According to the official Ollama documentation, Ollama documentation focuses on running models locally and interacting with the local service.
This note is based only on the official source: https://docs.ollama.com/ . Within that scope, the conservative statement is that the Ollama documentation describes running models locally and interacting with the local service. This explanation does not make claims about model quality, hardware requirements, pricing, uptime, dates, or unsupported capabilities.
Start with one visual workflow, connect a model, add a knowledge source, test inputs and outputs, and understand self-hosting and licensing boundaries.
Install Flowise with Node.js or Docker, build one small chat flow, add credentials safely, and understand when visual graphs need production controls.
Use Langflow Desktop or its Python package to build one visual flow, test components, expose an API, and understand dependency and secret handling.
Understand RAGFlow's document pipeline, prerequisites, Docker deployment, model configuration, resource demands, and production security before self-hosting.
Choose the desktop or portable build, load an official template, understand model files and nodes, and avoid common GPU, custom-node, and workflow-sharing risks.
A structured workflow for serving Holo 3.1 locally with llama.cpp and connecting it to OpenClaw, including model selection and agent safety controls.