​Chat​Completion

The database name

The database server host

The database password

The database server port

The table to store embeddings in

The database user

Whether to use use an IVFFlat index

An IVFFlat index divides vectors into lists, and then searches a subset of those lists closest to the query vector. It has faster build times and uses less memory than HNSW but has lower query performance (in terms of speed-recall tradeoff).

The collection name

The host

The index name

The scheme (e.g., mongodb+srv)

Create the index

The database

The metadata field names

The connection string options

The password

The username

API Key

Model name

API base URL

MCP client command, as a list of command parts

Environment variables

Log events

The database base URL

The collection name

The database server host

The database server port

The index name

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.

Basic authorization password

Basic authorization username

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

The API key

The cloud provider

The index

The cloud provider region

The namespace (default will be used if not provided)

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

API key

Custom search engine ID (cx)

Maximum number of results

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.

The name of the index to store embeddings

Maximum results to return from the embedding store

Minimum similarity score (0-1 inclusive). Only results with score ≥ minScore are returned.

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

The API key

The collection name

The database server host

The database server port

Endpoint URL

Project location

Model name

Project ID

API Key

Model name

The name of the KV pair to use

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

Maximum number of results to return

Redis host

The hostname of your Redis server (e.g., localhost or redis-server)

Drop memory: never, before, or after the agent's task run

By default, the memory ID is the value of the system.correlationId label, meaning that the same memory will be used by all tasks of the flow and its subflows. If you want to remove the memory eagerly (before expiration), you can set drop: AFTER_TASKRUN to erase the memory after the taskrun. You can also set drop: BEFORE_TASKRUN to drop the memory before the taskrun.

Memory ID - defaults to the value of the system.correlationId label. This means that a memory is valid for the entire flow execution including its subflows.

Maximum number of messages to keep in memory. If memory is full, the oldest messages will be removed in a FIFO manner. The last system message is always kept.

Redis port

The port of your Redis server

Memory duration - defaults to 1h

Token

Milvus auth token. Required if authentication is enabled; omit for local deployments without auth.

Auto flush on delete

If true, flush after delete operations.

Auto flush on insert

If true, flush after insert operations. Setting it to false can improve throughput.

Collection name

Target collection. Created automatically if it does not exist. Default: "default".

Consistency level

Read/write consistency level. Common values include STRONG, BOUNDED, or EVENTUALLY (depends on client/version).

Database name

Logical database to use. If not provided, the default database is used.

Host

Milvus host name (used when uri is not set). Default: "localhost".

ID field name

Field name for document IDs. Default depends on collection schema.

Index type

Vector index type (e.g., IVF_FLAT, IVF_SQ8, HNSW). Depends on Milvus deployment and dataset.

Metadata field name

Field name for metadata. Default depends on collection schema.

Metric type

Similarity metric (e.g., L2, IP, COSINE). Should match the embedding provider’s expected metric.

Password

Required when authentication/TLS is enabled. See https://milvus.io/docs/authenticate.md

Port

Milvus port (used when uri is not set). Typical: 19530 (gRPC) or 9091 (HTTP). Default: 19530.

Retrieve embeddings on search

If true, return stored embeddings along with matches. Default: false.

Text field name

Field name for original text. Default depends on collection schema.

URI

Connection URI. Use either uri OR host/port (not both). Examples:

• gRPC (typical): "milvus://host: 19530"
• HTTP: "http://host: 9091"

Username

Required when authentication/TLS is enabled. See https://milvus.io/docs/authenticate.md

Vector field name

Field name for the embedding vector. Must match the index definition and embedding dimensionality.

API Key

Model name

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

Drop memory: never, before, or after the agent's task run

By default, the memory ID is the value of the system.correlationId label, meaning that the same memory will be used by all tasks of the flow and its subflows. If you want to remove the memory eagerly (before expiration), you can set drop: AFTER_TASKRUN to erase the memory after the taskrun. You can also set drop: BEFORE_TASKRUN to drop the memory before the taskrun.

Memory ID - defaults to the value of the system.correlationId label. This means that a memory is valid for the entire flow execution including its subflows.

Maximum number of messages to keep in memory. If memory is full, the oldest messages will be removed in a FIFO manner. The last system message is always kept.

Memory duration - defaults to 1h

API key

Weaviate API key. Omit for local deployments without auth.

Host

Cluster host name without protocol, e.g., "abc123.weaviate.network".

Avoid duplicates

If true (default), a hash-based ID is derived from each text segment to prevent duplicates. If false, a random ID is used.

Consistency level

Write consistency: ONE, QUORUM (default), or ALL.

gRPC port

Port for gRPC if enabled (e.g., 50051).

Metadata field name

Field used to store metadata. Defaults to "_metadata" if not set.

Metadata keys

The list of metadata keys to store - if not provided, it will default to an empty list.

Object class

Weaviate class to store objects in (must start with an uppercase letter). Defaults to "Default" if not set.

Port

Optional port (e.g., 443 for https, 80 for http). Leave unset to use provider defaults.

Scheme

Cluster scheme: "https" (recommended) or "http".

Secure gRPC

Whether the gRPC connection is secured (TLS).

Use gRPC for batch inserts

If true, use gRPC for batch inserts. HTTP remains required for search operations.

List of HTTP Elasticsearch servers

Must be a URI like https://example.com: 9200 with scheme and port

Basic authorization configuration

List of HTTP headers to be sent with every request

Each item is a key: value string, e.g., Authorization: Token XYZ

Path prefix for all HTTP requests

If set to /my/path, each client request becomes /my/path/ + endpoint. Useful when Elasticsearch is behind a proxy providing a base path; do not use otherwise.

Treat responses with deprecation warnings as failures

Trust all SSL CA certificates

Use this if the server uses a self-signed SSL certificate

AWS Access Key ID

Model name

AWS Secret Access Key

Amazon Bedrock Embedding Model Type