Nipype is a flexible, lightweight, and extensible neuroimaging data processing framework in Python. It addresses several challenges in neuroimaging analysis, including lack of uniform access to software, difficulty in comparative algorithm development, personnel turnover, computational inefficiency, and inadequate method sections in journal articles. Nipype provides a unified interface to existing neuroimaging software, enabling efficient and reproducible analysis. It supports both local and remote execution on multi-core machines and clusters, and is licensed under the BSD license, allowing unrestricted use. Nipype is developed through an open, community-driven approach, enabling rapid adaptation to the evolving needs of the neuroimaging community. Nipype consists of three main components: interfaces to external tools, a workflow engine for creating analysis pipelines, and plug-ins for executing workflows locally or in distributed environments. Interfaces provide a uniform mechanism for accessing neuroimaging software, allowing users to interact with various tools through a consistent interface. The workflow engine enables the creation of analysis pipelines by connecting inputs and outputs of interfaces as a directed acyclic graph (DAG). Plug-ins support both local and remote execution, making it easy to run workflows on multi-core machines or clusters. Nipype supports a wide range of neuroimaging software packages, including FSL, SPM, and FreeSurfer, and provides interfaces for these tools. It also allows users to create custom interfaces for new software. The workflow engine enables the creation of complex analysis pipelines, with the ability to connect outputs of one node to inputs of another, allowing for the specification of dependencies. Workflows can be visualized and shared, facilitating collaboration and reproducibility. Nipype provides a flexible approach to parameter space exploration, allowing users to test different parameter settings and their impact on analysis results. It supports parallel execution on clusters and provides a mechanism for efficient rerunning of workflows, only recomputing nodes whose inputs have changed. The framework also includes a function interface that allows users to run any Python code as part of a workflow, enabling rapid prototyping of complex data processing methods. Nipype is designed to be user-friendly, with standardized help information for all interfaces, making it easy for users to access and use the software. It supports a wide range of operating systems, including GNU/Linux and Mac OS X, and provides deployment options through manual installation, PyPI, and package repositories. Nipype is developed through an open-source model, with contributions from a global community of developers, ensuring continuous improvement and adaptation to the needs of the neuroimaging community.Nipype is a flexible, lightweight, and extensible neuroimaging data processing framework in Python. It addresses several challenges in neuroimaging analysis, including lack of uniform access to software, difficulty in comparative algorithm development, personnel turnover, computational inefficiency, and inadequate method sections in journal articles. Nipype provides a unified interface to existing neuroimaging software, enabling efficient and reproducible analysis. It supports both local and remote execution on multi-core machines and clusters, and is licensed under the BSD license, allowing unrestricted use. Nipype is developed through an open, community-driven approach, enabling rapid adaptation to the evolving needs of the neuroimaging community. Nipype consists of three main components: interfaces to external tools, a workflow engine for creating analysis pipelines, and plug-ins for executing workflows locally or in distributed environments. Interfaces provide a uniform mechanism for accessing neuroimaging software, allowing users to interact with various tools through a consistent interface. The workflow engine enables the creation of analysis pipelines by connecting inputs and outputs of interfaces as a directed acyclic graph (DAG). Plug-ins support both local and remote execution, making it easy to run workflows on multi-core machines or clusters. Nipype supports a wide range of neuroimaging software packages, including FSL, SPM, and FreeSurfer, and provides interfaces for these tools. It also allows users to create custom interfaces for new software. The workflow engine enables the creation of complex analysis pipelines, with the ability to connect outputs of one node to inputs of another, allowing for the specification of dependencies. Workflows can be visualized and shared, facilitating collaboration and reproducibility. Nipype provides a flexible approach to parameter space exploration, allowing users to test different parameter settings and their impact on analysis results. It supports parallel execution on clusters and provides a mechanism for efficient rerunning of workflows, only recomputing nodes whose inputs have changed. The framework also includes a function interface that allows users to run any Python code as part of a workflow, enabling rapid prototyping of complex data processing methods. Nipype is designed to be user-friendly, with standardized help information for all interfaces, making it easy for users to access and use the software. It supports a wide range of operating systems, including GNU/Linux and Mac OS X, and provides deployment options through manual installation, PyPI, and package repositories. Nipype is developed through an open-source model, with contributions from a global community of developers, ensuring continuous improvement and adaptation to the needs of the neuroimaging community.