# Rerun: Open Source SDK for Multimodal Data Logging and Visualization This repository profile is provided by osrepos.com, an open source repository discovery platform. Source: osrepos.com Repository profile: https://osrepos.com/repo/rerun-io-rerun Generated for open source discovery and AI-assisted research. Rerun is an open-source SDK designed for logging, storing, querying, and visualizing complex multimodal and multi-rate data. It provides SDKs for C++, Python, and Rust, enabling developers to stream data to a powerful viewer for live visualization or later analysis. This tool is particularly valuable for debugging and understanding systems in robotics, computer vision, and spatial AI. GitHub: https://github.com/rerun-io/rerun OSRepos URL: https://osrepos.com/repo/rerun-io-rerun ## Summary Rerun is an open-source SDK designed for logging, storing, querying, and visualizing complex multimodal and multi-rate data. It provides SDKs for C++, Python, and Rust, enabling developers to stream data to a powerful viewer for live visualization or later analysis. This tool is particularly valuable for debugging and understanding systems in robotics, computer vision, and spatial AI. ## Topics - computer-vision - multimodal - robotics - rust - visualization - data-logging - sdk - debugging ## Repository Information Last analyzed by OSRepos: Fri Jan 02 2026 00:01:06 GMT+0000 (Western European Standard Time) Detail views: 5 GitHub clicks: 9 ## Safety Notice OSRepos shares public repositories for knowledge and discovery only. Review source code, dependencies, licenses, and security implications before running or installing anything. ## Content ## Introduction Rerun is an open-source SDK developed by rerun-io, providing a comprehensive solution for logging, storing, querying, and visualizing multimodal and multi-rate data. It is built to help developers understand and improve complex processes involving rich data types like 2D, 3D, text, time series, and tensors. Rerun offers SDKs for C++, Python, and Rust, allowing seamless integration into various projects, with data streamed to a dedicated Rerun Viewer for live visualization or saved to file for post-mortem analysis. This powerful tool finds its applications across diverse fields such as robotics, spatial and embodied AI, generative media, industrial processing, simulation, security, and health, making it an essential asset for anyone dealing with sensor data or evolving 2D/3D states over time. ## Installation Getting started with Rerun is straightforward. You can integrate the SDK into your projects using your preferred language: * **C++**: Follow the quick start guide on the [Rerun website](https://www.rerun.io/docs/getting-started/quick-start/cpp "Rerun C++ Quick Start" target="_blank"). * **Python**: Install via pip with `pip install rerun-sdk` or through [conda](https://github.com/conda-forge/rerun-sdk-feedstock "Rerun Conda Feedstock" target="_blank"). Find more details in the [Python quick start guide](https://www.rerun.io/docs/getting-started/quick-start/python "Rerun Python Quick Start" target="_blank"). * **Rust**: Add to your project with `cargo add rerun`. Refer to the [Rust quick start guide](https://www.rerun.io/docs/getting-started/quick-start/rust "Rerun Rust Quick Start" target="_blank") for more information. To visualize your logged data, you'll also need the `rerun` binary viewer. For Python users, it's bundled with `pip install rerun-sdk`. For C++ and Rust, you can install it separately using `cargo install rerun-cli --locked --features nasm`. Note that the `nasm` feature requires the `nasm` CLI to be installed for optimal video decoding performance. ## Examples Here's a quick taste of how to use the Rerun Python SDK to log 3D points: python 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 remote viewer # Associate subsequent data with 42 on the “frame” timeline rr.set_time("frame", sequence=42) # Log colored 3D points to the entity at `path/to/points` rr.log("path/to/points", rr.Points3D(positions, colors=colors)) # ... more logging ... This snippet initializes Rerun, spawns a viewer, sets a time context, and logs 3D point data, demonstrating the simplicity of integrating Rerun into your data pipelines. ## Why Use Rerun Rerun excels as a visual and temporal debugger for complex systems. Imagine a vacuum cleaning robot that keeps hitting walls. Traditional debuggers or simple text logs would fall short in explaining why. Rerun allows you to log and visualize all the robot's internal representations of the world, such as RGB camera feeds, depth images, lidar scans, segmentation images, 3D maps, detected objects, and confidence levels. By observing how these diverse data streams evolve over time, you can pinpoint the exact moment and reason for failures. This could reveal issues like sensor glare confusing a segmentation network, a bug in lidar code, or odometry errors. Beyond debugging, Rerun helps in improving algorithms, setting up new test cases, and creating datasets for model training and evaluation through its query APIs. It transforms how you understand and iterate on systems that rely on multimodal, time-series data. ## Links Explore Rerun further with these official resources: * [High-level Documentation](http://rerun.io/docs "Rerun High-level Documentation" target="_blank") * [Loggable Types Reference](https://www.rerun.io/docs/reference/types "Rerun Loggable Types" target="_blank") * [Examples](http://rerun.io/examples "Rerun Examples" target="_blank") * [C++ API Documentation](https://ref.rerun.io/docs/cpp "Rerun C++ API Docs" target="_blank") * [Python API Documentation](https://ref.rerun.io/docs/python "Rerun Python API Docs" target="_blank") * [Rust API Documentation](https://docs.rs/rerun/ "Rerun Rust API Docs" target="_blank") * [GitHub Repository](https://github.com/rerun-io/rerun "Rerun GitHub Repository" target="_blank")