AI Agents
SWE-bench
The benchmark that grades AI on real GitHub issues.
★ 6.0k starsMIT
View guide →A benchmark for tool-agent-user interaction in real-world domains.
tau2-bench is an open-source ai agents project by sierra-research (sierra-research/tau2-bench on GitHub). It has 2,186 GitHub stars and 562 forks, is released under the MIT license, and was last updated on GitHub on 7 October 2026.
Start with one concrete task rather than a grand architecture.
Clone the repo and check the examples folder for a quickstart.
Install packages, set environment variables, and point it at your model.
Execute the demo workflow, then adapt it to your own data.
Tool, agent, and user interaction.
Realistic task settings.
Dialogue agents under test.
From Sierra Research.
Inspectable benchmark design.
Free to use and extend.
Download the source and move into the project folder.
git clone https://github.com/sierra-research/tau2-bench.git
cd tau2-benchCreate a virtual environment and install the requirements.
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txtTreat these as starting points - the README documents the exact commands.
python examples/quickstart.pysierra-research/tau2-bench is its home on GitHub, with 562 forks so far. The contributors tab and recent commits show how actively it is maintained.
Yes - it is MIT licensed, so you can use, modify, and distribute it freely, including in commercial projects.
Running the evaluation is free; the model under test may use your own paid API keys.
tau2-bench evaluates tool-agent-user interaction in conversational domains; ClawBench targets browser agents on daily web tasks. Use both if your agent talks and browses.
Follow the README quickstart, then browse the examples directory for patterns that match what you want to build.
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