kshitijthakkar/smoltrace-results-20251126_104847

Tiny Agents. Total Visibility.

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SMOLTRACE Evaluation Results

This dataset contains evaluation results from a SMOLTRACE benchmark run.

Dataset Information

Field	Value
Model	google/gemma-2-9b-it
Run ID	22f49e98-ecbb-4030-945b-4fbb7e779352
Agent Type	code
Total Tests	7
Generated	2025-11-26 10:50:14 UTC
Source Dataset	kshitijthakkar/smoltrace-tasks

Schema

Column	Type	Description
model	string	Model identifier
evaluation_date	string	ISO timestamp of evaluation
task_id	string	Unique test case identifier
agent_type	string	"tool" or "code" agent type
difficulty	string	Test difficulty level
prompt	string	Test prompt/question
success	bool	Whether the test passed
tool_called	bool	Whether a tool was invoked
correct_tool	bool	Whether the correct tool was used
final_answer_called	bool	Whether final_answer was called
response_correct	bool	Whether the response was correct
tools_used	string	Comma-separated list of tools used
steps	int	Number of agent steps taken
response	string	Agent's final response
error	string	Error message if failed
trace_id	string	OpenTelemetry trace ID
execution_time_ms	float	Execution time in milliseconds
total_tokens	int	Total tokens consumed
cost_usd	float	API cost in USD
enhanced_trace_info	string	JSON with detailed trace data

Usage

from datasets import load_dataset

# Load the results dataset
ds = load_dataset("YOUR_USERNAME/smoltrace-results-TIMESTAMP")

# Filter successful tests
successful = ds.filter(lambda x: x['success'])

# Calculate success rate
success_rate = sum(1 for r in ds['train'] if r['success']) / len(ds['train']) * 100
print(f"Success Rate: {success_rate:.2f}%")

Related Datasets

This evaluation run also generated:

• Traces Dataset: Detailed OpenTelemetry execution traces
• Metrics Dataset: GPU utilization and environmental metrics
• Leaderboard: Aggregated metrics for model comparison

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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

• GitHub Repository
• PyPI Package
• Documentation
• Report Issues

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}
}

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_{Generated by SMOLTRACE}