| --- |
| pretty_name: Agent Runtime Telemetry Small |
| license: cc-by-4.0 |
| language: |
| - en |
| tags: |
| - agent-runtime |
| - agent-observability |
| - llm-observability |
| - mcp |
| - tool-calling |
| - runtime-telemetry |
| - audit-trail |
| - workflow-traces |
| - parquet |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: dataset_overview |
| data_files: |
| - split: train |
| path: data/dataset_overview.parquet |
| - config_name: operations |
| data_files: |
| - split: train |
| path: data/operations.parquet |
| - config_name: operation_events |
| data_files: |
| - split: train |
| path: data/operation_events.parquet |
| - config_name: artifact_records |
| data_files: |
| - split: train |
| path: data/artifact_records.parquet |
| - config_name: audit_records |
| data_files: |
| - split: train |
| path: data/audit_records.parquet |
| - config_name: tool_summary |
| data_files: |
| - split: train |
| path: data/tool_summary.parquet |
| - config_name: artifact_summary |
| data_files: |
| - split: train |
| path: data/artifact_summary.parquet |
| - config_name: daily_activity |
| data_files: |
| - split: train |
| path: data/daily_activity.parquet |
| --- |
| |
| # 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 |
|
|
| ```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. |
|
|
