UpstageAI/tau2-bench

UpstageAI/tau2-bench

Description: τ²-Bench: Evaluating Conversational Agents in a Dual-Control Environment

Language: Python

License: MIT

Stars: 0

Forks: 0

Open issues: 0

Created: 2025-08-07T05:50:18Z

Pushed: 2025-10-01T04:51:26Z

Default branch: main

Fork: yes

Parent repository: sierra-research/tau2-bench

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README:

$\tau^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment

Overview

$\tau^2$-bench implements a simulation framework for evaluating customer service agents across various domains.

Each domain specifies:

• a policy that the agent must follow
• a set of tools that the agent can use
• a set of tasks to evaluate the agent's performance
• Optionally: A set of tools that the user simulator can use

Domains are:

• mock
• airline
• retail
• telecom

All the information that an agent developer needs to build an agent for a domain can be accessed through the domain's API docs. See [View domain documentation](#view-domain-documentation) for more details.

Installation

1. Clone the repository:

git clone https://github.com/sierra-research/tau2-bench
cd tau2-bench

2. Create a new environment (optional)

$\tau^2$-bench requires Python 3.10 or higher. You may create and activate a new environment:

python -m venv .venv
source .venv/bin/activate

3. Install tau2

pip install -e .

This will enable you to run the tau2 command.

Note: If you use pip install . (without -e), you'll need to set the TAU2_DATA_DIR environment variable to point to your data directory:

export TAU2_DATA_DIR=/path/to/your/tau2-bench/data

Check your data directory setup:

After installation, you can verify that your data directory is correctly configured by running:

tau2 check-data

This command will check if the data directory exists and print instructions if it is missing.

To remove all the generated files and the virtual environment, run:

make clean

Quick Start

Setup LLM API keys

We use LiteLLM to manage LLM APIs, so you can use any LLM provider supported by LiteLLM.

To provide your API keys, copy .env.example as .env and edit it to include your API keys.

Run agent evaluation

To run a test evaluation on only 5 tasks with 1 trial per task, run:

tau2 run \
--domain airline \
--agent-llm gpt-4.1 \
--user-llm gpt-4.1 \
--num-trials 1 \
--num-tasks 5

Results will be saved in data/tau2/simulations/.

Command Line Interface

The tau2 command provides a unified interface for all functionality:

Running Benchmark

tau2 run \
--domain \
--agent-llm \
--user-llm \
--num-trials \
--task-ids \
--max-concurrency \
...

Viewing Results

tau2 view

This tool allows you to:

• Browse simulation files (in data/tau2/simulations/)
• View agent performance metrics
• View a particular simulation
• View task details

View domain documentation

tau2 domain

Visit http://127.0.0.1:8004/redoc to see the domain policy and API documentation.


Check data configuration

tau2 check-data

This command checks if your data directory is properly configured and all required files are present.

Experiments

Running Ablation Studies (No User, or Agent with Oracle Plan)

telecom domain enables running ablation studies.

1. Running an LLM in no-user mode. In this mode, the LLM is given all the tools and the information upfront. Just choose llm_agent_solo as the agent and dummy_user as the user.

tau2 run \
--domain telecom \
--agent llm_agent_solo \
--agent-llm gpt-4.1 \
--user dummy_user \
...

2. Running an LLM in oracle-plan mode. In this mode, the LLM is given an oracle plan ahead of time alleviating the need for action planning. Just choose llm_agent_gt as the agent.

tau2 run \
--domain telecom \
--agent llm_agent_gt \
--agent-llm gpt-4.1 \
--user-llm gpt-4.1 \
...

Running Telecom Domain with Workflow Policy

To test the impact of policy format, we provide an additional "workflow" policy for the telecom domain. To run using this policy, use the telecom-workflow domain.

tau2 run \
--domain telecom-workflow \
--agent-llm gpt-4.1 \
--user-llm gpt-4.1 \
...

Domains

For all the details see the domains [README](src/tau2/domains/README.md).

Basics

• Code is located in src/tau2/domains/
• Data is located in data/tau2/domains/
• Each domain has its own configuration and task definitions

View domain-specific policy and API docs:

Run the following command to see the domain policy and API documentation.

tau2 env

Then visit http://127.0.0.1:8004/redoc

Environment CLI (beta)

An interactive command-line interface for directly querying and testing domain environments. Features:

• Interactive query interface with domain-specific tools
• Support for multiple domains (airline, mock, etc.)
• Session management with history

To use:

make env-cli

Available commands:

• :q - quit the program
• :d - change domain
• :n - start new session (clears history)

Example usage:

$ make env-cli

Welcome to the Environment CLI!
Connected to airline domain.

Query (:n new session, :d change domain, :q quit)> What flights are available from SF to LA tomorrow?
Assistant: Let me check the flight availability for you...
[Flight details will appear here]

The Environment CLI is useful for:

• Testing domain tools and queries
• Debugging environment responses
• Exploring available domain functionality
• Quick domain interaction without starting the full server stack

Run tests

To run the test suite use the command

make test

Config

To configure the framework, see the [config](src/tau2/config.py) file.

LLM Calls caching

LLM call caching is disabled by default.

To enable LLM calls caching:

• Make sure redis is running.
• Update the redis config in config.py if necessary.
• Set LLM_CACHE_ENABLED to True in config.py

Evaluate Your Own Agent

For local or remote agent evaluation, see our [agent developer guide](src/tau2/agent/README.md).

Orchestration Sequence Diagram

sequenceDiagram
participant O as Orchestrator
participant A as Agent
participant U as UserSimulator
participant E as Environment

Note over O: Initialize(task)
rect rgb(100, 150, 150)
O->>A:…