Agent

::: agenticai_core.designtime.models.agent.Agent options: show_root_heading: true show_source: false members_order: source

AgentMeta

::: agenticai_core.designtime.models.agent.AgentMeta options: show_root_heading: true show_source: false

AgentConfig

::: agenticai_core.designtime.models.agent.AgentConfig options: show_root_heading: true show_source: false

AgentBuilder

::: agenticai_core.designtime.models.agent.AgentBuilder options: show_root_heading: true show_source: false show_if_no_docstring: true show_signature: true show_signature_annotations: true filters: - "!^_" # Hide private methods but show public ones

Supporting Classes

Communication

::: agenticai_core.designtime.models.agent.Communication options: show_root_heading: true

AgentSubType

::: agenticai_core.designtime.models.agent.AgentSubType options: show_root_heading: true

AgentType

::: agenticai_core.designtime.models.agent.AgentType options: show_root_heading: true

AgentRole

::: agenticai_core.designtime.models.agent.AgentRole options: show_root_heading: true

CommunicationType

::: agenticai_core.designtime.models.agent.CommunicationType options: show_root_heading: true

Endpoint

::: agenticai_core.designtime.models.agent.Endpoint options: show_root_heading: true

ResponseRoutingMode

::: agenticai_core.designtime.models.agent.ResponseRoutingMode options: show_root_heading: true

Usage Examples

Creating a Basic Agent

from agenticai_core.designtime.models.agent import Agent, AgentRole, AgentSubType, AgentType
from agenticai_core.designtime.models.llm_model import LlmModel, LlmModelConfig
from agenticai_core.designtime.models.prompt import Prompt
from agenticai_core.designtime.models.tool import Tool

agent = Agent(
    name="FinanceAssist",
    description="Banking assistant for account management",
    role=AgentRole.WORKER,
    sub_type=AgentSubType.REACT,
    type=AgentType.AUTONOMOUS,
    llm_model=LlmModel(
        model="gpt-4o",
        provider="Open AI",
        connection_name="Default Connection",
        modelConfig=LlmModelConfig(
            temperature=0.7,
            max_tokens=1600
        )
    ),
    prompt=Prompt(
        system="You are a helpful banking assistant.",
        custom="Assist with account inquiries and transactions."
    ),
    tools=[
        Tool(name="Get_Balance", type="inlineTool", ...)
    ]
)

Using Builder Pattern

from agenticai_core.designtime.models.agent import AgentBuilder, AgentRole, AgentSubType, AgentType

agent_dict = AgentBuilder() \
    .set_name("CustomerService") \
    .set_description("Customer service agent") \
    .set_role(AgentRole.WORKER) \
    .set_sub_type(AgentSubType.REACT) \
    .set_type(AgentType.AUTONOMOUS) \
    .set_llm_model(llm_model) \
    .set_prompt(prompt) \
    .set_tools([tool1, tool2]) \
    .build()

agent = Agent(**agent_dict)

Converting to AgentMeta

# Convert full Agent to lightweight AgentMeta
agent_meta = agent.to_agent_meta()

# Use in orchestrator registration
app_agents = [agent.to_agent_meta() for agent in agents]

Related Models

• LlmModel - Configure the agent's LLM
• Prompt - Define agent behavior with prompts
• Tool - Add capabilities to agents
• Icon - Visual identification

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