OpenHCS

Open-source bioimage workflow platform for high-content screening
Typed compilation · GUI ↔ Python · CellProfiler import · Napari/Fiji/OMERO · MCP agents

Install

Windows installer · macOS installer · Installation options

The graphical installers set up an isolated CPU-safe desktop environment with the OpenHCS GUI, CellProfiler compatibility, local MCP server, Napari, Fiji/ImageJ, and Bio-Formats. GPU libraries remain optional.

See OpenHCS in use

Browse the UI and viewer gallery · Watch an agent build, debug, run, and inspect a workflow

OpenHCS processes large microscopy datasets with a compile-then-execute architecture. Pipelines are validated across the selected execution axes before processing starts, preventing late failures after expensive work. Design pipelines in the GUI, export to Python, edit as code, and re-import — switching between visual and programmatic workflows. The local MCP exposes that same workflow model to supported agents, so agent-authored pipelines remain visible, editable, and reviewable in the GUI and generated Python.

graph LR
    subgraph Sources
        IX[ImageXpress]
        OP[Opera Phenix]
        BF[Bio-Formats]
        OM[OMERO]
    end

    subgraph OpenHCS Platform
        PD["Pipeline Designer<br/>(GUI ⇄ Code ⇄ Agent)"]
        CO["Typed Compiler<br/>(resolve + validate)"]
        EX["Bounded Worker Executor<br/>(well scheduling · multi-GPU)"]
        FN["Registry-Discovered Functions<br/>scikit-image · CuPy · pyclesperanto<br/>PyTorch · JAX · TF · CuCIM · custom"]
        PS["PolyStore<br/>(Memory ↔ Disk ↔ Zarr ↔ Stream)"]
    end

    subgraph Viewers
        NA[Napari]
        FJ[Fiji/ImageJ]
    end

    IX --> PD
    OP --> PD
    BF --> PD
    OM --> PD
    PD --> CO --> EX
    EX --> FN --> PS
    PS --> NA
    PS --> FJ

⚡ Key Capabilities

🧩 The OpenHCS Ecosystem

OpenHCS is built on 8 purpose-extracted, separately published libraries — each solving a general problem and all composed into one platform:

graph TD
    OH["OpenHCS Platform<br/>(domain wiring + pipelines)"]

    OH --> OS["ObjectState<br/>(config)"]
    OH --> AB["ArrayBridge<br/>(arrays)"]
    OH --> PS["PolyStore<br/>(I/O + streaming)"]
    OH --> ZR["ZMQRuntime<br/>(exec)"]
    OH --> QR["PyQT-reactive<br/>(forms)"]

    OS --> PI["python-introspect<br/>(signatures)"]
    OH --> MR["metaclass-registry<br/>(plugins)"]
    OH --> PC["pycodify<br/>(serialization)"]

🔬 Microscope & Function Support

Processing domains: image preprocessing · segmentation · cell counting · stitching (MIST + Ashlar GPU) · neurite tracing · morphology · measurements

🚀 Quick Start

For most desktop users, download the Windows installer or macOS installer. Neither download requires ZIP extraction or an existing Python installation.

For a manual installation, create a virtual environment and install the same CPU-safe desktop surface as the graphical installers:

# Complete CPU-safe desktop environment
python -m pip install "openhcs[gui,viz,bioformats,mcp,cellprofiler-compat]"

# Launch the application
openhcs

# Launch the local MCP server over stdio
openhcs-mcp

Smaller environments can select only the required features:

# Basic installation with GUI
python -m pip install "openhcs[gui]"

# Add Napari viewer
python -m pip install "openhcs[gui,napari]"

# Add Fiji/ImageJ viewer
python -m pip install "openhcs[gui,fiji]"

# Add both viewers
python -m pip install "openhcs[gui,viz]"

# Add GPU acceleration on a compatible CUDA 12 system
python -m pip install "openhcs[gui,gpu]"

# Full installation (GUI + viewers + GPU)
python -m pip install "openhcs[gui,viz,gpu]"

# Add the local MCP server for agent clients
python -m pip install "openhcs[gui,mcp,viz]"

# Or lower a CellProfiler pipeline into public OpenHCS declarations
from pathlib import Path

from objectstate import ensure_global_config_context
from openhcs.core.config import GlobalPipelineConfig
from openhcs.core.orchestrator.orchestrator import PipelineOrchestrator
from openhcs.interop.cellprofiler.pipeline_import import import_cellprofiler_pipeline

plate_path = Path("/data/plate").resolve()
ensure_global_config_context(GlobalPipelineConfig, GlobalPipelineConfig())
steps, pipeline_config = import_cellprofiler_pipeline(
    "analysis.cppipe",
    source_root=plate_path,
)

orchestrator = PipelineOrchestrator(
    plate_path,
    pipeline_config=pipeline_config,
).initialize()
compilation = orchestrator.compile_pipelines(steps)
execution_bundle = compilation["execution_bundle"]

The GUI and execution services consume the same list[FunctionStep], PipelineConfig, and typed execution bundle. See the API orientation for the explicit low-level execution call and progress lifecycle.

python -m pip install "openhcs"              # Headless engine
python -m pip install "openhcs[gui]"         # Desktop GUI
python -m pip install "openhcs[gui,napari]"  # GUI + Napari viewer
python -m pip install "openhcs[gui,viz]"     # GUI + Napari + Fiji
python -m pip install "openhcs[gui,viz,gpu]" # Full installation
python -m pip install "openhcs[gpu]"         # Headless + GPU
python -m pip install "openhcs[omero]"       # OMERO integration
python -m pip install -e ".[all,dev]"         # Development (all features)

python scripts/install_omero_deps.py
pip install 'openhcs[omero]'

pip install -r requirements-omero.txt
pip install 'openhcs[omero]'

📖 Documentation

⚙️ Architecture Highlights

PipelineConfig + list[FunctionStep]
        ↓ resolve once
StepSnapshot + CompilationSession
        ↓ derive and validate
typed CompiledStepPlan objects
        ↓ package
CompiledExecutionBundle
        ↓ execute
runtime values + materialized artifacts

Function patterns · Individual steps · Pipeline configs · Full orchestrator scripts
              ↕  generate / AST-parse back  ↕

🤝 Contributing

git clone --recurse-submodules https://github.com/OpenHCSDev/OpenHCS.git
cd OpenHCS
# Install the eight local packages as described in docs/development_setup.md,
# then install OpenHCS itself:
python -m pip install -e ".[dev,gui]"
OPENHCS_CPU_ONLY=1 python -m pytest tests/unit

Contribution areas: microscope formats · processing functions · GPU backends · documentation

📄 License

MIT — see LICENSE.

🙏 Acknowledgments

OpenHCS evolved from EZStitcher and builds on Ashlar (stitching), MIST (phase correlation), pyclesperanto (GPU image processing), and scikit-image (image analysis).

OpenHCS's CellProfiler interoperability and parity validation build on the CellProfiler project's open-source software, documentation, and public example, tutorial, and benchmark materials. We thank the CellProfiler authors and contributors and the authors of the biological datasets they distribute. Please cite CellProfiler following its official citation guidance, including Stirling et al., CellProfiler 4: improvements in speed, utility and usability (2021).

Third-party project names and logos identify supported integrations, compatible clients, or software used by OpenHCS. They remain the property of their respective projects or owners; their appearance does not imply affiliation or endorsement.