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Beta Frame Map projects every indexed frame in your library onto a 2D canvas, positioned by visual similarity — frames that look alike land close together. It’s a way to browse a library visually instead of only through query search.

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In the app

Frame Map is available at platform.pureframe.ai/framemap.
  • Hover a dot for a frame preview and video name
  • Click a dot to pin it to the preview strip at the bottom
  • Double-click to open the video at that timestamp
  • Lasso select a region to inspect multiple frames at once
  • Click a cluster in the sidebar to pin every frame in that cluster at once
  • Filter by collection using the left panel
Frames are grouped into clusters automatically (HDBSCAN), each representing a visual theme — all your outdoor shots, all your whiteboard moments. Frames that don’t fit any cluster show as uncategorized. Cluster quality improves with library size; at least 50 indexed videos is recommended for meaningful groupings.

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API access

Omit collection_id to compute the map across your whole account. The result is cached per scope (per collection, or globally) — repeat calls return the cached map instantly. Pass force=true to recompute after uploading new videos, which re-scans every frame vector and re-clusters from scratch, so it’s slower and costs more than a cached read. The Python SDK exposes this as client.frame_map.get_framemap(...).