textopen-thoughts/AgentTroveagentsagentic-tracescodereinforcement-learningfine-tuningtool-usesftapache-2.0

AgentTrove Agentic Interaction Traces

Free

Open dataset

Sample structure: 59.5 / 100
3 download links issued
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Category
Text
Records
1,696,847 rows
Format
PARQUET
Update Frequency
Not documented
Collection Method
auto_imported_huggingface_federated
PII
No flagged field names; not a privacy audit
File Size
~18646.57 MB
Download links issued
3

Source, license and coverage

Supplier documentation. These claims are separate from the automated sample score. A listing edit date is not a data freshness date.

License
apache-2.0
Source / creator
open-thoughts/AgentTrove
Collection method
The OpenThoughts-Agent team aggregated agentic interaction traces from 219 existing open-source datasets covering code repair, shell scripting, math, competitive programming, and computer-use tasks. Traces were normalized into a unified schema and packaged as Parquet for scalable consumption. Some traces are originally machine-generated by agent frameworks (e.g., Terminus-2, Harbor) operating against benchmark tasks.
Coverage start
Not documented
Coverage end
Not documented
Data last updated
Not documented
Update schedule
Not documented

Source documentation ↗

License terms ↗

- English-only; no multilingual coverage. - Heterogeneous quality: aggregated from 219 sources with varying generation methods, model providers, and success criteria — trace quality is not uniform. - Likely leakage risk against common code/agent benchmarks (SWE-bench, HumanEval, MATH, etc.) since upstream sources may have been generated on those tasks. Not deduplicated against held-out eval suites. - Source dataset licenses vary; the Apache 2.0 license applies to the AgentTrove aggregation but downstream users should verify compatibility for individual source contributions if redistributing subsets. - Source does not exhaustively document per-row provenance gaps; buyers should validate empirically.

Sample structure score: 59.5 / 100

This automated check describes the inspected sample, not factual accuracy, legal rights, representativeness, or the quality of the entire dataset. It is not a customer rating.

Assessed 10 sample records (JSON) on 2026-10-09. All records in the provided sample were checked.

CheckPointsEvidence
Populated cells9.5 / 5040 of 210 top-level cells contain a value. Null, absent and blank values count as missing; zero and false count as populated.
Consistent value types30 / 3040 of 40 populated cells match their column's most common observed type. Types are inferred, not checked against real-world truth.
Consistent record shape20 / 2010 of 10 records have the expected fields. CSV/TSV use the header width; JSON uses the union of observed keys.
Field-level findings and improvements

Check missing cells and mixed types below. Document intentional missing values or mixed types in your field descriptions. Do not fill legitimate unknowns with invented values just to increase this score.

FieldMissing cellsMost common typeOther populated types
conversations10 / 10unknown0 / 0
agent10 / 10unknown0 / 0
model10 / 10unknown0 / 0
date10 / 10unknown0 / 0
task10 / 10unknown0 / 0
episode10 / 10unknown0 / 0
run_id10 / 10unknown0 / 0
trial_name10 / 10unknown0 / 0
model_provider10 / 10unknown0 / 0
original_source0 / 10string0 / 10
original_teacher0 / 10string0 / 10
result10 / 10unknown0 / 0
trace_source10 / 10unknown0 / 0
path0 / 10string0 / 10
task_binary0 / 10object0 / 10
instruction10 / 10unknown0 / 0
verifier_output10 / 10unknown0 / 0
ground_truth10 / 10unknown0 / 0
judgment10 / 10unknown0 / 0
__index_level_0__10 / 10unknown0 / 0
split10 / 10unknown0 / 0
How the score is calculated, its limitations, and how to correct an assessment →

About this data

Collection of agentic interaction traces spanning code repair, shell scripting, math, competitive programming, and computer-use tasks, aggregated from 219 source datasets.

