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Lightcap/agent-runtime-telemetry-small

Agent Runtime Telemetry Small Curated by Faruk Alpay. Agent Runtime Telemetry Small is a compact tabular export of MCP-style agent execution telemetry. It is designed for dataset viewer inspection, lightweight agent observability experiments, tool-call reliability analysis, workflow trace summaries, and audit-trail research. The dataset is intentionally small and row-oriented. Each table is stored as Parquet so the Hugging Face Dataset Viewer can display clean columns without… See the full description on the dataset page: https://huggingface.co/datasets/Lightcap/agent-runtime-telemetry-small.

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Agent Runtime Telemetry Small

Curated by Faruk Alpay.

Agent Runtime Telemetry Small is a compact tabular export of MCP-style agent execution telemetry. It is designed for dataset viewer inspection, lightweight agent observability experiments, tool-call reliability analysis, workflow trace summaries, and audit-trail research.

The dataset is intentionally small and row-oriented. Each table is stored as Parquet so the Hugging Face Dataset Viewer can display clean columns without requiring a SQLite client.

What It Contains

ConfigRowsColumnsPurpose
dataset_overview76Table inventory and export policy
operations2,26233Tool execution records, status, stages, durations, and summarized result metadata
operation_events9,90313Lifecycle events for operations
artifact_records1,26919Forecast, state-decode, and training artifact index records
audit_records14,05317Tool request/result audit rows with compact metadata
tool_summary328Aggregated tool reliability and latency statistics
artifact_summary97Aggregated artifact status and payload-size statistics
daily_activity85UTC daily activity counts across runtime tables

Privacy Boundary

This export does not upload the original SQLite databases and does not include raw nested payload_json bodies. Large JSON fields are represented with inspectable columns such as key lists, byte lengths, selected scalar status fields, and SHA-256 digests. Absolute local paths are reduced to path scope and file name columns.

Suggested Uses

  • —compare agent tool success/error rates across runtime traces
  • —inspect workflow latency and stage transitions
  • —prototype LLM agent observability dashboards
  • —analyze audit request/result volume without parsing full JSON logs
  • —benchmark small-data telemetry pipelines that expect clean tabular inputs

Loading Example

python
from datasets import load_dataset

ops = load_dataset("Lightcap/agent-runtime-telemetry-small", "operations")
print(ops["train"][0])

summary = load_dataset("Lightcap/agent-runtime-telemetry-small", "tool_summary")
print(summary["train"].to_pandas().sort_values("operation_count", ascending=False).head())

Source

The rows were exported from local runtime SQLite stores into sanitized Parquet tables:

  • —operation_state.sqlite3
  • —artifact_store.sqlite3
  • —audit_store.sqlite3

The export focuses on the operational shape of agent runtimes rather than application-specific content.