lotus-data/lotus

Health v2 · maintenance only

Adoption not part of the score

1661 stars · 151 forks observed · 2026-08-28

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

LOTUS is a Python library providing a Pandas-like API of LLM-powered semantic operators (map, filter, extract, aggregate, top-k) for bulk processing structured and unstructured datasets with agents and LLMs. It optimizes these declarative operations for higher accuracy and lower cost, with reported speedups up to 400x.

Use cases

• classify or filter thousands of documents with natural language criteria
• extract structured fields from unstructured text at scale
• run an agent over every file in a codebase and summarize results
• build RAG pipelines over a document corpus
• score model outputs row by row with LLM-as-judge evals
• mine agent logs for failure modes
• fan out deep research over many papers and synthesize findings

When to choose

• you need to apply LLM reasoning across a large dataset or document collection
• you want a Pandas-like declarative API instead of writing per-row LLM prompts
• cost and latency of naive LLM bulk processing are too high
• you want to combine structured and unstructured data in one query pipeline

When to avoid

• you need simple single-prompt LLM calls without bulk dataset operations
• your data is small enough that direct API calls suffice
• you need a no-code GUI tool rather than a Python library
• you require strict deterministic transformations with no LLM nondeterminism

Facets

library · maturity active

machine-learning llm-inference rag etl data-science agent-framework search-engine large-language-models data-science python semantic-operators pandas llm-data-processing query-engine unstructured-data declarative-programming data-engineering natural-language-processing retrieval-augmented-generation ai-agents

Member repositories

For agents

markdown · JSON · MCP: product_card(name="lotus-data/lotus")