README.md

metadata

license: mit
task_categories:
  - question-answering
  - text-generation
language:
  - en
pretty_name: FlowBench
size_categories:
  - n<1K
tags:
  - agents
  - tool-use
  - benchmark
  - harbor
  - data-orchestration

FlowBench

Dataset ID: jwu323/FlowBench

FlowBench is a tool-use benchmark for deterministic business operations workflows. Each task asks an agent to compose Python tools over synthetic customers, products, orders, returns, inventory, support tickets, FX rates, and SLA policies.

This repository contains the public test split. It is answer-free by design: fixed gold labels, oracle solutions, and strict verifier expected files are not included. The public files are suitable for task inspection, agent integration, and reproducible smoke tests. Official scoring should use a private evaluator pack or a freshly generated held-out split with hidden answers.

Splits

test contains 300 public task specifications:

depth	tasks	description
1	50	single-step lookups such as region currency or top-selling product id
2	50	filtered order and customer counts
3	50	revenue and refund-rate aggregations
4	50	local-currency conversion and margin-after-refund calculations
5	50	replenishment shortfall and delayed-order revenue workflows
6	50	delayed fulfillment plus SLA breach burden, and at-risk revenue from breached tickets

There are 12 task families with 25 tasks per family. The public test split is closed-world and deterministic, but it is not a public-answer leaderboard split.

Files

• data/test.jsonl: answer-free task records.
• tools/flowbench_tools.py: deterministic tool implementation visible to agents.
• harbor/: Harbor-compatible public task pack with a smoke verifier. The smoke verifier checks output shape only; it is not official scoring.
• RUN.md: instructions for wiring the public split to an LLM agent and writing prediction files.

Each JSONL record includes:

• task_id
• depth
• family
• region, category, month_start, month_end
• instruction
• answer_format

No record contains a gold answer.

Where the Data Comes From

There is no separate CSV, database dump, or network service to download. The benchmark data are generated deterministically inside tools/flowbench_tools.py and inside each Harbor task's /app/flowbench_tools.py. Importing that file builds in-memory tables for 72 customers, 48 products, 720 orders, 180 returns, 260 support tickets, inventory, FX rates, and SLA policies from SHA-256 keyed generators. The same task and tool file therefore produce the same data on every machine.

When running without Harbor, load tools/flowbench_tools.py into the agent runtime and expose its functions as the tool API. When running with Harbor, the task container already places the same tool file at /app/flowbench_tools.py. Agents should solve tasks by calling the provided tools; the public release does not ship gold answers or a strict answer checker.

Running an Agent

For public agent evaluation, give the agent one task record from data/test.jsonl, expose only the functions listed in tools/flowbench_tools.py::TOOLS, and ask the agent to return only the final answer in the record's answer_format. The task record supplies the question and parameters; the tool module is the data source and builds all tables deterministically at import time.

Recommended public prediction format:

{"task_id": "v3_depth1_currency_lookup_t0", "answer": "USD", "model": "your-model-name"}

RUN.md has a fuller harness contract, adapter skeleton, prompt template, and a worked tool plan for a depth-6 task. The public split has no labels, so predictions from this repository alone are for inspection, integration testing, or private scoring; do not report them as official FlowBench scores.

Running the Harbor Smoke Pack

harbor run -p harbor -a <agent> -l 1

The Harbor pack in this repository is a public smoke pack. It verifies that an agent writes an output with the required shape. It deliberately does not contain the private expected answers.

Task Families

• currency_lookup
• top_product_lookup
• order_count
• unique_customer_count
• net_revenue_usd
• refund_share_bp
• local_net_revenue
• margin_after_refunds
• reorder_shortfall
• delayed_net_revenue
• delay_sla_burden
• breached_ticket_revenue

Evaluation Notes

FlowBench is designed to compare how agent interfaces express the same tool capabilities: function-calling style, command style, and code/REPL style substrates can all call the same tools. For public release, do not evaluate by matching against labels embedded in this repository; there are none. Use a private evaluator or generate a held-out split with hidden answers.

Citation

If you use FlowBench, cite the associated paper or this dataset repository once the paper is released.