Highlights

Viewer MCP

We've added a MCP that allows an llm agent to see and interact with the Viewer! You could ask your agent to

• verify its work looks as expected in the Viewer.
• debug a bug when something doesn't show up right.
• explore a recording or dataset to search for specific patterns.

The agent has full control over the Viewer, meaning it can see and click any widget.

Here's an example where Claude Sonnet was asked to create a fancy particle animation of the Rerun logo and verify its work using the mcp in the open Viewer (sped up by a lot, except when showing the end result):

https://github.com/user-attachments/assets/14ffe7ed-6000-4193-900c-627784682125

Once it wrote the script, it logged the recording to the Viewer, and then iterated until the result looked as requested. It adjusted the camera position, improved the particle rendering by looking at different frames in the animation, and then debugged why the fade out animation was still showing particles on the last frame.

/goal Create a new rerun python example in this folder that uses reruns 2D shapes to recreate the rerun logo (rerun-wordmark-black.svg). There should be a nice fade-in animation in the beginning, 10 frames duration. Then pause a bit with the full rerun logo visible and then the shapes should explosively fade away with a 20 frame animation before the recording ends.

You may only stop once the recreated logo in the viewer looks close to the provided svg (black text, white background). Use the mcp to verify in the open viewer, don't ever kill it. Once done, launch an opus agent and ask it to judge how closely it looks to the original image. Keep going until it's convinced that it looks close.

See our mcp docs to get started.

Learning course

https://rerun.io/learn is a great way to learn how the Rerun data model covers the full physical AI experiment loop. It is a short, hands-on course for robotics ML engineers who want the full robot learning data loop in one place:

raw data -> RRD -> derived layers -> dataset queries -> training -> evaluation

Rerun agent skills

We added new skills to the Rerun repo to make it easier to investigate existing robotics data with Rerun. You can install the skills in your project via:

npx skills add rerun-io/rerun

The new learning course also shows how these agent skills can be used to collect, refine and train with robotics data.

VoxelGridMap archetype

Rerun now supports sparse voxel grids through a new VoxelGridMap archetype (thanks to @makeecat for the contribution!). The archetype supports sparse indexing, anisotropic voxel sizes, pose offsets, and optional explicit colors or values & colormap per voxel.

Rerun's MCAP importer now also converts the dense ROS nav2_msgs/VoxelGrid and Foxglove VoxelGrid formats to Rerun VoxelGridMap.

And if you wonder how the smooth 3D navigation through the voxel scene in this video was done, see below!

https://github.com/user-attachments/assets/87fb80da-66dd-4fcd-8b35-ab553696f536

🎮 Gamepad support in 3D views

You can now use a gamepad to navigate 3D views in the native viewer. This makes it easier to do fine-grained, complex maneuvers with varying speed - e.g. for navigating large scenes or for screen videos. Analog sticks control the eye position and look target, shoulder triggers move the eye up and down, and shoulder buttons accelerate/decelerate.

Note: The gamepad feature is currently experimental and can be activated through the settings menu. Switch the 3D view's eye controls to FirstPerson for optimal experience. Under the hood, we use the gilrs crate that supports a wide range of devices.

Drag & drop components

You can now drag & drop a component right from the streams panel to visualize it in a Time series view or Status timeline.

https://github.com/user-attachments/assets/d70587a9-2020-4ae8-9cf3-0fef54dcf896

Transform debugging tool

We added a new debugging UI for visual introspection of the 3D transform cache. This allows to view the tree structure of the transform hierarchy, including potentially disconnected trees, and inspect the latest stored values of each frame node or transform edge. The UI supports horizontal and vertical tree layout and you can filter by transform type (e.g. static or temporal).

Note: this UI is currently a tab in the dev panel (accessible via "Toggle dev panel" in the menu or ctrl/cmd+shift+m). But we are open to making this a dedicated view in the future - let us know if you have any feedback!

https://github.com/user-attachments/assets/b4b1ea6e-bce9-4e88-9ede-262f545e3b47

Breaking changes

log_tick no longer logged by default; log_time can be disabled

The SDK no longer injects the log_tick timeline column into logged data by default. The log_time timeline is still injected by default, but can now be disabled.

The initial defaults are controlled by environment variables, read once on first use:

They can also be toggled at runtime, either on the active recording or on a specific RecordingStream:

import rerun as rr

rr.set_log_tick_enabled(True)  # opt in to `log_tick` on the active recording
rr.set_log_time_enabled(False)  # opt out of `log_time` on the active recording

rec = rr.RecordingStream("rerun_example_my_app")
rec.set_log_tick_enabled(True)  # …or on a specific recording

If you relied on the log_tick timeline being present, set RERUN_LOG_TICK=1 (or call set_log_tick_enabled(true)) to restore the old behavior.

rerun.recording module removed

The rerun.recording module — Recording, RRDArchive, load_recording, load_archive — has been removed, having been deprecated in 0.32. The related rr.send_recording(), RecordingStream.send_recording(), Recording.from_chunks(), and DatasetEntry.download_segment() are removed as well.

Use rerun.experimental.RrdReader instead. See the 0.32 migration guide for more details.

Remove embedded base64-encoded table blueprints & replace with blueprint registration

Table blueprints are no longer read from the Arrow schema metadata key rerun:table_blueprint. If you previously stored base64:… encoded .rbl bytes in table metadata, export that blueprint as a regular .rbl file and register it with TableEntry.register_blueprint(...) instead. Tables without a registered blueprint fall back to Arrow field metadata and viewer heuristics.

DatasetEntry.manifest() deprecated

DatasetEntry.manifest() was always intended for internal and debugging use only and should never have been part of the public API. It is now marked @deprecated and will be removed in a future release. No public replacement is offered.

Remove previously deprecated SDK methods for custom indices

The DatasetEntry methods create_fts_search_index, create_vector_search_index, delete_search_indexes, search_fts, and search_vector have been removed, having been deprecated in 0.31.

This change does not impact your ability to search through your dataset via dataframe queries.

rr.send_dataframe is now stricter and built on Chunk.from_record_batch

rr.send_dataframe / rr.send_record_batch are now thin wrappers over the new rerun.experimental.Chunk.from_record_batch (and Chunk.from_dataframe), which turns an Arrow record batch into one chunk per entity path. This makes the Arrow → chunk interpretation a first-class, well-specified capability, but it changes a few behaviors that previously happened silently.

Consequently, the following breaking behavior changes are introduced:

• A batch with no index column now raises instead of silently logging static data. Opt in for a static chunk explicitly with index=None for static, or specify a column to use as index with index=<column> for temporal chunk.
• Entity-path recognition from a column name now requires a leading /. Names without it are no longer parsed for an entity path: foo and foo:bar previously became the entity /foo, and now land on the root entity / as components. Only /entity:component names are split. (Column names emitted by the Rerun SDK always have the / prefix.)
• As a consequence, property:… columns now land on the root entity / rather than an entity named property. Neither map back to /__properties — proper handling of this is not yet implemented.
• component_type is no longer defaulted to the literal "Unknown" when absent; it is left unset.

ParquetReader column rules removed in favor of lenses

rerun.experimental.ParquetReader no longer accepts the column_rules parameter, and the ColumnRule class has been removed. ParquetReader is now a pure reader — it turns raw parquet columns into grouped, time-indexed chunks of struct/scalar components. Mapping those struct fields into Rerun archetypes is now done with lenses on the reader's .stream().

SaveScreenshot gRPC endpoint moved to new ViewerControlService

Previously, the SaveScreenshot gRPC endpoint was part of the MessageProxyService.

Looking for an older release? See the migration guides for 0.33 and earlier.