Datasets:
File size: 3,687 Bytes
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---
license: mit
task_categories:
- text-generation
tags:
- agents
- tool-use
- benchmark
- enterprise-api
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: test_id
dtype: string
- name: test_name
dtype: string
- name: service
dtype: string
- name: task_horizon
dtype: int64
- name: operation_type
dtype: string
- name: entity_scope
dtype: string
- name: information_availability
dtype: string
- name: prompt_ambiguity
dtype: string
- name: info
dtype: string
splits:
- name: train
num_bytes: 256049
num_examples: 179
- name: test
num_bytes: 74705
num_examples: 45
download_size: 124036
dataset_size: 330754
---
# Agent-Diff Bench
[**Website**](https://agentdiff.dev) | [**Paper**](https://huggingface.co/papers/2602.11224) | [**GitHub**](https://github.com/agent-diff-bench/agent-diff)
Agent-Diff is a benchmarking framework for evaluating agentic Large Language Models (LLMs) on real-world tasks that execute code via external APIs. The benchmark provides access to real API interfaces (Slack, Box, Linear, Google Calendar) while sandboxing the environment in which calls are made and evaluated.
## Dataset Summary
The dataset contains 224 tasks utilizing enterprise software workflows, provided with an 80/20 train/test split. It introduces a **state-diff contract**, which separates process from outcome — task success is defined as whether the expected change in environment state was achieved, rather than fuzzy trace or parameter matching.
- **Services**: Slack, Linear, Box, Google Calendar.
- **Evaluation**: State-diff based (comparing "before" and "after" snapshots of the sandboxed environment).
## Sample Usage
The following example demonstrates how to run evaluations using the `agent-diff` SDK as found in the [GitHub repository](https://github.com/agent-diff-bench/agent-diff):
```python
from agent_diff import AgentDiff, PythonExecutorProxy, create_openai_tool
from agents import Agent, Runner
client = AgentDiff()
# List test suites (e.g., "Slack Bench")
suite_list = client.list_test_suites(name="Slack Bench")
slack_suite = suite_list.testSuites[0]
suite = client.get_test_suite(slack_suite.id, expand=True)
for test in suite.tests:
prompt = test.prompt
test_id = test.id
# Initialise isolated environment
env = client.init_env(testId=test_id)
# Start the run (takes a snapshot before execution)
run = client.start_run(envId=env.environmentId, testId=test_id)
# Setup agent with proxied code execution tool
python_executor = PythonExecutorProxy(env.environmentId)
python_tool = create_openai_tool(python_executor)
agent = Agent(
name="Slack Assistant",
instructions="Use execute_python tool to interact with Slack API. Authentication is handled automatically.",
tools=[python_tool]
)
# Run the agent on the task
response = await Runner.run(agent, prompt)
# Compute evaluation based on state-diff
client.evaluate_run(runId=run.runId)
run_result = client.get_results_for_run(runId=run.runId)
print(f"Test: {test_id}, Score: {run_result.score}")
# Clean up
client.delete_env(envId=env.environmentId)
```
## Citation
```bibtex
@article{pysklo2025agentdiff,
title={Agent-Diff: Benchmarking LLM Agents on Enterprise API Tasks via Code Execution with State-Diff-Based Evaluation},
author={Hubert Marek Pysklo and others},
journal={arXiv preprint arXiv:2602.11224},
year={2025}
}
``` |