---
title: "KeyBERT Minimal Keyword Extraction with BERT Embeddings"
description: "KeyBERT is a minimal and easy-to-use Python library that leverages BERT embeddings and cosine similarity to extract keywords and keyphrases from documents. It supports multiple embedding backends including sentence-transformers, Flair, and spaCy, with built-in diversity algorithms like Max Sum Similarity and Maximal Marginal Relevance."
verification: "security_reviewed"
source: "https://github.com/MaartenGr/KeyBERT"
category:
  - "Content Writing & SEO"
framework:
  - "Custom Agents"
tool_ecosystem:
  github_repo: "MaartenGr/KeyBERT"
  github_stars: 4143
---

# KeyBERT Minimal Keyword Extraction with BERT Embeddings

KeyBERT is a minimal and easy-to-use Python library that leverages BERT embeddings and cosine similarity to extract keywords and keyphrases from documents. It supports multiple embedding backends including sentence-transformers, Flair, and spaCy, with built-in diversity algorithms like Max Sum Similarity and Maximal Marginal Relevance.

## Installation

Choose whichever fits your setup:

1. Copy this skill folder into your local skills directory.
2. Clone the repo and symlink or copy the skill into your agent workspace.
3. Add the repo as a git submodule if you manage shared skills centrally.
4. Install it through your internal provisioning or packaging workflow.
5. Download the folder directly from GitHub and place it in your skills collection.

## Source

- [Agent Skill Exchange](https://agentskillexchange.com/skills/keybert-keyword-extraction-bert/)