Training runs stop being terminal scrollback and become a board you can read at a glance. SwanLab is an open-source experiment tracking and visualization platform for AI model training that records metrics, hyperparameters, logs, and hardware signals from your training code into a modern web dashboard you can self-host. A few lines of Python start an experiment, and the dashboard follows loss curves, learning rates, and custom scalars live, with line chart configuration, smoothing, grouping, and detailed hover inspection for comparing runs. Experiments organize into projects and groups with tags, pinning, filtering, and a table view that sorts and aligns runs against a baseline with difference percentages. Media logging goes far beyond scalars: images, audio, video, text with Markdown rendering, 3D point clouds, molecular structures, and custom ECharts visualizations including PR curves, ROC curves, and confusion matrices. Hardware monitoring records GPU, CPU, memory, disk, and network behavior from NVIDIA, AMD, Ascend, and other accelerators alongside the metrics they produced. Integrations cover more than fifty frameworks, from PyTorch Lightning and Hugging Face Transformers to LLaMA Factory, veRL, and XGBoost, and existing histories migrate in through sync tools for MLflow, TensorBoard, and Weights and Biases. Teams collaborate with organizations, roles, project sharing, webhooks, and notification plugins. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

Trusted by thousands of organizations with over 30 million monthly downloads and 20,000+ GitHub stars, MLflow is the largest open-source AI engineering platform providing end-to-end lifecycle management for traditional ML models, LLMs, and AI agents. The OpenTelemetry-based tracing system captures complete request flows through any LLM provider or agent framework — including OpenAI, LangChain, DSPy, Vercel AI, PydanticAI, and smolagents — with one-line auto-instrumentation that tracks inputs, outputs, token usage, and costs at every intermediate step. MLflow's evaluation engine offers 50+ built-in metrics and LLM judges for systematic quality assessment, detecting issues across correctness, latency, adherence, relevance, and safety dimensions before code reaches production. The Prompt Registry versions, tests, and deploys prompts with full lineage tracking while automated optimization algorithms improve prompt performance using evaluation feedback. The AI Gateway provides a unified API endpoint for all LLM providers, enforcing rate limits, cost controls, and access policies across the organization. MLflow 3.0 introduces the LoggedModel abstraction linking traces, metrics, and prompts to specific model versions across Python, TypeScript, Java, and R SDKs. The model registry manages deployment workflows with automated quality gates, while experiment tracking records parameters, metrics, and artifacts across training runs. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache License 2.0 licensed.