AI Nodes

Why a dedicated node

Calling an LLM from a hand-written backend usually means picking a provider SDK, handling its specific request/response shape, and wiring retries and error handling around it — for every different call site. The AI node does that once: configure a provider, model and mode, and every downstream node just sees a normal output, the same as if a Database node had produced it.

The AI node

Three fields configure a call:

• • aiProvider — openai, anthropic, google or custom for any OpenAI-compatible endpoint.
• • aiModel — the specific model id, e.g. gpt-4o, claude-3-5-sonnet.
• • aiMode — what kind of call this is:

• • chat — conversational, multi-turn responses.
• • completion — free-form text generation from a single prompt.
• • embedding — vector representations of text, for similarity search.
• • classification — sort input into a fixed set of categories.
• • extraction — pull structured fields out of free text.

The node's output feeds the rest of your workflow exactly like any other node — transform it, store it in Database, or return it directly in a Response.

Example: classifying a support ticket

API Endpoint  (POST /tickets)
   ↓
AI            provider: anthropic, model: claude-3-5-sonnet
              mode: classification
              categories: ["billing", "bug", "feature-request", "other"]
   ↓
SQL           INSERT INTO tickets (category, body) VALUES ({{category}}, {{body}})
   ↓
Response      201 { ticketId, category }

One extra node — no separate service to deploy, no provider SDK to import into a hand-written handler.

Bring your own AI

Use Fimaflow's built-in AI credits, or connect your own API key for a provider and pay them directly — set it in the node, or reference one stored as an Environment Variable. Useful once you have production volume or an existing contract with a provider. See AI Credits for exactly how usage is measured either way.

Vector search

Pair an AI node in embedding mode with the Search node to build semantic search over your own data — embed content on write, then query by similarity instead of exact keyword match. Store embeddings in a vector-capable database (Postgres with pgvector, or a dedicated vector store) the same way you'd store any other column.

Interactions with the rest of the platform

• • An assistant connected via MCP can trigger a workflow containing an AI node the same way it triggers any other — the credits it consumes are still billed to your project.
• • Chain several AI nodes for multi-step reasoning — classify first, then generate a response conditioned on the category, without a single monolithic prompt trying to do both.

Errors & edge cases

• • A provider outage or rate limit surfaces as a normal node failure — wrap the AI node in a Try/Catch if the rest of the request shouldn't fail when the model is briefly unavailable.
• • classification/extraction modes return structured output — a response that doesn't match the expected shape fails the node rather than passing malformed data downstream silently.
• • Running out of AI credits mid-workflow fails only the AI node — everything else in the chain still executes; see AI Credits.