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AI Capabilities

FluidGrids brings large language models into your workflows in two ways: AI nodes you place on the canvas, and the FluidGrids AI assistant that helps you build workflows and nodes.

The FluidGrids AI assistant​

The floating FluidGrids AI button on every screen opens an assistant with two modes:

  • Workflow — describe an automation and the assistant drafts it as a new workflow in your workspace (trigger, nodes, wiring, configuration). It also answers questions about the screen you are on and can act on your workflows through the same API the app uses.
  • Build a node — describe an integration and the assistant scaffolds a complete node package as a downloadable zip.

See AI Assistant.

Chat Models​

Two chat-model nodes connect to leading LLM providers:

  • Anthropic Chat Model — Claude models
  • OpenAI Chat Model — GPT models

Configure a chat-model node with:

  • System prompt and user prompt (with template variables)
  • Temperature, max tokens, and model selection
  • Structured output (paired with the Structured Output Parser)

AI Agent​

The AI Agent node runs an LLM in a reasoning loop that can call tools to accomplish a task. Give it a goal and a set of tools, and it decides which tools to invoke and in what order.

On the canvas the agent exposes sub-node ports for its chat model, memory, and tools; you attach the matching store nodes to those ports. If no compatible node is installed for a port, the builder says so and points you to the Node Store. Store connector nodes such as Gmail can also be attached as tools, so an agent can, for example, search a mailbox and apply labels.

The following dedicated AI tools are available to an agent:

  • Calculator Tool — evaluate arithmetic and numeric expressions
  • Code Executor Tool — run code to compute a result
  • HTTP Request Tool — call external APIs during reasoning

Structured Output & Document Parsing​

  • Structured Output Parser — coerce a model's response into a schema so downstream nodes receive clean, typed data
  • PDF Extract — pull text and content out of PDF documents for use as model input

On the roadmap​

Semantic search over vector databases and full retrieval-augmented generation (RAG) pipelines — including document ingestion and embeddings — are not yet available. They are planned for a future release.

Next steps​

  • Nodes — full node catalog including AI nodes
  • Custom Nodes — building AI-powered custom nodes