Team Agents

Team Agents are a sophisticated type of agent on the aiXplain platform, designed for handling complex, multi-step tasks that require coordination between multiple components. By leveraging a system of specialized agents, tools, and workflows, Team Agents excel in scenarios that demand advanced task planning, quality control, and adaptability.

Team Agent Architecture

• Mentalist: Handles task decomposition, planning, and dynamic re-planning as needed.

• Inspector: Ensures outputs meet user-defined criteria, focusing on quality and consistency.

• Bodyguard: (Coming Soon) Implements security measures, enforcing data access and privacy policies.

• Orchestrator: Central LLM that coordinates tasks and invokes appropriate tools or agents.

Example

This example demonstrates a team agent that combines the functionalities of individual agents for text analysis, multimedia processing, and other tasks. It uses a specified large language model (LLM) to coordinate the agents.

from aixplain.factories import TeamAgentFactory

team = TeamAgentFactory.create(
    name="Team of Agents for Text Audio and Image Processing",
    agents=[
        text_analysis_agent,
        multimedia_agent,
    ],
    llm_id="6646261c6eb563165658bbb1"
)

Guide to Building the Example Team Agent

Initialize the Team Agent

Define the team agent with a name, list of individual agents, and the LLM to manage interactions.

from aixplain.factories import TeamAgentFactory

team = TeamAgentFactory.create(
    name="Team of Agents for Text Audio and Image Processing",
    agents=[
        text_analysis_agent,
        multimedia_agent,
    ],
    llm_id="6646261c6eb563165658bbb1"
)

Run the Team Agent

Execute the team agent with specific inputs and evaluate its combined response.

response = team.run("Process this audio and analyze the text content.")
print(response)

Update the Team Agent

Make modifications to the team agent configuration or replace agents as needed.

new_agent = AgentFactory.create(
    name="Search Agent",
    description="Conduct search based on given topic."
)
new_agent

team.agents.append(new_agent)
team.update()

Debug the Team Agent

Test and refine the team agent:

• Check individual agents' performance and outputs.
• Evaluate interactions between agents for seamless task execution.
• Fine-tune the LLM or individual agents for optimized results.

Deploy the Team Agent

Save the team agent configuration to the platform and deploy it for production.

team.deploy()

Team Agent Parameters

Defines the configuration options for creating a team agent, including its name, included agents, LLM, and advanced features.

Parameter	Type	Default	Description
name	Text	[required]	The unique name of the team agent.
agents	List[Text | Agent]	[required]	A list of agent names or objects to include in the team agent.
llm_id	Text	"669a63646eb56306647e1091"	ID of the large language model (LLM) powering the team agent.
description	Text	""	A brief description of the team agent's purpose.
api_key	Text	config.TEAM_API_KEY	API key for authenticating requests to manage the team agent.
vendor	Dict | Text | Any | int	"aiXplain"	The owner or provider of the team agent.
version	Text | None	None	The version number of the team agent for tracking updates.
use_mentalist_and_inspector	bool	True	Enables advanced features for supervision and planning when a supervisor is used.

Team Agent Methods

Provides key functions to create, list, and retrieve team agents on the platform.

Method	Description
create	Creates a new Team Agent with specified parameters, agents, and configurations.
list	Lists all Team Agents available on the platform for the given API key.
get	Retrieves the details of a Team Agent using its unique ID.
run	Executes the Team Agent on specified input data or query, providing results.
run_async	Executes the Team Agent asynchronously, returning a polling URL for the response.
delete	Deletes the Team Agent configuration from the platform.
to_dict	Converts the Team Agent's configuration and metadata to a dictionary format.
validate	Validates the Team Agent by checking its agents, name, and associated LLM.
update	Updates the Team Agent with new configurations and metadata.
deploy	Deploys the Team Agent, changing its status from DRAFT to ONBOARDED.

Create a team agent by following this multi-purpose text agent tutorial.