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LUML Flow

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LUML Flow's own site

What LUML Flow is

LUML Flow is a live experiment tracker for predictive machine learning and generative AI. It records training parameters, step metrics, model artifacts, AI traces, evaluation samples, and human annotations. Flow runs locally: the `lumlflow ui` command opens a dashboard reading a SQLite store on your machine. You can use it offline indefinitely without an account or telemetry, while a LUML account is needed to upload experiments to a shared workspace. Uploaded models and experiment context become versioned artifacts in the LUML registry for workspace members. Listed integrations include PyTorch, TensorFlow, scikit-learn, XGBoost, LightGBM, CatBoost, JAX, Keras, LangGraph, LangChain, LlamaIndex, and DSPy. OpenAI and Anthropic clients can be traced through OpenTelemetry instrumentors. Experiments may include datasets, plots, prompt files, logs, and evaluation reports. The SDK and UI are publicly available on GitHub. A free Light plan is offered; paid plans start at €79/mo (annual).

Who it suits

It suits machine-learning and generative-AI practitioners who need local experiment logging, artifact tracking, comparisons, or optional team sharing. Teams can upload runs to a shared LUML workspace.

What it does well

  • Works offline without an account
  • Local experiments stay in SQLite until upload
  • Tracks parameters, metrics, artifacts, and traces
  • Supports many ML and LLM integrations
  • Uploaded runs become versioned registry artifacts

What to know first

  • Account required for shared-workspace uploads
  • Paid plans start at €79/mo annually
  • Local dashboard requires the `lumlflow ui` command

Verdict

Flow combines private local tracking with optional registry sharing and broad framework integrations. Its free local use is distinct from account-based collaboration and paid plan support.

Facts from LUML Flow's own pages

Account requirement
Flow can be used indefinitely without signing up; a LUML account is needed to upload experiments to a shared workspace. · luml.ai, 2 Oct 2026
Attachments
Experiments can include arbitrary files such as datasets, plots, prompt files, training logs, and evaluation reports. · luml.ai, 3 Oct 2026
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. · luml.ai, 2 Oct 2026
Experiment logging
Flow logs parameters, step metrics, model artifacts, traces, evaluation samples, and human annotations. · luml.ai, 3 Oct 2026
Framework integrations
Flow names scikit-learn, XGBoost, LightGBM, CatBoost, LangGraph, PyTorch, TensorFlow, JAX, and Keras among supported frameworks. · luml.ai, 3 Oct 2026
Headquarters
Linz, Austria · luml.ai, 28 Sept 2026
Integrations
The page lists PyTorch, LangGraph, XGBoost, scikit-learn, LlamaIndex, TensorFlow, Anthropic, LightGBM, DSPy, JAX, LangChain, and Keras. · luml.ai, 2 Oct 2026
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. · luml.ai, 2 Oct 2026
LLM integrations
Flow says OpenAI, Anthropic, LangChain, LlamaIndex, and DSPy clients can be traced through OpenTelemetry instrumentors. · luml.ai, 3 Oct 2026
LLM tracing
Flow routes OpenTelemetry spans into experiment storage and describes instrumentors for OpenAI clients, LangChain, and LangGraph. · luml.ai, 2 Oct 2026
Local storage
The local dashboard reads experiment data from a SQLite store on your machine. · luml.ai, 3 Oct 2026
Local use
You can install Flow with `pip install lumlflow` and start its dashboard with `lumlflow ui`. · luml.ai, 3 Oct 2026
Maker identity
LUML’s legal notice identifies the company as DataForce Solutions GmbH, headquartered in Linz, Austria. · luml.ai, 2 Oct 2026
Offline use
Flow can be used offline indefinitely without a LUML account; an account is needed to upload experiments to a shared workspace. · luml.ai, 3 Oct 2026
Open source
The Flow SDK and UI are described as available in LUML’s public GitHub repository. · luml.ai, 3 Oct 2026
Purpose
Flow is a live experiment tracker for predictive ML and generative AI. · luml.ai, 3 Oct 2026
Security and privacy
Flow says it uses no account or telemetry locally and keeps experiments in the local SQLite store until upload. · luml.ai, 3 Oct 2026
Storage and upload
Experiments are stored locally in SQLite, and uploaded models and experiment context become versioned artifacts in the LUML registry. · luml.ai, 2 Oct 2026
Support
The Flow page links to documentation and offers a request-demo link. · luml.ai, 3 Oct 2026
Team sharing
Uploaded experiments become versioned artifacts in the LUML registry, where workspace members can view experiment context. · luml.ai, 3 Oct 2026
Tracking
It captures training parameters, step metrics, model artifacts, generative AI traces, evaluation samples, and human annotations. · luml.ai, 2 Oct 2026

Where LUML Flow stands on Freedom251

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