Connector (Action)

The Connector node (also called an Action node) is how your workflow interacts with external services and processes data. It executes tools from your connectors, and you choose how it runs: let an AI agent figure things out, or specify the exact tool and parameters yourself.

Execution modes

Every connector node runs in one of two modes. You select the mode in the properties panel when configuring the node.

Agent mode

In agent mode, you write a prompt describing what you want to accomplish. The AI reads your prompt, reasons about which tools to use, calls them, and returns a result. You describe what you want — the AI figures out how. Configuration:

Field	Required	Description
Connector	Yes	The service or utility to use
Connection	Depends	For integration connectors, the connection to use
Prompt	Yes	A natural-language description of what you want the AI to do
Model	No	Which AI model to use — see Models for options (uses default if not set)
Allowed Tools	No	Filter which tools the AI can see — uncheck tools that aren’t relevant to keep the AI focused
Response Schema	No	Define a JSON schema to force the AI to return structured data. See Response Schema below

Best for:

Tasks that require reasoning or analysis
Multi-step operations where the AI chains several tools together
Flexible logic — “summarise these orders”, “find the best match”, “clean up this data”
Situations where you’re not sure which exact tool or parameters to use

Direct mode

In direct mode, you pick the exact tool and fill in its parameters yourself. No AI is involved — the tool runs immediately with exactly the inputs you provide. Configuration:

Field	Description
Connector	The service or utility to use
Tool	The specific operation to perform (e.g., `send-message`, `parse`, `case`)
Connection	For integration connectors, the connection to use
Parameters	Tool-specific inputs — these change depending on the selected tool

Best for:

Known, repeatable operations — “always send this Slack message”, “always convert to uppercase”
When you need predictable, consistent results every time
Simple tasks where you already know the tool and parameters
High-throughput workflows where speed matters

When to use which

Scenario	Recommended mode
You know exactly which tool and parameters to use	Direct
You need the AI to decide how to approach a task	Agent
The task is simple and repeatable	Direct
The task requires analysis or judgement	Agent
You want the fastest possible execution	Direct
You want to chain multiple tools in a single step	Agent

Tool selection

When a connector node runs in agent mode, the AI has access to the tools provided by the selected connector. If the connector has many tools, you can filter which tools the AI can see using the checkbox list in the properties panel. This keeps the AI focused on the tools that are relevant to the task, which leads to:

Faster execution — the AI spends less time considering irrelevant tools
More accurate results — fewer choices means the AI is more likely to pick the right tool
Clearer behaviour — you know exactly which tools the AI might use

If your connector has dozens of tools but you only need two or three for a particular step, uncheck the rest. The AI will work better with a focused set of tools.

Using integration connectors

Integration connectors like Shopify, Slack, and Resend require a connection with valid credentials. Select the connection in the node configuration.

Using utility connectors

Utility connectors (Text, JSON, Math, CSV, etc.) don’t require a connection. Select the connector and tool, then fill in the parameters.

Referencing previous outputs

You can reference the output of any previous node as input to a connector node. Use the variable picker to select outputs from upstream nodes.

Response Schema

In agent mode, you can define a response schema to force the AI to return structured data instead of free-form text. This is useful when downstream nodes (Condition, Transform, Loop) need to work with specific fields. Click Response Schema in the properties panel to expand the schema editor. It has two modes:

Visual — add properties with a name, type, description, and required flag. Supports nested objects and typed arrays.
JSON — paste or edit raw JSON schema directly.

Example: Extract order summary as structured data:

{
  "type": "object",
  "description": "Order summary",
  "properties": {
    "orderCount": {
      "type": "number",
      "description": "Total number of orders"
    },
    "totalRevenue": {
      "type": "number",
      "description": "Combined revenue across all orders"
    },
    "topProducts": {
      "type": "array",
      "description": "Top selling products",
      "items": {
        "type": "object",
        "properties": {
          "name": { "type": "string" },
          "quantity": { "type": "number" }
        }
      }
    }
  },
  "required": ["orderCount", "totalRevenue"]
}

When a response schema is defined, the AI is forced to return data matching exactly that structure — no extra fields, no missing required fields. The output is a parsed object, not a string.

Response schema is also available on Knowledge nodes in query mode.

Retry

Connector nodes can automatically retry on transient failures (timeouts, server errors, rate limits). Configure in the Retry section of the properties panel:

Max attempts (1–5; default 1, no retry). When set above 1, the node retries on transient failures with backoff between attempts.
Retry on tool errors (off by default). Also retries connector errors that aren’t clearly transient. May create duplicate side effects for write operations — only enable on tools you’re sure are safe to retry.

Retry scope is per-tool-call in agent mode and per-step in direct mode. See Retry behaviour for the full classification rules and per-mode semantics.

Output

The connector node binds the result of the tool execution to its output variable. The shape depends on the runner mode:

Direct mode — {{ step }} is the parsed tool payload. The shape is tool-specific; check the connector page for details. For HTTP tools that’s typically { data, status, headers, statusText } — address the response body with {{ step.data }} and the status code with {{ step.status }}.
Agent mode without a Response Schema — {{ step }} is the LLM’s prose answer (a string).
Agent mode with a Response Schema — {{ step }} is the structured schema object; address fields directly: {{ step.<schemaField> }}.

See Passing Data Between Nodes for the full template reference.

Examples

Agent mode: Analyse Shopify orders

Connector: Shopify
Mode: Agent
Prompt: Get all orders from the last 7 days and summarise the total revenue by product category

The AI decides which Shopify tools to call (e.g., listing orders, fetching product details) and assembles the summary for you.

Direct mode: Send a Slack message

Connector: Slack
Mode: Direct
Tool: send-message
channel: C01234567
text: New order received!

Direct mode: Convert text to uppercase

Connector: Text Tools
Mode: Direct
Tool: case
text: hello world
to: upper

Direct mode: Query JSON data

Connector: JSON Tools
Mode: Direct
Tool: query
data: (reference previous node output)
path: $.orders[*].total

Documentation Index

​Execution modes

​Agent mode

​Direct mode

​When to use which

​Tool selection

​Using integration connectors

​Using utility connectors

​Referencing previous outputs

​Response Schema

​Retry

​Output

​Examples

​Agent mode: Analyse Shopify orders

​Direct mode: Send a Slack message

​Direct mode: Convert text to uppercase

​Direct mode: Query JSON data

Execution modes

Agent mode

Direct mode

When to use which

Tool selection

Using integration connectors

Using utility connectors

Referencing previous outputs

Response Schema

Retry

Output

Examples

Agent mode: Analyse Shopify orders

Direct mode: Send a Slack message

Direct mode: Convert text to uppercase

Direct mode: Query JSON data