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 requiring a SQLite client.

What It Contains

Config	Rows	Columns	Purpose
dataset_overview	7	6	Table inventory and export policy
operations	2,262	33	Tool execution records, status, stages, durations, and summarized result metadata
operation_events	9,903	13	Lifecycle events for operations
artifact_records	1,269	19	Forecast, state-decode, and training artifact index records
audit_records	14,053	17	Tool request/result audit rows with compact metadata
tool_summary	32	8	Aggregated tool reliability and latency statistics
artifact_summary	9	7	Aggregated artifact status and payload-size statistics
daily_activity	8	5	UTC 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

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.