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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

AgentConfigBuilder

::: agenticai_core.designtime.models.agent.AgentConfigBuilder 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

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
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="WORKER",
    sub_type="REACT",
    type="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 AgentConfigBuilder

agent_dict = AgentConfigBuilder() \
    .set_name("CustomerService") \
    .set_description("Customer service agent") \
    .set_role("WORKER") \
    .set_sub_type("REACT") \
    .set_type("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]
  • LlmModel - Configure the agent's LLM
  • Prompt - Define agent behavior with prompts
  • Tool - Add capabilities to agents
  • Icon - Visual identification