rerun-sdk
The Rerun Logging SDK
Decision gist · record as of 2026-08-14
Yes, if you work with visual or multimodal scientific data and need live or replay-based inspection. The SDK is actively maintained, has no known vulnerabilities, and offers a permissive license. Medium install friction is typical for compiled packages; the Rust backend provides performance benefits for real-time logging. Suitable for research, prototyping, and production monitoring in computer-vision and ML workflows.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; compiled wheels available for macOS (arm64), Linux (aarch64, x86_64), and Windows (x86_64).
- Medium install friction due to compiled wheels for multiple platforms (arm64, x86_64, Windows).
- Active maintenance with recent release (4 days old) and 11298 repository stars.
License · maintenance · safety
MIT OR Apache-2.0 (permissive) — Dual-licensed under MIT or Apache-2.0, both permissive licenses allowing commercial and private use with minimal restrictions.
last release 2026-08-10 (4 days) · last repo commit 2026-08-14 · 11,298 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 3,002,062 downloads/mo, #2,791 on PyPI
Alternatives
Verify before relying
pip install rerun-sdk
import rerun as rr
import numpy as np
rr.init("app_name", spawn=True)
positions = np.random.rand(10, 3)
rr.log("points", rr.Points3D(positions))- Whether the Viewer can be run headless or requires a display for server mode.
- Performance characteristics when logging high-frequency or large-volume data streams.
- Memory overhead of maintaining live connections between SDK and Viewer processes.
What it is and what it does
Rerun is a Python SDK for recording and visualizing multimodal scientific and computer-vision data. It captures images, tensors, point clouds, text, and other structured data, streaming them to an interactive Viewer for live inspection or saving to disk for later replay. The SDK is built on Rust and compiled to native wheels, giving it performance suitable for real-time logging in data pipelines.
Typical workflows involve initializing a Rerun session, logging data with typed constructors like Points3D or Tensor, and viewing results either in-process (spawn=True) or by connecting to a separate Viewer server running on the same or different machine. The package depends on numpy, pillow, pyarrow, attrs, psutil, and typing-extensions, making it suitable for scientific Python environments.
Use it for
- Log and inspect 3D point clouds and geometric data during computer-vision algorithm development.
- Record image sequences with annotations for debugging and validating ML model outputs.
- Stream tensor data and metrics from training loops for real-time monitoring.
- Capture and replay multimodal sensor data for robotics or autonomous systems.
- Visualize structured scientific data for exploratory analysis.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work with visual or multimodal scientific data and need live or replay-based inspection.
The SDK is actively maintained, has no known vulnerabilities, and offers a permissive license. Medium install friction is typical for compiled packages; the Rust backend provides performance benefits for real-time logging. Suitable for research, prototyping, and production monitoring in computer-vision and ML workflows.
Install
rerun-sdk on PyPI
Before you install
Medium install friction due to compiled wheels for multiple platforms (arm64, x86_64, Windows). Active maintenance with recent release (4 days old) and 11298 repository stars. Requires Python 3.10 or later.
Requires Python 3.10 or later; compiled wheels available for macOS (arm64), Linux (aarch64, x86_64), and Windows (x86_64).
License in practice
Dual-licensed under MIT or Apache-2.0, both permissive licenses allowing commercial and private use with minimal restrictions.
Quickstart
pip install rerun-sdk
import rerun as rr
import numpy as np
rr.init("app_name", spawn=True)
positions = np.random.rand(10, 3)
rr.log("points", rr.Points3D(positions))Verify before relying
- Whether the Viewer can be run headless or requires a display for server mode.
- Performance characteristics when logging high-frequency or large-volume data streams.
- Memory overhead of maintaining live connections between SDK and Viewer processes.
Package facts
| License | MIT OR Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 6 packagesattrsnumpypillowpsutilpyarrowtyping-extensions |
| Maintenance | Actively maintained 4 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 3,002,062 / month, #2,791 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaProgramming Language :: Python :: Implementation :: CPythonProgramming Language :: Python :: Implementation :: PyPyProgramming Language :: RustTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: Visualization |
Evidence: rerun_sdk-0.36.0-cp310-abi3-macosx_11_0_arm64.whl; rerun_sdk-0.36.0-cp310-abi3-manylinux_2_28_aarch64.whl; rerun_sdk-0.36.0-cp310-abi3-manylinux_2_28_x86_64.whl; rerun_sdk-0.36.0-cp310-abi3-win_amd64.whl
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