Embedding Atlas
Embedding Atlas is an open-source, scalable visualization tool for embedding vectors and metadata that enables interactive exploration, cross-filtering, search, and multimodal data viewing at scale.
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Quick Overview
Best for: Research
What it does
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Best fit
Research
Pricing snapshot
Freemium from Free (MIT license)
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Embedding Atlas is a scalable, interactive visualization system for embedding vectors and associated metadata. It provides interactive navigation of data structure with automatic clustering and labeling, real-time search and nearest-neighbor queries, multimodal viewers (text, image, audio, numeric, categorical, and time), and linked dashboards with cross-filtering. The tool targets users who need to explore, analyze, and build visual dashboards over large embedding datasets and supports high-performance rendering (WebGPU) and AI agent access via MCPAI and the Model Context Protocol.
Embedding Atlas is a tool that provides interactive visualizations for large embeddings, allowing users to visualize, cross-filter, and search embeddings and metadata. It is open sourced by Apple.
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Automatic data clustering & labeling
Interactively visualize and navigate overall data structure with automatic clustering and labeling to surface patterns in embeddings.
Real-time search & nearest neighbors
Find similar data to a given query or existing data point using built-in real-time search and nearest-neighbor capabilities.
Smooth rendering at scale (WebGPU)
Render up to a few million points with density contours, powered by WebGPU for smooth, high-performance visualization.
Linked dashboards & cross-filtering
Arrange charts and configure cross-filtering between them; compose custom charts via a chart spec to create interactive dashboards.
Multimodal data support
Built-in viewers for text, image, audio, numeric, categorical, and time columns to explore heterogeneous datasets.
AI agent access (MCPAI)
MCPAI agents can query, chart, and explore data via the Model Context Protocol to enable AI-driven analysis.
Pricing
Released under the MIT license; available free as open-source software.
Open-source
Free (MIT license)- Source code and documentation available under the MIT license
- No usage fees listed on the site
Use Cases
Exploratory analysis of embedding datasets
Interactively navigate and cluster embeddings to discover structure, outliers, and relationships in high-dimensional data.
Nearest-neighbor search and similarity lookup
Perform real-time similarity search to find items similar to a query or an existing example.
Dashboarding and cross-filtered analytics
Compose linked charts and configure cross-filtering to build interactive dashboards for analysis and presentation.
Multimodal dataset inspection
View and inspect text, image, audio, numeric, categorical, and time columns alongside embeddings to contextualize results.
AI-assisted exploration
Allow MCPAI agents to query and chart data via Model Context Protocol for programmatic or agent-driven workflows.
Integrations
MCPAI agents / Model Context Protocol
AI agent access via MCPAI agents can query, chart, and explore data via the Model Context Protocol.
WebGPU
Uses WebGPU for high-performance rendering and density contours at scale.
Chart spec
Compose custom charts via a chart spec to integrate with the linked dashboards and cross-filtering system.
Benefits
Limitations
Frequently Asked Questions
No verified FAQs are available.
Getting Started
- 1 Open the project's Documentation page (Docs) on the site to read setup and usage instructions.
- 2 Use the Load Data examples and guides to import your embeddings and metadata into Embedding Atlas.
- 3 Try the provided Examples and compose charts via the chart spec to build linked dashboards and enable cross-filtering.
Support
docs
Documentation available on the site (Docs) for setup, examples, and usage guidance.
examples
Examples section on the site to try pre-built demos and learn workflows.
load_data
Load Data guidance on the site to help ingest embeddings and metadata.
API
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