Retrieve with your agent or Python

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Download the Python example
python3 retrieve-dataset.py 1d5b9795-bb25-4713-bb89-7975e43c7f9c --output dataset.bin
Full supplier documentation
## Overview AgentTrove is the largest open-source collection of agentic interaction traces to date, released by the OpenThoughts-Agent team. It contains 1,696,847 rows drawn from 219 source datasets spanning code repair, shell scripting, mathematical problem-solving, competitive programming, and general computer-use tasks. At 1.7M rows, it is roughly 4× the size of the Nemotron Terminal Corpus (430K rows), the previous largest open-source agentic trace dataset. Distributed as Parquet, English-language, text modality. ## Schema The dataset is provided as Parquet with agentic interaction traces. Typical columns in agentic-trace corpora of this kind include: - `messages` / `trajectory` — list/JSON — multi-turn agent interaction with tool calls - `task` / `instruction` — string — original task prompt - `source_dataset` — string — which of the 219 source datasets the row came from - `category` — string — task category (code repair, shell, math, competitive programming, computer-use) - `tools` — list — tool/function schemas available to the agent - `outcome` / `success` — bool/string — whether the trajectory solved the task - `language` — string — primary language (en) - `length` / `num_turns` — int — trace length metadata Buyers should consult the HF dataset page for the authoritative schema as published. ## Sources - open-thoughts/AgentTrove on HuggingFace — https://huggingface.co/datasets/open-thoughts/AgentTrove — Apache 2.0 - Aggregated from 219 upstream source datasets (see dataset card for full attribution list) ## Methodology The OpenThoughts-Agent team aggregated agentic interaction traces from 219 existing open-source datasets covering code repair, shell scripting, math, competitive programming, and computer-use tasks. Traces were normalized into a unified schema and packaged as Parquet for scalable consumption. Some traces are originally machine-generated by agent frameworks (e.g., Terminus-2, Harbor) operating against benchmark tasks. ## Known gaps & limitations - English-only; no multilingual coverage. - Heterogeneous quality: aggregated from 219 sources with varying generation methods, model providers, and success criteria — trace quality is not uniform. - Likely leakage risk against common code/agent benchmarks (SWE-bench, HumanEval, MATH, etc.) since upstream sources may have been generated on those tasks. Not deduplicated against held-out eval suites. - Source dataset licenses vary; the Apache 2.0 license applies to the AgentTrove aggregation but downstream users should verify compatibility for individual source contributions if redistributing subsets. - Source does not exhaustively document per-row provenance gaps; buyers should validate empirically. ## Intended use & out-of-scope - **Intended:** SFT and RL fine-tuning of agentic / tool-using LLMs, behavior cloning, trajectory analysis, agent capability research, RAG over agent traces. - **Out-of-scope:** Training models for benchmark evaluation without first deduplicating against target eval sets (high leakage risk); production deployment without quality filtering across the 219 heterogeneous sources. _Federated dataset: 38 parquet shards, 18.21 GB total. Queries and downloads stream through the DataBazaar API._ Original supplier listing: AgentTrove: 1.7M Agentic Interaction Traces Largest open-source collection of agentic interaction traces (1.7M rows from 219 source datasets) covering code repair, shell scripting, math, competitive programming, and computer-use tasks. Apache 2.0.

Schema

NameTypeDescription
conversationsSTRUCT("content" VARCHAR, "role" VARCHAR)[]Multi-turn agent-environment interaction with content (string) and role (agent/user/system) pairs
agentVARCHARAgent identifier or name executing the task
modelVARCHARLanguage model name used by the agent
dateVARCHARTimestamp or date when the trace was recorded
taskVARCHAROriginal task prompt or instruction text
episodeVARCHAREpisode identifier for grouped interactions
run_idVARCHARUnique identifier for a single execution run
trial_nameVARCHARName or label for the trial/experiment
model_providerVARCHARProvider of the language model (e.g., OpenAI, Anthropic)
original_sourceVARCHARSource dataset or origin of the task (e.g., exp_rpt)
original_teacherVARCHARTeacher model or reference model that generated the trace
resultVARCHARResult status or outcome of the agent's execution
trace_sourceVARCHARSource system or framework that generated the trace
pathVARCHARFile path or identifier for the task resource
task_binaryBLOBGzip-compressed binary encoding of task data
instructionVARCHARDetailed instruction or prompt given to the agent
verifier_outputVARCHAROutput from verification/evaluation of agent's solution
ground_truthVARCHARExpected correct solution or reference output
judgmentVARCHARHuman or automated judgment of solution correctness
__index_level_0__BIGINTZero-indexed row number in the original dataset
splitVARCHARDataset partition label (train/val/test)

Sample Data

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// Found: 1d5b9795-bb25-4713-bb89-7975e43c7f9c
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