Embedding Atlas

Embedding Atlas is a tool that provides interactive visualizations for large embeddings and their metadata. You can visualize, cross-filter, and search across your data.

For embeddings

• 🏷️ Automatic data clustering & labeling:

Interactively visualize and navigate overall data structure.

• 🫧 Kernel density estimation & density contours:

Easily explore and distinguish between dense regions of data and outliers.

• 🧊 Order-independent transparency:

Ensure clear, accurate rendering of overlapping points.

• 🔍 Real-time search & nearest neighbors:

Find similar data to a given query or existing data point.

• 🚀 Smooth performance at scale:

Up to a few million points, powered by WebGPU.

For any tabular data

• 📊 Linked dashboards & cross-filtering:

Standard chart types (bar, line, bubble, count plot, eCDF) plus a composable chart spec for building custom charts like heatmaps and average-line overlays. Charts can be configured to cross-filter.

• 🧩 Multimodal data support:

Built-in viewers for text, image, audio, numeric, categorical, and time columns.

• 🤖 AI agent access via MCP:

AI agents can query the schema, run SQL, create charts, and capture screenshots via Model Context Protocol.

Please visit for a demo and documentation.

Get started

To use Embedding Atlas with Python:

pip install embedding-atlas
embedding-atlas <your-dataset>

In addition to the command line tool, Embedding Atlas is available as a Python Notebook (e.g., Jupyter) widget:

from embedding_atlas.widget import EmbeddingAtlasWidget
Show the Embedding Atlas widget for your data frame:
EmbeddingAtlasWidget(df)

Finally, components from Embedding Atlas are also available in an npm package:

npm install embedding-atlas

import { EmbeddingAtlas, EmbeddingView } from "embedding-atlas";
// or with React: import { EmbeddingAtlas, EmbeddingView } from "embedding-atlas/react";
// or Svelte: import { EmbeddingAtlas, EmbeddingView } from "embedding-atlas/svelte";

For more information, please visit .

BibTeX

For the Embedding Atlas tool:

@misc{ren2025embedding,
  title={Embedding Atlas: Low-Friction, Interactive Embedding Visualization},
  author={Donghao Ren and Fred Hohman and Halden Lin and Dominik Moritz},
  year={2025},
  eprint={2505.06386},
  archivePrefix={arXiv},
  primaryClass={cs.HC},
  url={https://arxiv.org/abs/2505.06386},
}

For the algorithm that automatically produces clusters and labels in the embedding view:

@misc{ren2025scalable,
  title={A Scalable Approach to Clustering Embedding Projections},
  author={Donghao Ren and Fred Hohman and Dominik Moritz},
  year={2025},
  eprint={2504.07285},
  archivePrefix={arXiv},
  primaryClass={cs.HC},
  url={https://arxiv.org/abs/2504.07285},
}

Development

For development instructions, please visit , or check out packages/docs/develop.md.

License

This code is released under the MIT license.