OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Developer Tools & SDKs

Bespoke Curator

Bespoke Curator is an open-source Python library for bulk inference and synthetic-data curation, including structured extraction and dataset preparation for model post-training.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Bespoke Curator — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Curator when a Python workflow needs to apply model inference across a dataset and transform responses into structured records or synthetic training data.

Who it is for

  • Python developers creating synthetic datasets for model post-training.
  • Teams running bulk inference to extract structured fields from input data.
  • Practitioners preparing task-specific data for fine-tuning workflows.

Documented uses

  • Generate synthetic instruction-tuning examples.
  • Apply a prompt and structured response format across a dataset.
  • Turn model responses into curated output records.
  • Prepare questions and other fine-tuning data from domain-specific documents.
Before you choose

Requirements & limits

The README documents installation through the Python package `bespokelabs-curator`. The repository identifies an Apache-2.0 license.

  • The workflow is defined in Python, including custom prompt and parsing methods.
  • The README describes structured-output support and inference options through LiteLLM, vLLM and popular batch APIs.
  • Parallelism, retries and response caching are handled by Curator in the documented quickstart.
  • A model and supported inference route must be selected for the intended workflow.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install Bespoke Curator

Add the Curator Python package to an environment for a Python app or notebook.

Before you start

  • A Python environment and its associated Python interpreter.

Steps

  1. Install the package with the command below.

    Documented command
    python -m pip install bespokelabs-curator

  2. Use the same interpreter for the app or notebook that will import Curator.

Expected outcome

The Curator package is installed in the selected Python environment.

Guide limitations

  • Installation alone does not configure a model provider or demonstrate an inference run.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
11 October 2026
Original website title
bespokelabsai/curator · Official homepage title · retained in its source language
How we review sourcesSuggest a correction