Kognitos Meta

Procedures and concepts for the Kognitos Meta integration.

The following documentation is for Kognitos Meta v1.0.4 (BDK).

Overview

Kognitos Meta is an intelligent automation book that executes actions based on natural language prompts using Large Language Models (LLMs). This dynamic integration can generate and execute Python code to fulfill complex automation requests that go beyond standard book capabilities.

Prerequisites

1. Required Books

The following Book(s) need to be added to your agent so it can learn and understand the automation procedures defined within them:

• Kognitos Meta

How to Add the Book(s)

1. Go to Books → All Books.

2. Search for the name of the book and click on it.

3. Click on Install or Add Connection to add the book to your agent.

4. If adding a connection, you'll be prompted for connectivity details.

Connectivity

This section outlines the available methods for connecting to the Book, along with the required configuration details for each.

This books supports the connectivity methods described in this section.In here you will find information about what information is required in order to employ each method.

Connect using Gemini API Key, Credential, AWS Access Key ID, AWS Secret Access Key, AWS Region and AWS Bucket Name

Connects to an API using the provided API key.

Label	Description	Type
Gemini API Key	The Gemini API key to be used for connecting to LLM	sensitive
Credential	The credentials to be used for connecting to the external API. This has to be a JSON String. If no credentials are required, fill in with '{}'.	sensitive
AWS Access Key ID	The AWS access key id to be used for connecting to the external API.	text
AWS Secret Access Key	The AWS secret access key to be used for connecting to the external API.	sensitive
AWS Region	The AWS region to be used for connecting to the external API.	text
AWS Bucket Name	The AWS bucket name to be used for connecting to the external API.	text

Configuration

The following table details all the available configuration options for this book.

Concept	Description	Type	Default Value
cache prefix	The prefix to be used for the cache keys.	text	metabook
department id	The kognitos department id.	text	(no default)
llm model name	The LLM model name to be used for the code generation.	text	gemini/gemini-2.0-flash

Configuration can be set or retrieved as shown in the following examples:

the department's Kognitos Meta's cache prefix is metabook

the department's Kognitos Meta's llm model name is gemini/gemini-2.0-flash

Procedures

to perform a task

Performs a task based on the prompt and payload.

Input Concepts

Concept	Description	Type	Required	Default Value
task	The task to perform.	text	Yes	(no default)
payload	The payload to perform the task.	json	No	(no default)

Output Concepts

Concept	Description	Type
answer	The result of the task	`` or file or number or text

Examples

the task is "get the current temperature in Celsius"
create a json
use the above as the payload
set the payload's "city" to "Buenos Aires"
perform the task
   the payload is the payload