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rerun-io/rerun

10,690RustAudience · researcherComplexity · 3/5LicenseSetup · moderate

TLDR

Rerun lets you log and replay multimodal data streams, camera feeds, sensors, 3D point clouds, over a timeline, making it easy to debug robotics and computer vision systems.

Mindmap

mindmap
  root((rerun))
    What it does
      Log multimodal data
      Replay over time
      Debug pipelines
    Data types
      Camera feeds
      3D point clouds
      Sensor readings
    SDKs
      Python via pip
      Rust native
      C++ library
    Use cases
      Robotics debugging
      CV research
      ML dataset export
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Code map

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Things people build with this

USE CASE 1

Replay a robot's sensor data, camera feed, and decision logs side-by-side on a timeline to debug unexpected behavior.

USE CASE 2

Log 3D point clouds and object detections from a computer vision pipeline and scrub through frames to find where detection fails.

USE CASE 3

Extract clean training datasets from recorded sessions using the dataframe API.

USE CASE 4

Stream live data from a Python robotics script into the Rerun viewer for real-time monitoring.

Tech stack

RustPythonC++pip

Getting it running

Difficulty · moderate Time to first run · 30min

Python pip install includes the viewer binary, C++ and Rust users must install the Rerun viewer separately.

Dual MIT and Apache 2.0 license, use freely for any purpose, including commercially.

In plain English

Rerun is a tool for logging and visualizing multimodal data over time. It is aimed at engineers and researchers working in areas like robotics, computer vision, simulation, and any field where you need to track many different data streams, such as camera feeds, sensor readings, 3D point clouds, and text, all at once and across time. The core idea is simple: your code sends data to Rerun as your program runs, and Rerun records and displays it in a visual viewer. You can watch the data live as it streams in, or save it to a file and review it later. The viewer lets you scrub through time, compare what different sensors saw at the same moment, and zoom into specific frames to understand what happened and why. A practical example from the README is a robot vacuum that keeps hitting walls. A normal debugger would show you code lines, a text log would show you messages. Neither tells you what the robot actually saw. Rerun lets you replay the camera feed, the depth sensor, the map the robot built, the objects it detected, and its confidence scores, all synced to the same timeline. That makes it far easier to spot whether a glare on a camera lens, a bad sensor reading, or a code bug was the culprit. SDKs are available for Python, C++, and Rust, so you can drop Rerun into an existing project with a few lines. The Python version installs with pip and includes the viewer binary. C++ and Rust users install the viewer separately. There is also a dataframe API for extracting clean datasets from your recordings, which is useful for training machine learning models on data you already collected. The project is actively developed and the API is still changing, so breaking changes are expected. The team notes that the viewer can slow down with very large numbers of entities or extremely dense point clouds. It is open source under MIT and Apache licenses.

Copy-paste prompts

Prompt 1
I have a Python robotics script that captures camera frames and sensor readings. Show me how to log them to Rerun so I can visualize them on a shared timeline.
Prompt 2
How do I save a Rerun recording to a .rrd file and then open it later for offline review?
Prompt 3
Using the Rerun dataframe API in Python, extract all logged 3D bounding boxes from a recording file into a pandas DataFrame.
Prompt 4
Set up a Rerun viewer in a C++ project that logs lidar point clouds at 10 Hz.
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