Retrieve a JSON list of all agents the user has access to

Retrieve Agents

True or false check to determine if the agent is allowed to delegate tasks to other agents in the context of an app

A detailed description of what the LLM will be doing

A secondary model used to execute function calls (this links to a Model object), set to null if unused automatically by the API

The goal of the agent is fed into the LLM, this allows the LLM to know it's role in the RAG pipeline

IconAttachment object used to hold the attached icon used for the agent (this links to an IconAttachment object);

The linked ObjectId of the model being used by this agent (this links to a Model object)

Organisation the agent is linked to (generally the org of the user that created the agent)

Fed into the LLM to help it provide a more detailed and correct response

Team the agent is linked to (generally the team of the user that created the agent)

Array of the tools the agent can access to improve performance and abstract tool functionality from agent usage

True or false check to determine if custom verbosity is used in agent, higher verbosity requires agent to include more of the retrieved documents at the expense of longer answers, lower verbosity can result in shorter answers but can also ommit crucial details

Mongodb Object id, unique identifier, length of 24 characters fitting the following regex; [a-f0-9]{24}

JSON object containing an array of all the associated agents with that teamId, will return an empty array if the request is successful but there are no associated agents.

Invalid toolIds, modelIds or function calling modelIds. The content will contain an error message which will detail which field is incorrect.

Invalid permissions on the resourceSlug, attempting to access an agent outside the permissions of the team

Test the endpoints on your own instance of Agent Cloud!

Agent Cloud

Introduction

Agent Cloud - Talk to Your Data

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How to Create Your First Agent Cloud App

Local Setup

Get Started with Agent Cloud on a Local Deployment

How to use Agent Cloud on your local development machine.

Local Dev with CLI

Agent Cloud Platform

Agents

Apps

Datasources

Models

Sessions

Tasks

Tools

Variables

Step by step guide to setup a conversational chat app to RAG a Google Big Query datasource (or any other data source)

RAG with BigQuery Tutorial

Tutorial to Build a RAG with Google Bigquery

What is RAG?

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Retrieve a JSON object of the session from path sessionId. Will also return a list of all tasks, tools, agents, models and datasources as a front end helper

Retrieve Session

Retrieve a JSON list of all sessions the user has access to. Will also return a list of all tasks, tools, agents, models and datasources as a front end helper

Retrieve Sessions

Get all messages associated to the path sessionId

Retrieve Messages

Create a new chat session from the parameter appId

Add a session

Cancel a session by Id, best practice to cancel a session once it has completed. The session and it's associated fields will still exist within the database under the same sessionId. The session can be completley removed from the database using the delete route AFTER this has been executed.

Cancel a session

Delete a session

Retrieve a JSON object of the retrieved app. Will also return a list of all tasks, tools, agents, models and datasources as a front end helper

Get App

Retrieve a JSON list of all apps the user has access to. Will also return a list of all tasks, tools, agents, models and datasources as a front end helper

Get Apps

Retrieve a JSON object of a single agent from agentId and the resourceSlug

Retrieve Agent

Add an agent

Edit An Agent

Delete An Agent

Retrieve a JSON object with a singular task to be retrieved by id.

Retrieve Task

Retrieve a JSON list of all tasks the user has access to

Retrieve Tasks

Add A New Task

Edit A Task

Delete A Task

Retrieve a JSON object of a single tool from toolId and the resourceSlug

Retrieve Tool

Retrieve a JSON list of all tools the user has access to

Retrieve Tools

Add A New Tool

Edit an existing tool by its ID within the specified team.

Edit A Tool

Delete a tool by its ID within the specified team.

Delete A Tool

Retrieve a JSON object of a single model within the specified team by modelId.

Retrieve Model

Retrieve a JSON list of all models the user has access to within the specified team.

Retrieve Models

Add A New Model

Edit an existing model by its ID within the specified team.

Edit A Model

Delete a model by its ID within the specified team.

Delete A Model

Retrieve A Datasource

Retrieve a JSON list of all Datasources the user has access to within the specified team.

Retrieve Datasources

Edit Schedule In Datasource - This is marked as Uncomplete

Edit Datasource Streams - This Is Marked As Unfinished

Initialise an Airbyte synchronisation for an existing datasource object

Sync Datasource

Test a new datasource. This is required to gather the datasource schema, tables and values to continue to the next stage of addition. This MUST be executed first when adding a new datasource

Test A Datasource To Be Added

Second stage of adding Datasource (check test datasource for the first step), this connects the datasource to a vectordb, creates a connection between the datasource and airbyte, starts a job to synchronise the rows and finally creates a tool for the datasource. A tool for the datasource is required as within agents a datasource is used as a 'tool' just the same as any other tool, this is required for the 'RAG' process.

Reference

Retrieve Agents

Path Parameters

Response