Kognitos Meta

Overview of the Kognitos Meta book.

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.

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