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] | 30,956
| 1,407,298.072362
| 0.129257
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0xc9daf903ef5aa60d5342d07f272d1336
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c508df22-46b7-4f8d-8932-75016e579d5e
| [{"attributes":{"agent.type":null,"gen_ai.operation.name":null,"gen_ai.request.max_tokens":null,"gen(...TRUNCATED)
| 27,803
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0xe85db2811eea10ec3035f65034ac7d7
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c508df22-46b7-4f8d-8932-75016e579d5e
| [{"attributes":{"agent.type":null,"gen_ai.operation.name":null,"gen_ai.request.max_tokens":null,"gen(...TRUNCATED)
| 29,521
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0xa97ee204ac098e4dd48e42bc267d8481
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c508df22-46b7-4f8d-8932-75016e579d5e
| [{"attributes":{"agent.type":null,"gen_ai.operation.name":null,"gen_ai.request.max_tokens":null,"gen(...TRUNCATED)
| 23,548
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|
Tiny Agents. Total Visibility.
SMOLTRACE Execution Traces
This dataset contains OpenTelemetry execution traces from a SMOLTRACE benchmark run.
Dataset Information
| Field | Value |
|---|---|
| Model | ibm-granite/granite-3.3-8b-instruct |
| Run ID | c508df22-46b7-4f8d-8932-75016e579d5e |
| Total Traces | 7 |
| Generated | 2025-11-26 11:50:46 UTC |
| Format | OpenTelemetry-compatible |
Schema
| Column | Type | Description |
|---|---|---|
trace_id |
string | Unique trace identifier |
span_id |
string | Span identifier within trace |
parent_span_id |
string | Parent span for hierarchy |
span_name |
string | Name of the operation |
start_time |
string | ISO timestamp of span start |
end_time |
string | ISO timestamp of span end |
duration_ms |
float | Span duration in milliseconds |
status |
string | Span status (OK, ERROR) |
attributes |
string | JSON with span attributes |
events |
string | JSON with span events |
total_tokens |
int | Tokens used in this span |
input_tokens |
int | Input/prompt tokens |
output_tokens |
int | Output/completion tokens |
total_cost_usd |
float | Cost for this span |
total_duration_ms |
float | Total duration including children |
Understanding Traces
SMOLTRACE uses OpenTelemetry to capture detailed execution traces:
agent.run (root span)
├── agent.step (step 1)
│ ├── llm.call (model inference)
│ └── tool.execute (tool invocation)
├── agent.step (step 2)
│ └── llm.call
└── agent.finalize
Usage
from datasets import load_dataset
import json
# Load traces
ds = load_dataset("YOUR_USERNAME/smoltrace-traces-TIMESTAMP")
# Analyze execution patterns
for trace in ds['train']:
attrs = json.loads(trace['attributes']) if trace['attributes'] else {}
print(f"Trace {trace['trace_id']}: {trace['span_name']} - {trace['duration_ms']}ms")
Related Datasets
This evaluation run also generated:
- Results Dataset: Pass/fail outcomes for each test case
- Metrics Dataset: GPU utilization and environmental metrics
- Leaderboard: Aggregated metrics for model comparison
About SMOLTRACE
SMOLTRACE is a comprehensive benchmarking and evaluation framework for Smolagents - HuggingFace's lightweight agent library.
Key Features
- Automated agent evaluation with customizable test cases
- OpenTelemetry-based tracing for detailed execution insights
- GPU metrics collection (utilization, memory, temperature, power)
- CO2 emissions and power cost tracking
- Leaderboard aggregation and comparison
Quick Links
Installation
pip install smoltrace
Citation
If you use SMOLTRACE in your research, please cite:
@software{smoltrace,
title = {SMOLTRACE: Benchmarking Framework for Smolagents},
author = {Thakkar, Kshitij},
url = {https://github.com/Mandark-droid/SMOLTRACE},
year = {2025}
}
Generated by SMOLTRACE
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