bespokelabsai/curator

Health v2 · maintenance only

Adoption not part of the score

1744 stars · 151 forks observed · 2026-10-13

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Bespoke Curator is a Python library for bulk LLM inference and scalable synthetic data curation for post-training and structured data extraction. It provides a declarative prompt/parse interface over datasets, with caching, fault recovery, structured outputs via Pydantic, and integrations with LiteLLM, vLLM, batch APIs, and fine-tuning backends like Tinker and Fireworks AI.

Use cases

• generate synthetic instruction-tuning datasets with llms
• bulk run prompts over a dataset in parallel
• extract structured data from llm responses with pydantic schemas
• create reasoning datasets for fine-tuning like openthoughts
• resume large llm data generation runs after failures
• fine-tune a lora from curated data via tinker or fireworks
• visualize and inspect llm-generated datasets while generating

When to choose

• you need to generate large-scale synthetic training data with llms reliably
• you want structured outputs and caching/fault recovery built into a data pipeline
• you are building post-training datasets (sft, reasoning, agents) for fine-tuning

When to avoid

• you need a single one-off llm call rather than dataset-scale generation
• you want a no-code gui tool instead of a python library
• you are not comfortable sending data through hosted llm apis or the hosted viewer

Facets

library · maturity active

llm-inference data-generation etl rag llm-training sdk machine-learning large-language-models developer-tools python cross-platform synthetic-data fine-tuning structured-output batch-inference data-curation instruction-tuning litellm vllm data-engineering natural-language-processing

Member repositories

For agents

markdown · JSON · MCP: product_card(name="bespokelabsai/curator")