> For the complete documentation index, see [llms.txt](https://docs.bespokelabs.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.bespokelabs.ai/bespoke-curator/getting-started.md).

# Getting Started

Bespoke Curator makes it easy to create synthetic data pipelines. Whether you are training a model or extracting structure, Curator will prepare high-quality data quickly and robustly.

* Rich Python based library for generating and curating synthetic data.
* Interactive viewer to monitor data while it is being generated
* First class support for structured outputs
* Built-in performance optimizations for asynchronous operations, caching, and fault recovery at every scale
* Support for a wide range of inference options via LiteLLM, vLLM, and popular batch APIs

<figure><img src="/files/PN5spnMGp7LHFJCySIlu" alt=""><figcaption></figcaption></figure>

In addition, we are actively working on improving the library. Expect more changes to come in the future:

1. Verifiers: filter outputs to improve your data quality with models like [Broken mention](broken://pages/Zvukm7MAGL6ooitMTmpY), or with code executors.
2. MCTS: explore reasoning trajectories using Monte Carlo Tree Search.
3. Data versioning: version your data along with the code that generates it.
4. Diversity and data quality indicators: understand the quality of your data.
5. Curator viewer: visualize and explore your generated data.

Next, let's take a [Quick Tour](/bespoke-curator/getting-started/quick-tour.md) of the Curator library!