AI Models & Setup

Flow-Like model nodes use the providers and models available in the active profile. Configure credentials and endpoints once, then either select a provider model explicitly or let Find Model choose from the available catalog using preferences.

Configure the active profile

Use the profile and model settings to:

add a provider connection;
enter the required credential or local endpoint;
discover or enable the models you intend to use;
test the connection;
save the profile;
run a small workflow with the selected model.

See Profiles and AI models in the getting-started guide for the current interface.

Store provider credentials in the profile or secret-backed configuration. Do not put API keys into boards, prompts, logs, or documentation screenshots.

Provider nodes

The generated provider catalog currently includes model builders for:

Provider family	Examples
Major hosted APIs	OpenAI, Anthropic, Gemini, Vertex AI, AWS Bedrock
Hosted inference and routing	Groq, OpenRouter, Together AI, Perplexity, Huggingface
Other hosted providers	Cohere, Deepseek, Mistral, Moonshot AI, xAI, Hyperbolic, VoyageAI
Local or compatible endpoints	Ollama, LM Studio, Mozilla any-llm
Additional catalog providers	Galadriel, Mira

Browse Generative model provider nodes for the current set and each node’s inputs. Provider availability and model lists can change independently of the docs.

Explicit model selection

Use a provider-specific model node when the board requires a known provider configuration. Examples include:

Explicit selection is useful when:

a workflow has been evaluated against one model configuration;
data residency or provider policy is fixed;
a provider-specific option is required;
exact cost and behavior need controlled rollout.

Keep the model identifier configurable rather than scattering it across several boards.

Preference-based selection

Find Model selects a model from the active profile using a BitModelPreference.

Build the preference with:

Node	Purpose
Make Preferences	Start a preference value and require multimodal capability when needed
Set Preference Weight	Weight cost, speed, reasoning, creativity, factuality, function calling, safety, openness, multilinguality, or coding
Set Model Hint	Add a soft hint for a desired model family

Preference weights guide selection; they are not hard guarantees of quality. Evaluate the selected-model behavior for the workflow and log the actual model used with each run.

Use preference-based selection when the board can tolerate a compatible alternative and the active profile may differ across environments.

Match capability to the task

Task	Required capability to verify
Chat or generation	Text generation and sufficient context
Tool-using agent	Reliable function or tool calling
Structured extraction	Required tool call and JSON Schema adherence
Image understanding	Multimodal or vision input
RAG indexing	Embedding model with stable vector dimension
Speech	Matching speech-to-text or text-to-speech model type
Image or video generation	Corresponding generation model and options

A provider may expose several model types. A text-generation model is not automatically an embedding, speech, image, or video model.

Local models

Ollama Model and LM Studio Model connect to compatible local services.

Before using a local model:

confirm the service is reachable from the execution backend;
verify model type and tool or vision support;
measure memory, accelerator, and disk requirements on the target machine;
test concurrency and timeout behavior;
define what should happen when the local service is unavailable.

Hardware requirements depend on model architecture, quantization, context size, and runtime. Use the model and runtime documentation instead of a universal RAM estimate.

Embedding models

RAG requires an embedding model for documents and queries. Use Load Embedding Model, Embed Document, and Embed Query.

Index and query with the same embedding model and configuration. Changing the model normally requires rebuilding the vector index.

Model configuration

History nodes can set options such as maximum tokens, temperature, top-p, response format, streaming, seed, and stop words. Provider support differs.

Choose settings through evaluation:

lower variability for extraction and governed answers;
enough output budget for the response contract;
streaming only when partial output can be handled safely;
response format compatible with the downstream parser;
explicit timeout and retry behavior.

Do not assume a provider interprets every sampling parameter identically.

Evaluate before rollout

Maintain task-specific cases and compare:

correctness and completeness;
tool or schema adherence;
refusal and uncertainty behavior;
latency and timeout rate;
token usage and cost;
multilingual and domain behavior where relevant;
safety on adversarial or sensitive inputs.

Record the provider, model identifier, profile or configuration version, prompt version, and relevant settings with evaluation results.

Troubleshooting

Symptom	Check
No models available	Active profile, provider connection, model discovery, network reach
Authentication fails	Credential scope, expiration, endpoint, secret handling
Local model is unreachable	Service address from the execution backend, firewall, process state
Tool calls fail	Model capability, tool schema, iteration and timeout limits
Structured extraction fails	Function-call support, schema validity, selected model
Output changes between environments	Active profile, selected model, preference result, settings
Context errors	Input size, history length, retrieval count, output budget