tau-trait 0.1.0

The Tau-Trait package

Tau-Trait

Collinear AI

Tau-Trait is a benchmark for evaluating large language models (LLMs) with realistic, persona-aware simulations. It builds on Tau-Bench but introduces two key modifications:

1. TraitBasis-generated personas – more accurate and interpretable user simulations.
2. Domain-specific evaluation – tasks drawn from retail, airline, telecom, and telehealth settings.

Tau-Trait is designed to test model robustness, personalization, and fairness in high-impact, customer-facing domains where user traits strongly influence interaction quality.

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✨ Features

• Persona Simulation with TraitBasis Generate diverse, coherent user personas with different traits.

• Domain Coverage Tau-Trait includes evaluation tasks in four industries:

  • 🛒 Retail
  • ✈️ Airline
  • 📱 Telecom
  • 🩺 Telehealth

🚀 Getting Started

Installation

pip install tau-trait

Usage

import argparse
from tau_trait.types import RunConfig
from tau_trait.run import run
from litellm import provider_list
from tau_trait.envs.user import UserStrategy

from tau_trait.types import RunConfig
from tau_trait.run import run

config = RunConfig(
    model_provider="openai",
    user_model_provider="steer",
    model=CLIENT_ASSISTANT_MODEL_NAME,
    user_model="", # steer api abstracts the model
    num_trials=1,
    env="retail",
    agent_strategy="tool-calling",
    temperature=0.7,
    task_split="test",
    start_index=0,
    end_index=-1,
    task_ids=[4],
    log_dir="results",
    max_concurrency=1,
    seed=10,
    shuffle=0,
    user_strategy="llm",
    few_shot_displays_path=None,
    trait_dict={"impatience": 1, "confusion": 0, "skeptical": 0, "incoherence": 0},
)

Some definitions of the settings are below.

Tau-Hard Config Settings

General

• --num-trials (int, default: 1)
  Number of independent trials to run.

• --seed (int, default: 10)
  Random seed for reproducibility.

• --shuffle (int, default: 0)
  Whether to shuffle task order (0 = no, 1 = yes).

• --log-dir (str, default: results)
  Directory where logs and results are stored.

Environment & Tasks

• --env (str, choices: retail, airline, default: retail)
  Domain environment in which to run simulations.

• --task-split (str, choices: train, test, dev, default: test)
  Dataset split of tasks to run (applies only to the retail domain currently).

• --start-index (int, default: 0)
  Index of the first task to run.

• --end-index (int, default: -1)
  Index of the last task to run. Use -1 to run all remaining tasks.

• --task-ids (list of int, optional)
  Explicit list of task IDs to run (overrides index ranges).

Agent Configuration

• --model (str, required)
  The model to use for the agent.

• --model-provider (str, choices from provider_list)
  Provider for the agent’s model.

• --agent-strategy (str, choices: tool-calling, act, react, few-shot, default: tool-calling)
  Strategy used by the agent to interact with the environment.

  • tool-calling: Invoke external tools.
  • act: Pure action selection.
  • react: Reason + act alternation.
  • few-shot: Use few-shot exemplars.

• --temperature (float, default: 0.0)
  Sampling temperature for the action model (higher = more randomness).

• --few-shot-displays-path (str, optional)
  Path to a JSONL file containing few-shot demonstration examples.

User Simulator Configuration

• --user-model (str, default: gpt-4o)
  Model to use for the user simulator.

• --user-model-provider (str, optional)
  Provider for the user simulator’s model.

• --user-strategy (str, choices from UserStrategy, default: llm)
  Strategy for the simulated user (e.g., LLM-based).

Execution Controls

• --max-concurrency (int, default: 1)
  Number of tasks to run in parallel.

@misc{curator-evals,
  author       = {Mackey, Tsach and Shafique, Muhammad Ali and Kumar, Anand},
  title        = {Curator Evals: A Benchmark for High-quality Post-training Data Curation},
  year         = {2025},
  month        = {Sep},
  howpublished = {\url{https://github.com/collinear-ai/curator-evals}}
}