AutoLEI is an XDS-based pipeline with graphical user interface for automated real-time and offline batch 3D ED/microED data processing. It provides a user-friendly platform for rapid, automated data processing and merging of multiple MicroED datasets, with well-designed and significantly streamlined structure determination workflows. If you use AutoLEI in your work, please cite the following publication: 1. Wang, L., Chen, Y., Hutchinson, E. S., Stenmark, P., Hofer, G., Xu, H., & Zou, X. (2026). IUCrJ, 13. https://doi.org/10.1107/S2052252525010784 2. Kabsch, W. (2010). Acta Crystallogr D Biol Crystallogr 66, 125–132. https://doi.org/10.1107/S0907444909047337 If you encounter any issues, please report them to:lei.wang@su.seyinlin.chen@su.se Version selection: AutoLEI version 1.0.3 (build date: 2026-03-18) AutoLEI v-1.0.0: version demonstrated in the paper (https://doi.org/10.1101/2025.04.12.648515). Practicing data available: https://zenodo.org/records/14536385 What's new in v-1.0.3: - SHELX INS file generation is now supported when SHELX HKL is generated via XDSCONV.- The Laue group recognition algorithm has been updated, delivering a higher success rate and improved accuracy.- The phase recognition algorithm has also been improved.- With AutoSolveX enabled, real-time mode can now identify the Laue group and solve the structure automatically, provided the corresponding setting is activated.- A new framework has been reconstructed in preparation for the upcoming version 1.1 update.- The rotation angle refinement algorithm has been rewritten, achieving 3× faster performance.- HKL analysis has also been accelerated by 3×.- The slice viewer and 3D reciprocal space viewer now feature a new GUI.- Numerous bugs have been fixed. If you want to update autolei, in the Python environment type: pip install -U autolei 1. Key FeaturesUser-friendly interface: Simplifies MicroED data processing, requiring minimal manual input.-Batch Processing: Handles large numbers of datasets with automated workflows.-Real-Time Data Processing: Provides live feedback during data collection.Versatility: Supports diverse samples, including small molecules and protein workflows. 2. Installation Operating Systems: Linux or Windows via [WSL](https://en.wikipedia.org/wiki/Windows_Subsystem_for_Linux) (versions 1/2).Software Dependencies:Python 3.8+ with libraries specified in `pyproject.toml`.[XDS](https://xds.mr.mpg.de/) and [XDSGUI](https://wiki.uni-konstanz.de/xds/index.php/XDSGUI).Optional tools: `xprep` for advanced features and LibreOffice for `.xlsx` files in Linux. Steps:a) Install via pip: bash pip install autolei For historical versions, use: bash pip install autolei-[version_name].zip b) Manual installation: Follow the steps in the AutoLEI_Tutorial.pdf and https://gitlab.com/tristonewang/autolei/-/wikis/home 3. UsageCommand-line Usage Launch the GUI bash autolei Note: The first launch may take slightly longer as dependencies initialize. Configure Settings: bash autolei_setting The opened .ini file includes settings on screen scaling, multi-thread and report format. Import Instrument: bash autolei_add_instrument [instrument_setting_file] 4. GUI pagesAutoLEI is organised into multiple working pages:- Input: Configure experiment parameters and generate input files.- XDSRunner: Automate initial processing and data quality inspection.- CellCorr: Update unit cell information and refine settings.- XDSRefine: Fine-tune processing parameters, including rotation axis and scaling.- MergeData: Filter and merge datasets for downstream analysis.- Cluster&Output: Perform clustering and generate outputs for structure determination.- Expert: Miscellaneous tools for data reduction and PETS2-related function.- RealTime: Live data processing with real-time feedback and automatic merging. 5. Documentation and other sourcesDetailed guides and examples can be found in: - Tutorial for AutoLEI- PyPI: https://pypi.org/project/autolei/ - Gitlab (our Wiki): https://gitlab.com/tristonewang/autolei/-/wikis/home 6. Authors and AcknowledgmentsDeveloped by Lei Wang and Yinlin Chen. Contributions from Gerhard Hofer, Hongyi Xu, and Xiaodong Zou at Stockholm University. The project integrates valuable resources from [edtools](https://github.com/instamatic-dev/edtools). 7. LicenseThe software is licensed under the BSD 3-Clause License.