An AI node calls a language model with a prompt and hands the result to the rest of your workflow. Use it to classify, extract, summarize, draft, or decide. AI nodes bill in AI Credits by actual token cost: bigger models and longer outputs cost more per call. See billing.
#Prompt
- Prompt (required), the instruction for this step. Reference earlier data with
{{node_name.field}}and{{trigger.field}}. - System prompt (optional), a leading instruction that sets role, tone, or rules independent of the per-run input.
#Model
Pick the model in the builder’s model dropdown. The dropdown is always the live list: new models show up there first, so treat the list below as a snapshot, not the source of truth.
| Family | Current models | Best for |
|---|---|---|
| Claude Opus | 4.8, 4.6, 4.5 | Heavy reasoning, hard judgment calls |
| Claude Sonnet | 4.6, 4.5 | General-purpose work, good default |
| Claude Haiku | 4.5, 3.5 | Fast, cheap classification and extraction |
| GPT | 5.2, 5, 4.1 | GPT-5-class for reasoning, 4.1 for fast/cheap |
Heavier models spend AI Credits faster; reach for them only when a task needs the reasoning.
#Generation settings
| Setting | Default | Range | Notes |
|---|---|---|---|
| Temperature | 0.7 | 0–2 | Lower is more deterministic; raise it for creative drafting. |
| Max tokens | 1024 | 1–32768 | Caps the output length. Raise it for long-form drafting; a step that hits the cap fails rather than returning a half-written answer. |
#Output
By default an AI node returns plain text, available downstream as {{node_name.text}}.
To get structured fields instead, click Define JSON output and describe the shape you expect as a JSON Schema object — its top-level properties, and which of them are required. Once a schema is set, downstream nodes bind its fields directly, {{node_name.fieldName}}, with typed suggestions in the variable picker, instead of parsing free text out of .text. Use this whenever the next step needs to branch on, or read, a specific value the model produces — a score, a category, a boolean — rather than the whole answer. Clear the schema to go back to plain text.
#Retry policy
AI nodes support the standard retry policy, when a call fails with a retryable error, Rills retries before marking the step failed. The defaults and ranges are the same as every other retryable node; see the retry table.
#Research tools
An AI node can reach the web mid-run. Enable one or both:
fetch_url, fetch and read a specific page.web_search, search the web and read results.
Use them to enrich a lead from its company site, check whether a URL is live, or pull a fact the prompt needs. Both add fetched pages to the model’s context, which spends more tokens, so guardrails bound the cost:
- Max research pages,
1–20, default5. Caps how many pages a single run may fetch. - Allowed domains (optional), an allowlist for
fetch_url. When set, a fetch is rejected unless its hostname matches, or is a subdomain of, a listed domain. Leave it empty to allow any domain. Listingacme.comalso permitsblog.acme.comandapp.acme.com.
Research tools only do anything when the node’s output depends on fresh external data; leave them off for pure reasoning or transformation prompts.
#Workspace context
By default, every AI node is given your workspace’s business context, a snapshot of what your company does, who you serve, and how you talk, injected as a leading system block. It’s the same context you set in your workspace settings and refine during onboarding, and it means you don’t have to re-explain your business in every prompt.
Two modes:
- Workspace (default), inject the snapshotted business context.
- None, opt out. Use this for tightly-tuned prompts that must see only exactly what you wrote, no workspace framing.
The context is an immutable snapshot; AI nodes never see mutable conversation history or per-user memory.
#See also
- Workflows: the full node reference and retry policy
- Billing & Credits: how AI Credits are metered
- Runs & Versions: inspecting a run’s AI step output