datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
real-pi-coding-agent-traces-sessions
Real Pi Coding Agent Traces Sessions
An aggregated dataset of real human–AI coding agent sessions, collected from 21 independently published Hugging Face datasets and hand-filtered to exclude synthetic or AI-generated content.
Every session is an unedited (but redacted) trace of a real person using pi — an open-source AI coding agent harness — to build, debug, and ship real open-source software. Real prompts, real tool calls, real errors, real backtracking.
Why this… See the full description on the dataset page: https://huggingface.co/datasets/MaxDevv/real-pi-coding-agent-traces-sessions.jobseek-agent-traces
Jobseek Agent Traces
Claude Code agent session traces from jobseek — a job posting monitor for company career pages.
Each trace captures a complete agent workflow session: company discovery, board configuration, monitor/scraper selection, and quality validation. These are raw session transcripts, not tabular data — use the trace viewer to explore them.
Structure
traces/
{company-slug}/
{date}.jsonl # One trace per session (header + records)
Each .jsonl… See the full description on the dataset page: https://huggingface.co/datasets/viktor-shcherb/jobseek-agent-traces.hal_tracesagent-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.traceshermes-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-reasoning-tracesinstrumental-choices-agent-traces
Instrumental Choices Agent Traces
This dataset contains the 1,680 agent trajectories reported in Instrumental Choices: Measuring the Propensity of LLM Agents to Pursue Instrumental Behaviors. It covers ten models, seven terminal-based tasks, eight variants, and three repetitions per task-variant-model combination.
Paper: arXiv:2605.06490
Project website: instrumentalchoices.com
Contents
inspect_logs/: 210 sanitised native Inspect .eval logs. Open locally with… See the full description on the dataset page: https://huggingface.co/datasets/aisa-group/instrumental-choices-agent-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.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.mcphunt-agent-traces
MCPHunt Agent Traces
Agent execution traces from the MCPHunt evaluation framework, measuring
cross-boundary data propagation in multi-server MCP agents.
Contents
main/ — 3,615 traces from 5 models across 147 tasks and 7 environment
variants (risky_v1/v2/v3, benign, hard_neg_v1/v2/v3). One JSON file per model.
mitigation/ — 2,706 traces from the prompt-mitigation study (M0--M3
levels) across 3 models.
live_guard_defense/ — 387 DeepSeek-V4-Flash traces from the… See the full description on the dataset page: https://huggingface.co/datasets/lihaonan0716/mcphunt-agent-traces.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.swe-rebench-v2-glm-5.1-pi-agent-successful-traces
Agentic coding trajectories of GLM-5.1
We collected 7,777 successful multi-turn trajectories by running
GLM-5.1 with the
PI agent on
nebius/SWE-rebench-V2.
Configuration
Each SWE-rebench-V2 task is an issue of a public repository. An agent has to
implement a fix that is then validated by unit tests.
The model was run on 3,837 issues from Python repositories, with 4 rollouts
per issue. The task list is available as the train split of… See the full description on the dataset page: https://huggingface.co/datasets/whitecircle/swe-rebench-v2-glm-5.1-pi-agent-successful-traces.mimir-agent-traces
MimirAether Agent Traces
7,124 unique real agent sessions · 35,000+ tool calls from a multi-agent system in daily production — four cooperating agents (orchestrator, executor, red-team reviewer, verifier) running real workloads since May 2026.
What makes this corpus different: it is not single-agent coding transcripts. It captures inter-agent collaboration in production — task dispatch, cross-audits, failure & recovery (real incidents: gateway freezes, API outages, deadlocks —… See the full description on the dataset page: https://huggingface.co/datasets/kelikelibababian/mimir-agent-traces.conda-forge-agent-tracescoding_agent_tracesWe release coding agent traces using Claude Code for
Opus
ISL, OSL, ISL_new counts
GPT-oss-120B
ISL, OSL, ISL_new counts and their raw texts
For Opus, only the locally saved files from the harness were used for analysis.
Coding agents take multiple turns to carry out a task from the input prompt. To analyze the token distribution, two models were selected: Anthropic's Opus and OpenAI's gpt-oss-120B. The input sequence length (ISL), output sequence length (OSL) and the uncached, new input… See the full description on the dataset page: https://huggingface.co/datasets/netpreme/coding_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.nemotron-gym-agent-calendar-qwen3.5-122b-131k-opencode-traces
Agent trace dataset
Decoding the literal token IDs
The prompt_token_ids / completion_token_ids / logprobs columns are the
verbatim tokens the serving engine emitted, stored PER AGENT STEP as a
list-of-lists (one inner list per turn). To turn them back into text you MUST
use the exact tokenizer the model was served with — a generic same-family
tokenizer will decode word tokens to garbage.
Served model / tokenizer source: Qwen/Qwen3.5-122B-A10B-FP8
from transformers… See the full description on the dataset page: https://huggingface.co/datasets/open-athena/nemotron-gym-agent-calendar-qwen3.5-122b-131k-opencode-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.glm-qwen-kimi-traces-agent-v1
GLM-Qwen-Kimi-Traces-Agent-V1
Exact 134,000-row SFT dataset.
Composition
Domain
Rows
math
20,000
tool_use
17,000
general_coding
20,000
agentic_coding
30,000
website_coding
30,000
reasoning
17,000
TOTAL
134,000
Website coding is kept as a dedicated domain.
The processing pipeline uses incremental Parquet batches,
column projection, HTTP JSONL streaming, global SHA-256
deduplication, source early-stop, and exact target checks.
No… See the full description on the dataset page: https://huggingface.co/datasets/usernamebetter/glm-qwen-kimi-traces-agent-v1.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.glm52-datagen-r11-17-agent-calendar-tracesagent-traces-flashmaniaswival.dev agent traces for single-shot "Reimplement trackmania" prompt using Gemini-3.5-flash.
Generated code: https://github.com/dip-proto/flashmania
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.or-agent-execution-traces
or-agent-execution-traces
Structured execution traces from the ORForge full-lifecycle OR engineering agent.
Schema
Field
Description
task
BUILD, REVISE, or EXPLAIN
files_opened
Workspace files accessed by the agent
actions
Ordered agent actions with payloads
model_changes
Parameter and model modifications
solver_output
Solver status, objective, variable values
final_result
Task outcome summary
success
Whether the task completed successfully… See the full description on the dataset page: https://huggingface.co/datasets/alirezaaminzadeh/or-agent-execution-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.mcphunt-agent-traces
MCPHunt Agent Traces
Agent execution traces from the MCPHunt evaluation framework, measuring
cross-boundary data propagation in multi-server MCP agents.
Contents
main/ — 3,615 traces from 5 models across 147 tasks and 7 environment
variants (risky_v1/v2/v3, benign, hard_neg_v1/v2/v3). One JSON file per model.
mitigation/ — 2,706 traces from the prompt-mitigation study (M0--M3
levels) across 3 models.
live_guard_defense/ — 387 DeepSeek-V4-Flash traces from the… See the full description on the dataset page: https://huggingface.co/datasets/mcphunt-benchmark/mcphunt-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/insym/hermes-agent-reasoning-traces.