filtered
A structurally filtered subset of lambda/hermes-agent-reasoning-traces, pruned from 7,646 to 3,679 rows using automated quality analysis targeting reasoning depth, structural integrity, and tool-call validity.
Why This Matters for Agent Training
Most agentic datasets teach models what tool to call but not how to reason about tool selection. The difference matters in production: an agent that dispatches tools without… See the full description on the dataset page: https://huggingface.co/datasets/ansulev/hermes-agent-traces-filtered.agent_traces
pi-mono Unsloth SFT Dataset
Derived from badlogicgames/pi-mono.
This dataset converts pi coding-agent session traces into one supervised row per
assistant turn. Each row keeps the prior multi-turn agent state in the prompt and
uses only the next assistant message/action as the target.
Format:
{"instruction":"serialized system + user/tool/assistant context before this turn","output":"next assistant message or tool call"}
For Unsloth Studio, choose the Alpaca / instruction-output… See the full description on the dataset page: https://huggingface.co/datasets/valoomba/agent_traces.hermes-agent-reasoning-traces
Hermes Agent Reasoning Traces
Multi-turn tool-calling trajectories for training AI agents using the Hermes Agent harness. Each sample is a real agent conversation with step-by-step reasoning (<think> blocks) and actual tool execution results.
This dataset has two configs, one per source model:
Config
Model
Samples
kimi
Moonshot AI Kimi-K2.5
7,646
glm-5.1
ZhipuAI GLM-5.1-FP8
7,055
Loading
from datasets import load_dataset
# Kimi-K2.5 traces
ds =… See the full description on the dataset page: https://huggingface.co/datasets/Mattral/hermes-agent-reasoning-traces.hermes-agent-traces-chatml
Hermes Agent Traces — ChatML Format
A ready-to-train dataset of 18,487 multi-turn tool-calling conversations in ChatML messages format, combining Hermes Agent reasoning traces with NousResearch function-calling data.
Built for SFT training of tool-calling / agentic LLMs with TRL's SFTTrainer.
Quick Start
from datasets import load_dataset
from trl import SFTTrainer
dataset = load_dataset("sroecker/hermes-agent-traces-chatml", split="train")
trainer = SFTTrainer(… See the full description on the dataset page: https://huggingface.co/datasets/sroecker/hermes-agent-traces-chatml.hermes-agent-reasoning-traces
Hermes Agent Reasoning Traces
Multi-turn tool-calling trajectories for training AI agents using the Hermes Agent harness. Each sample is a real agent conversation with step-by-step reasoning (<think> blocks) and actual tool execution results.
This dataset has two configs, one per source model:
Config
Model
Samples
kimi
Moonshot AI Kimi-K2.5
7,646
glm-5.1
ZhipuAI GLM-5.1-FP8
7,055
Loading
from datasets import load_dataset
# Kimi-K2.5 traces
ds =… See the full description on the dataset page: https://huggingface.co/datasets/ThreeSixNine/hermes-agent-reasoning-traces.weave-agent-traces-2025-11-05This dataset is 200 megabytes (30mb gzip compressed) of agent trace data from the weave-agent project.
It consists of long context python code agent traces which demonstrate a series of ReAct blocks attempting to complete a task the agent is prompted with.
Some sample traces you can view on my website:
First Working Weave-Agent TraceAgent Trace: Weave Agent At The Edge Of Sanity Trying To Check Wikipedia CitationsAgent Trace: Weave Agent Attempts To Decrypt The Vigenere CipherAgent Trace: A… See the full description on the dataset page: https://huggingface.co/datasets/jdpressman/weave-agent-traces-2025-11-05.hermes-agent-reasoning-traces
Hermes Agent Reasoning Traces
Multi-turn tool-calling trajectories for training AI agents using the Hermes Agent harness. Each sample is a real agent conversation with step-by-step reasoning (<think> blocks) and actual tool execution results.
