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Library for constraining LLM output to a specific structure - JSON schema, regex, grammar - guaranteeing valid, parseable results.

• AI & Machine Learning

Programming paradigm and library for controlling LLM generation with interleaved prompting, control flow, and constrained output in one program.

• AI & Machine Learning

Library for getting reliable, structured data output from LLMs using function-calling APIs and Pydantic models for validation.

• AI & Machine Learning

Fast serving framework for large language models and vision-language models, with a structured generation language for complex LLM programs.

• AI & Machine Learning

Open-source testing framework for evaluating LLM outputs, treating LLM application quality checks like conventional unit tests.

• AI & Machine Learning

Document parsing library that converts PDFs, Word docs, and other formats into clean, structured data ready for LLM and RAG pipelines.

• AI & Machine Learning

CNCF-graduated log collector that unifies data collection from many sources into a single, structured logging layer.

• Monitoring

Open-source tool for testing and evaluating LLM prompts, comparing outputs across models and catching quality regressions before deployment.

• AI & Machine Learning

Fast, high-level Python web scraping and crawling framework for extracting structured data from websites.

• Developer Tools

Optimized, distributed gradient boosting library widely used for structured/tabular data problems, a staple of applied machine learning.

• AI & Machine Learning