> ## Documentation Index > Fetch the complete documentation index at: https://wb-21fd5541-wbdocs-1882.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # How is W&B different from TensorBoard? W\&B integrates with TensorBoard and improves experiment tracking tools. The founders created W\&B to address common frustrations faced by TensorBoard users. Key improvements include: 1. **Model Reproducibility**: W\&B facilitates experimentation, exploration, and model reproduction. It captures metrics, hyperparameters, code versions, and saves model checkpoints to ensure reproducibility. 2. **Automatic Organization**: W\&B streamlines project handoffs and vacations by providing an overview of all attempted models, which saves time by preventing the re-execution of old experiments. 3. **Quick Integration**: Integrate W\&B into your project in five minutes. Install the free open-source Python package and add a few lines of code. Logged metrics and records appear with each model run. 4. **Centralized Dashboard**: Access a consistent dashboard regardless of where training occurs—locally, on lab clusters, or cloud spot instances. Eliminate the need to manage TensorBoard files across different machines. 5. **Robust Filtering Table**: Search, filter, sort, and group results from various models efficiently. Easily identify the best-performing models for different tasks, an area where TensorBoard often struggles with larger projects. 6. **Collaboration Tools**: W\&B enhances collaboration for complex machine learning projects. Share project links and utilize private teams for result sharing. Create reports with interactive visualizations and markdown descriptions for work logs or presentations. *** [Tensorboard](/support/models/tags/tensorboard)