Weights & Biases

Weights & Biases (often abbreviated as W&B) is a cloud-based platform designed specifically for AI developers, data scientists, and machine learning engineers to manage the full lifecycle of AI projects—from experiment tracking and model training to deploying production-grade AI agents and applications. The platform is built around two primary product pillars: W&B Models and W&B Weave, each addressing distinct phases of AI development. W&B Models focuses on the experimental and training phase. It allows users to log hyperparameters, metrics, and output artifacts (such as model weights, checkpoints, and dataset versions) during training runs. By initializing a `wandb` run and calling `wandb.init()`, developers can record configuration dictionaries, track real-time metrics using `run.log()`, and store model artifacts via `run.log_artifact()`. This creates a central, queryable repository of all training history, making it simple to compare different runs, visualize learning curves, and identify the best-performing configurations. The platform also supports automated hyperparameter sweeps, distributed training integration (e.g., with PyTorch, TensorFlow, Keras, Hugging Face), and collaboration features like team workspaces and shared dashboards. W&B Weave is the newer product aimed at productionizing AI applications, especially those involving large language models (LLMs) and agentic workflows. With Weave, developers can instrument their application code to trace every step of an AI agent’s reasoning—LLM calls, document retrieval, tool usage, and intermediate decisions—all within a single unified view. The Python library provides a `weave.op()` decorator that automatically captures input/output pairs, latency, and error rates, making it possible to debug complex multi-agent