explosion/spacy-llm

Health v2 · maintenance only

Adoption not part of the score

1394 stars · 109 forks observed · 2026-08-28

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

spacy-llm is a Python library that integrates Large Language Models into spaCy NLP pipelines via a serializable llm component. It provides modular task and model abstractions for prompting and parsing LLM responses into robust structured outputs for NLP tasks without training data.

Use cases

• extract named entities from text using gpt-4 without training data
• add llm-based text classification to a spacy pipeline
• prototype prompts for nlp tasks like relation extraction or summarization
• use open-source llms like llama or mistral for zero-shot ner
• turn unstructured llm responses into structured spacy doc annotations
• run sentiment analysis and lemmatization with an llm api

When to choose

• you already use spaCy and want LLM capabilities inside your existing pipeline
• you need structured, parsed outputs from LLMs for standard NLP tasks
• you want to swap between hosted APIs (OpenAI, Anthropic, Cohere) and self-hosted open-source models
• you want fast zero-shot prototyping without labeled training data

When to avoid

• you need a general-purpose LLM agent or chat framework rather than structured NLP tasks
• your project doesn't use spaCy and you don't want the dependency
• you need fine-grained control over raw LLM API calls outside a pipeline abstraction

Facets

library · maturity active

nlp llm-inference prompt-engineering machine-learning parser large-language-models machine-learning developer-tools python cross-platform spacy llm-pipeline named-entity-recognition text-classification openai anthropic huggingface langchain zero-shot-nlp natural-language-processing

Member repositories

For agents

markdown · JSON · MCP: product_card(name="explosion/spacy-llm")