$npx skillfedfor your agent

bluesky

Experiment specification & orchestration.

With conditionsPyPI Scientific/EngineeringReleased May 202692.3K downloads / mopermissive licensePure Python

Decision gist · record as of 2026-08-14

pure-Python wheel — bluesky-1.15.1-py3-none-any.whl
v1.15.1 · released 2026-05-06 · Python >=3.10 · 10 runtime deps: cycler, event-model, historydict, msgpack, msgpack-numpy, numpy, opentelemetry-api, toolz

Yes, if you are building or automating scientific experiments at a facility or lab scale and need hardware-agnostic experiment specification, live data streaming, and reproducibility guarantees. The active maintenance, low install friction, permissive license, and integration with standard scientific Python packages make it a solid foundation. The Alpha status and lack of known vulnerabilities are not blockers, but verify that the library's maturity and feature set align with your facility's production requirements before committing to it as a core dependency.AI-flagged interpretation of the facts on this page — verify before relying

Before you install

  • Requires Python 3.10 or later.
  • Bluesky is designed for integration with hardware control systems and scientific instruments; standalone use requires appropriate detector and motor objects.
  • Low install friction with a pure Python wheel.

License · maintenance · safety

permissive license (permissive) — BSD 3-Clause License (permissive). You may use, modify, and distribute bluesky freely in commercial and private projects, provided you retain the copyright notice and disclaimer. No copyleft obligations.

last release 2026-05-06 (100 days) · last repo commit 2026-08-13 · 238 stars

0 known vulnerabilities (OSV.dev, 2026-08-14) · 92,288 downloads/mo, #13,461 on PyPI

Verify before relying

pip install bluesky

from bluesky import RunEngine
from bluesky.plans import scan

RE = RunEngine({})
# Define and execute an experiment plan
RE(scan([detector], motor, start, stop, num_points))
  • Specific hardware platforms or control systems that bluesky officially supports or integrates with.
  • Whether the interruption recovery and suspend/resume features work with all experiment types or have limitations.
  • Performance characteristics or scalability limits for large-scale or high-frequency data collection.
  • Maturity of the Alpha status (Development Status :: 3) relative to production use in scientific facilities.
Same gist for agents: .md · .json

What it is and what it does

Bluesky is a Python library for specifying and running scientific experiments with automatic orchestration, live data streaming, and rich metadata capture. It abstracts experiment logic from hardware details, allowing the same procedure to run on different instruments without modification. The library emphasizes reproducibility, interruption recovery (experiments can be paused and resumed cleanly), and integration with the scientific Python stack (numpy, msgpack for serialization, tqdm for progress, toolz for functional utilities, and opentelemetry-api for observability).

Typical use involves defining an experiment as a plan—a generator-based specification of detector readings, motor movements, and data collection steps—then submitting it to a RunEngine that handles execution, live data emission, metadata organization, and pluggable I/O backends. This design separates experiment intent from hardware control, enabling reuse across labs and facilities. The library targets scientific facilities and lab-bench automation where reproducibility, traceability, and unattended operation are priorities.

Use it for

  • Define a synchrotron beamline scan procedure once and reuse it across different beamlines with different hardware.
  • Stream detector data live during an experiment for real-time visualization and early stopping decisions.
  • Automatically suspend a long-running materials characterization experiment if a resource becomes unavailable, then resume from the checkpoint.
  • Capture and organize experimental metadata (sample ID, conditions, operator, timestamps) alongside raw data for reproducibility audits.
  • Export live experimental data to multiple formats or databases simultaneously via pluggable I/O handlers.
  • Integrate custom laboratory procedures and commands while inheriting interruption recovery and metadata management for free.

Worth the install?

AI-flagged interpretation of the facts on this page. Verify before relying on it.

With conditions

Yes, if you are building or automating scientific experiments at a facility or lab scale and need hardware-agnostic experiment specification, live data streaming, and reproducibility guarantees.

