AgentTrove Agentic Interaction Traces
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
- 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.
| Check | Points | Evidence |
|---|---|---|
| Populated cells | 9.5 / 50 | 40 of 210 top-level cells contain a value. Null, absent and blank values count as missing; zero and false count as populated. |
| Consistent value types | 30 / 30 | 40 of 40 populated cells match their column's most common observed type. Types are inferred, not checked against real-world truth. |
| Consistent record shape | 20 / 20 | 10 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.
| Field | Missing cells | Most common type | Other populated types |
|---|---|---|---|
| conversations | 10 / 10 | unknown | 0 / 0 |
| agent | 10 / 10 | unknown | 0 / 0 |
| model | 10 / 10 | unknown | 0 / 0 |
| date | 10 / 10 | unknown | 0 / 0 |
| task | 10 / 10 | unknown | 0 / 0 |
| episode | 10 / 10 | unknown | 0 / 0 |
| run_id | 10 / 10 | unknown | 0 / 0 |
| trial_name | 10 / 10 | unknown | 0 / 0 |
| model_provider | 10 / 10 | unknown | 0 / 0 |
| original_source | 0 / 10 | string | 0 / 10 |
| original_teacher | 0 / 10 | string | 0 / 10 |
| result | 10 / 10 | unknown | 0 / 0 |
| trace_source | 10 / 10 | unknown | 0 / 0 |
| path | 0 / 10 | string | 0 / 10 |
| task_binary | 0 / 10 | object | 0 / 10 |
| instruction | 10 / 10 | unknown | 0 / 0 |
| verifier_output | 10 / 10 | unknown | 0 / 0 |
| ground_truth | 10 / 10 | unknown | 0 / 0 |
| judgment | 10 / 10 | unknown | 0 / 0 |
| __index_level_0__ | 10 / 10 | unknown | 0 / 0 |
| split | 10 / 10 | unknown | 0 / 0 |
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
Create an account and configure DATABAZAAR_API_KEY. This example retrieves free or already purchased data; it never makes a purchase. For a multi-file dataset, choose a file index from the manifest.
Download the Python examplepython3 retrieve-dataset.py 1d5b9795-bb25-4713-bb89-7975e43c7f9c --output dataset.bin
Full supplier documentation
Schema
| Name | Type | Description |
|---|---|---|
| conversations | STRUCT("content" VARCHAR, "role" VARCHAR)[] | Multi-turn agent-environment interaction with content (string) and role (agent/user/system) pairs |
| agent | VARCHAR | Agent identifier or name executing the task |
| model | VARCHAR | Language model name used by the agent |
| date | VARCHAR | Timestamp or date when the trace was recorded |
| task | VARCHAR | Original task prompt or instruction text |
| episode | VARCHAR | Episode identifier for grouped interactions |
| run_id | VARCHAR | Unique identifier for a single execution run |
| trial_name | VARCHAR | Name or label for the trial/experiment |
| model_provider | VARCHAR | Provider of the language model (e.g., OpenAI, Anthropic) |
| original_source | VARCHAR | Source dataset or origin of the task (e.g., exp_rpt) |
| original_teacher | VARCHAR | Teacher model or reference model that generated the trace |
| result | VARCHAR | Result status or outcome of the agent's execution |
| trace_source | VARCHAR | Source system or framework that generated the trace |
| path | VARCHAR | File path or identifier for the task resource |
| task_binary | BLOB | Gzip-compressed binary encoding of task data |
| instruction | VARCHAR | Detailed instruction or prompt given to the agent |
| verifier_output | VARCHAR | Output from verification/evaluation of agent's solution |
| ground_truth | VARCHAR | Expected correct solution or reference output |
| judgment | VARCHAR | Human or automated judgment of solution correctness |
| __index_level_0__ | BIGINT | Zero-indexed row number in the original dataset |
| split | VARCHAR | Dataset partition label (train/val/test) |
Sample Data
Preview a sample of the data before downloading.
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For AI Agents
# 1. Add to your agent's MCP config (claude_desktop_config.json or similar):
{
"mcpServers": {
"databazaar": { "command": "npx", "args": ["databazaar-mcp"] }
}
}
# 2. Your agent can then call:
search_datasets({ query: "AgentTrove Agentic Interaction" })
// Found: 1d5b9795-bb25-4713-bb89-7975e43c7f9c
get_download_url({ dataset_id: "1d5b9795-bb25-4713-bb89-7975e43c7f9c" }) // free — sign in with MCP OAuth first# Free dataset — sign in or use your account API key: curl https://api.databazaar.io/datasets/1d5b9795-bb25-4713-bb89-7975e43c7f9c/download-url -H "Authorization: Bearer $DATABAZAAR_API_KEY"