rerun

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  <a href="https://www.rerun.io/">
    <img width="1000" height="200" alt="Banner with Rerun logo" src="https://static.rerun.io/d0f5443d4803cac65c73fcc064936c09f5e7f208_rerun_banner.png" />

  </a>
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  <a href="https://pypi.org/project/rerun-sdk/">                        <img alt="PyPi"           src="https://img.shields.io/pypi/v/rerun-sdk.svg">                              </a>
  <a href="https://crates.io/crates/rerun">                             <img alt="crates.io"      src="https://img.shields.io/crates/v/rerun.svg">                                </a>
  <a href="https://github.com/rerun-io/rerun/blob/main/LICENSE-MIT">    <img alt="MIT"            src="https://img.shields.io/badge/license-MIT-blue.svg">                        </a>
  <a href="https://github.com/rerun-io/rerun/blob/main/LICENSE-APACHE"> <img alt="Apache"         src="https://img.shields.io/badge/license-Apache-blue.svg">                     </a>
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# The data layer for physical AI

Log, query, visualize, and stream to training on shared columnar storage built for multimodal data.

**What it does:** Rerun ingests multi-rate, multimodal data (images, point clouds, transforms, time series, joint states, video) from many sources and formats (robot logs, human-data rigs, sim, web video; MCAP, rrd, LeRobot). The built-in viewer renders everything in sync, in realtime: scrub episodes, compare sensors side-by-side, watch CV pipelines run live. The same data is queryable with [dataframes](https://rerun.io/docs/howto/query-and-transform/get-data-out) or SQL, and streams directly into training. Built in Rust on column-chunk storage purpose-built for multi-rate physical data. SDKs in Python, Rust, and C++.

**Quickstart:** `pip install rerun-sdk` — log your first multimodal data and see it in the viewer in under 2 minutes.

* [Run the Rerun Viewer in your browser](https://www.rerun.io/viewer)
* [Read about what Rerun is and who it is for](https://www.rerun.io/docs/overview/what-is-rerun)

### Use cases
- Ingest robot logs, egocentric/UMI rigs, sim, and web video into one substrate
- Run CV pipelines (SLAM, hand tracking, motion retargeting) as table edits
- Query raw, intermediate, and derived data with dataframes or SQL
- Visualize multi-rate, multimodal sequences across the pipeline
- Stream dataset mixes directly to training — no export jobs, no stale copies

### Data types
Multi-rate, multimodal, spatial: images, point clouds, time series, tensors, transforms, joint states, video. Preserved end-to-end.

### A short taste
```py
import rerun as rr  # pip install rerun-sdk

rr.init("rerun_example_app")

rr.spawn()  # Spawn a child process with a viewer and connect
# rr.save("recording.rrd")  # Stream all logs to disk
# rr.connect_grpc()  # Connect to a remo