LUML Flow

Spec sheet

Purpose

Flow is a live experiment tracker for predictive ML and generative AI.Read 3 Oct 2026 · luml.ai

Intended users

The page presents Flow for people tracking predictive ML training and generative AI experiments, including teams that want to share runs through LUML.Read 2 Oct 2026 · luml.ai

Integrations

The page lists PyTorch, LangGraph, XGBoost, scikit-learn, LlamaIndex, TensorFlow, Anthropic, LightGBM, DSPy, JAX, LangChain, and Keras.Read 2 Oct 2026 · luml.ai

Support

The Flow page links to documentation and offers a request-demo link.Read 3 Oct 2026 · luml.ai

Headquarters

Linz, AustriaRead 28 Sep 2026 · luml.ai

Account requirement

Flow can be used indefinitely without signing up; a LUML account is needed to upload experiments to a shared workspace.Read 2 Oct 2026 · luml.ai

Attachments

Experiments can include arbitrary files such as datasets, plots, prompt files, training logs, and evaluation reports.Read 3 Oct 2026 · luml.ai

Data handling

The page says there is no account or telemetry required for local use and experiments remain in the local SQLite store until uploaded.Read 2 Oct 2026 · luml.ai

Experiment logging

Flow logs parameters, step metrics, model artifacts, traces, evaluation samples, and human annotations.Read 3 Oct 2026 · luml.ai

Framework integrations

Flow names scikit-learn, XGBoost, LightGBM, CatBoost, LangGraph, PyTorch, TensorFlow, JAX, and Keras among supported frameworks.Read 3 Oct 2026 · luml.ai

LLM integrations

Flow says OpenAI, Anthropic, LangChain, LlamaIndex, and DSPy clients can be traced through OpenTelemetry instrumentors.Read 3 Oct 2026 · luml.ai

LLM tracing

Flow routes OpenTelemetry spans into experiment storage and describes instrumentors for OpenAI clients, LangChain, and LangGraph.Read 2 Oct 2026 · luml.ai

Local storage

The local dashboard reads experiment data from a SQLite store on your machine.Read 3 Oct 2026 · luml.ai

Local use

You can install Flow with `pip install lumlflow` and start its dashboard with `lumlflow ui`.Read 3 Oct 2026 · luml.ai

Maker identity

LUML’s legal notice identifies the company as DataForce Solutions GmbH, headquartered in Linz, Austria.Read 2 Oct 2026 · luml.ai

Offline use

Flow can be used offline indefinitely without a LUML account; an account is needed to upload experiments to a shared workspace.Read 3 Oct 2026 · luml.ai

Open source

The Flow SDK and UI are described as available in LUML’s public GitHub repository.Read 3 Oct 2026 · luml.ai

Security and privacy

Flow says it uses no account or telemetry locally and keeps experiments in the local SQLite store until upload.Read 3 Oct 2026 · luml.ai

Storage and upload

Experiments are stored locally in SQLite, and uploaded models and experiment context become versioned artifacts in the LUML registry.Read 2 Oct 2026 · luml.ai

Team sharing

Uploaded experiments become versioned artifacts in the LUML registry, where workspace members can view experiment context.Read 3 Oct 2026 · luml.ai

Tracking

It captures training parameters, step metrics, model artifacts, generative AI traces, evaluation samples, and human annotations.Read 2 Oct 2026 · luml.ai

Where Specifiction places it

• ML Experiment Tracking Software#27 of 397.0