TauTrait is a benchmark for evaluating large language models (LLMs) with realistic, persona-aware simulations. It builds on Tau-Bench from Sierra 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.
TauTrait 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 TauTrait includes evaluation tasks in four industries:
    • 🛒 Retail
    • ✈️ Airline
    • 📱 Telecom
    • 🩺 Telehealth

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🚀 Getting Started

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Installation

pip install tau-trait

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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},
)
Each rollout writes a checkpoint file under results/ in the format agent_strategy-model-temperature_range_start-end_user-user_strategy_traits-<traits>_<timestamp>.json. The JSON captures the reward, transcript, and debug info for every task. Some definitions of the settings are below.

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TauTrait Config Settings

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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.

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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).

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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.

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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).

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Execution Controls

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