REPOGEO REPORT · LITE

explosion/spacy-llm

Default branch main · commit 99ac9229 · scanned 5/23/2026, 12:51:42 PM

GitHub: 1,389 stars · 105 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

3 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface explosion/spacy-llm, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition the README H1 to specify spaCy integration

Why:

CURRENT

<h1 align="center">
<span style="font: bold 38pt'Courier New';">spacy-llm</span>
<br>Structured NLP with LLMs
</h1>

COPY-PASTE FIX

<h1 align="center">
<span style="font: bold 38pt'Courier New';">spacy-llm</span>
<br>Integrate LLMs into spaCy for Structured NLP
</h1>

mediumreadme#2

Add a comparison section to the README

Why:

COPY-PASTE FIX

## Why spacy-llm? How it compares

spacy-llm is designed specifically for integrating Large Language Models into existing spaCy pipelines, focusing on turning unstructured LLM outputs into robust, structured data for NLP tasks like NER and text classification. While frameworks like LangChain and Haystack offer broader LLM orchestration, spacy-llm excels at making LLMs first-class components within the spaCy ecosystem.

lowtopics#3

Refine topics to emphasize spaCy integration

Why:

CURRENT

anthropic, claude, cohere, dolly, falcon, gpt-3, gpt-4, large-language-models, llama, llm, machine-learning, named-entity-recognition, natural-language-processing, nlp, openai, prompt-engineering, spacy, text-classification

COPY-PASTE FIX

anthropic, claude, cohere, dolly, falcon, gpt-3, gpt-4, large-language-models, llama, llm, machine-learning, named-entity-recognition, natural-language-processing, nlp, openai, prompt-engineering, spacy, text-classification, spacy-component, nlp-pipeline, llm-integration-spacy

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface explosion/spacy-llm

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Hugging Face Transformers

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Hugging Face Transformers · recommended 2×
spaCy · recommended 1×
spacy-transformers · recommended 1×
Flair · recommended 1×
OpenNMT · recommended 1×

CATEGORY QUERY
How to integrate large language models for named entity recognition in existing NLP pipelines?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. spaCy
3. spacy-transformers
4. Flair
5. OpenNMT
6. Google Cloud AI Platform
7. Vertex AI
8. Amazon Comprehend
AI recommended 8 alternatives but never named explosion/spacy-llm. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a framework to perform text classification using LLMs without requiring training data.
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. LangChain
3. OpenAI API
4. Haystack
5. SetFit
6. Spacy
AI recommended 6 alternatives but never named explosion/spacy-llm. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of explosion/spacy-llm?
pass
AI named explosion/spacy-llm explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts explosion/spacy-llm in production, what risks or prerequisites should they evaluate first?
pass
AI named explosion/spacy-llm explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo explosion/spacy-llm solve, and who is the primary audience?
pass
AI named explosion/spacy-llm explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of explosion/spacy-llm. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/explosion/spacy-llm.svg)](https://repogeo.com/en/r/explosion/spacy-llm)

HTML

<a href="https://repogeo.com/en/r/explosion/spacy-llm"><img src="https://repogeo.com/badge/explosion/spacy-llm.svg" alt="RepoGEO" /></a>

Pro

Subscribe to Pro for deep diagnoses

explosion/spacy-llm — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite