​Chat​Completion

API Key

Model name

API base URL

MCP client command, as a list of command parts

Environment variables

Log events

Description of the flow if not already provided inside the flow itself

Use it only if you define the flow in the tool definition. The LLM needs a tool description to identify whether to call it. If the flow has a description, the tool will use it. Otherwise, the description property must be explicitly defined.

Flow ID of the flow that should be called

Whether the flow should inherit labels from this execution that triggered it

By default, labels are not inherited. If you set this option to true, the flow execution will inherit all labels from the agent's execution. Any labels passed by the LLM will override those defined here.

Input values that should be passed to flow's execution

Any inputs passed by the LLM will override those defined here.

Labels that should be added to the flow's execution

Any labels passed by the LLM will override those defined here.

Namespace of the flow that should be called

Revision of the flow that should be called

Schedule the flow execution at a later date

If the LLM sets a scheduleDate, it will override the one defined here.

URL of the MCP server

Custom headers

Useful, for example, for adding authentication tokens via the Authorization header.

Log requests

Log responses

Connection timeout duration

List of Kestra runnable tasks

API Key

Model name

API base URL

Generated text completion

The result of the text completion

Finish reason

Response identifier

Request duration in milliseconds

Token usage

Tool execution requests

JSON Schema (used when type = JSON)

Provide a JSON Schema describing the expected structure of the response. In Kestra flows, define the schema in YAML (it is still a JSON Schema object). Example (YAML):

responseFormat: 
    type: JSON
    jsonSchema: 
      type: object
      required: ["category", "priority"]
      properties: 
        category: 
          type: string
          enum: ["ACCOUNT", "BILLING", "TECHNICAL", "GENERAL"]
        priority: 
          type: string
          enum: ["LOW", "MEDIUM", "HIGH"]

Note: Provider support for strict schema enforcement varies. If unsupported, guide the model about the expected output structure via the prompt and validate downstream.

Schema description (optional)

Natural-language description of the schema to help the model produce the right fields. Example: "Classify a customer ticket into category and priority."

Response format type

Specifies how the LLM should return output. Allowed values:

• TEXT (default): free-form natural language.
• JSON: structured output validated against a JSON Schema.

Container image

API version

Volume binds

MCP client command, as a list of command parts

Docker certificate path

Docker configuration

Docker context

Docker host

Whether Docker should verify TLS certificates

Environment variables

Whether to log events

Container registry email

Container registry password

Container registry URL

Container registry username

API key

Custom search engine ID (cx)

Model endpoint

Model name

RapidAPI key for Judge0

You can obtain it from the RapidAPI website.

API Key

Model name

API base URL

Log LLM requests

If true, prompts and configuration sent to the LLM will be logged at INFO level.

Log LLM responses

If true, raw responses from the LLM will be logged at INFO level.

Response format

Defines the expected output format. Default is plain text. Some providers allow requesting JSON or schema-constrained outputs, but support varies and may be incompatible with tool use. When using a JSON schema, the output will be returned under the key jsonOutput.

Seed

Optional random seed for reproducibility. Provide a positive integer (e.g., 42, 1234). Using the same seed with identical settings produces repeatable outputs.

Temperature

Controls randomness in generation. Typical range is 0.0–1.0. Lower values (e.g., 0.2) make outputs more focused and deterministic, while higher values (e.g., 0.7–1.0) increase creativity and variability.

Top-K

Limits sampling to the top K most likely tokens at each step. Typical values are between 20 and 100. Smaller values reduce randomness; larger values allow more diverse outputs.

Top-P (nucleus sampling)

Selects from the smallest set of tokens whose cumulative probability is ≤ topP. Typical values are 0.8–0.95. Lower values make the output more focused, higher values increase diversity.

Tool request arguments

Tool execution request identifier

Tool name

API endpoint

The Azure OpenAI endpoint in the format: https://{resource}.openai.azure.com/

Model name

API Key

Client ID

Client secret

API version

Tenant ID

Endpoint URL

Project location

Model name

Project ID

API Key

Model name

SSE URL of the MCP server

Custom headers

Could be useful, for example, to add authentication tokens via the Authorization header.

Log requests

Log responses

Connection timeout duration

API Key

Model name

Tavily API Key - you can obtain one from the Tavily website

AWS Access Key ID

Model name

AWS Secret Access Key

Amazon Bedrock Embedding Model Type