The active maintenance, low install friction, permissive license, and integration with standard scientific Python packages make it a solid foundation. The Alpha status and lack of known vulnerabilities are not blockers, but verify that the library's maturity and feature set align with your facility's production requirements before committing to it as a core dependency.

Install

bluesky on PyPI

Before you install

Low install friction with a pure Python wheel. Active maintenance with a recent commit on 2026-08-13 and a release 100 days ago. Ten runtime dependencies are all standard scientific Python packages (numpy, msgpack, tqdm, toolz, opentelemetry-api, and others), suggesting a stable, well-integrated ecosystem.

Requires Python 3.10 or later. Bluesky is designed for integration with hardware control systems and scientific instruments; standalone use requires appropriate detector and motor objects.

License in practice

BSD 3-Clause License (permissive). You may use, modify, and distribute bluesky freely in commercial and private projects, provided you retain the copyright notice and disclaimer. No copyleft obligations.

Quickstart

pip install bluesky

from bluesky import RunEngine
from bluesky.plans import scan

RE = RunEngine({})
# Define and execute an experiment plan
RE(scan([detector], motor, start, stop, num_points))

Verify before relying

  • Specific hardware platforms or control systems that bluesky officially supports or integrates with.
  • Whether the interruption recovery and suspend/resume features work with all experiment types or have limitations.
  • Performance characteristics or scalability limits for large-scale or high-frequency data collection.
  • Maturity of the Alpha status (Development Status :: 3) relative to production use in scientific facilities.

Package facts

Licensepermissive license permissive
Python supportSupports the current Python release >=3.10
Install frictionLow. Pure-Python wheel
Runtime dependencies
10 packages
cyclerevent-modelhistorydictmsgpackmsgpack-numpynumpyopentelemetry-apitoolztqdmtyping-extensions
MaintenanceActively maintained 100 days since the last release
Last repo commit
First released
Downloads92,288 / month, #13,461 on PyPI 30-day window, as of 2026-08-14
Known vulnerabilitiesNone known OSV.dev, checked 2026-08-14
Classifiers
Development Status :: 3 - AlphaLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13

Evidence: bluesky-1.15.1-py3-none-any.whl

Tags

Capabilities
experiment control and orchestrationscientific data collection frameworklab automation and experiment managementstreaming scientific data processingreproducible experiment specificationhardware-agnostic experiment procedureslive data visualization and collection
Topics
experiment-orchestrationscientific-data-collectionlab-automation

Let your AI agent find packages like this

Example. Real query, live index.

You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.

wish › “scientific data collection framework”

  • blueskyBluesky is an experiment control and scientific data collection…
  • qcodesQCoDeS is a Python data acquisition and control framework for systems…
  • gamma-pytoolsA collection of machine learning and visualization utilities…

Give your agent the search over MCP, or paste the wish link into any chat.

More Scientific/Engineering packages

numpy Worth it
PyPI · Software Development · released Aug 2026

NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.

BSD-3-Clause AND 0BSD AND MIT AND Zlib AND CC0-1.0compiled wheel · 3.12+
1.1Bdownloads / mo
pandas Worth it
PyPI · Scientific/Engineering · released Jul 2026

pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.

BSD-3-Clausecompiled wheel · 3.11+
769.1Mdownloads / mo
scipy Worth it
PyPI · Libraries · released Jun 2026

scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.

BSD-3-Clausecompiled wheel · 3.12+
449.0Mdownloads / mo
scikit-learn Worth it
PyPI · Software Development · released Jun 2026

scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.

Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.

BSD-3-Clausecompiled wheel · 3.11+
235.5Mdownloads / mo
dill Worth it
PyPI · Software Development · released Jan 2026

dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.

BSD-3-Clausepure Python · 3.9+
208.1Mdownloads / mo
multiprocess Worth it
PyPI · Software Development · released Jan 2026

Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.

Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.

BSD-3-Clausepure Python · 3.9+
202.7Mdownloads / mo

See also atproto · event-model · ophyd · ophyd-async · qiskit-experiments · pydoe · clearml · yacs · dvc-studio-client · dora-search