This dataset has two configs, one per source model:
Config
Model
Samples
kimi
Moonshot AI Kimi-K2.5
7,646
glm-5.1
ZhipuAI GLM-5.1-FP8
7,055
Loading
from datasets import load_dataset
# Kimi-K2.5 traces
ds =… See the full description on the dataset page: https://huggingface.co/datasets/Bas95/hermes-agent-reasoning-traces.hermes-agent-reasoning-traces
Hermes Agent Reasoning Traces
Structured fine-tuning dataset extracted from Hermes Agent execution logs and skill files.
Examples: 688
Skill examples: 688
Session examples: 0
Source: Hermes Agent (Nous Research)
Generated: 2026-08-16
Format
Each line is a JSON object with:
instruction: The user request or skill creation prompt
response: The agent's response or skill body
source: Origin (skill file or session ID)
category: Type (skill_creation or conversation)… See the full description on the dataset page: https://huggingface.co/datasets/zombierotten/hermes-agent-reasoning-traces.sft-coding-agent-traces
My AI Coding Helper Data
I use this data to teach an AI how to code. I use records of past coding work.
What is in this data
This data has 6,625 examples. It has records from:
Real coding work.
Chat logs about code.
My own work with an AI helper.
How I use this data
I use this data to train a model. I use a method called Supervised Fine-Tuning. The AI learns how to think and how to use tools. It learns this by reading the examples in this data.… See the full description on the dataset page: https://huggingface.co/datasets/focustiki/sft-coding-agent-traces.hermes-agent-reasoning-traces
Hermes Agent Reasoning Traces
Multi-turn tool-calling trajectories for training AI agents using the Hermes Agent harness. Each sample is a real agent conversation with step-by-step reasoning (<think> blocks) and actual tool execution results.
This dataset has two configs, one per source model:
Config
Model
Samples
kimi
Moonshot AI Kimi-K2.5
7,646
glm-5.1
ZhipuAI GLM-5.1-FP8
7,055
Loading
from datasets import load_dataset
# Kimi-K2.5 traces
ds =… See the full description on the dataset page: https://huggingface.co/datasets/dylan1982/hermes-agent-reasoning-traces.Simple-agent-traces
📱 Simple Agent Traces – Tiny Tool‑Calling Conversations for Small Models
Simple Agent Traces is a compact, hand‑picked dataset of 605 real‑world tool‑calling conversations, each carefully truncated to ≤8,192 tokens (using the SmolLM2‑360M tokenizer).It is purpose‑built for training and fine‑tuning tiny language models (≤500M) that must run on‑device – smartphones, edge devices, or any environment with strict memory and latency constraints.
🧹 No chain‑of‑thought, no fluff.Every… See the full description on the dataset page: https://huggingface.co/datasets/LiteMind/Simple-agent-traces.agent-omission-traces
Omission Detection — Cross-Family Agent Traces
120 tool-using agent traces produced by five open model families answering the
same law and medicine tasks, so that omission behavior can be compared across
model families on identical inputs. Each trace records the tool outputs the
orchestrator was given and the answer it wrote — the raw material for measuring how
much an agent silently omits.
These traces back the cross-family prevalence study in the Lacuna/Rubric line
(no family… See the full description on the dataset page: https://huggingface.co/datasets/Santhiyarajan/agent-omission-traces.hermes-agent-reasoning-traces
Hermes Agent Reasoning Traces
Multi-turn tool-calling trajectories for training AI agents using the Hermes Agent harness. Each sample is a real agent conversation with step-by-step reasoning (<think> blocks) and actual tool execution results.
This dataset has two configs, one per source model:
Config
Model
Samples
kimi
Moonshot AI Kimi-K2.5
7,646
glm-5.1
ZhipuAI GLM-5.1-FP8
7,055
Loading
from datasets import load_dataset
# Kimi-K2.5 traces
ds =… See the full description on the dataset page: https://huggingface.co/datasets/txchmechanicus/hermes-agent-reasoning-traces.