Class Agent

src/agents/agent.py:270–977

An agent is an AI model configured with instructions, tools, guardrails, handoffs and more. We strongly recommend passing `instructions`, which is the "system prompt" for the agent. In addition, you can pass `handoff_description`, which is a human-readable description of the agent, used

@dataclass
class Agent(AgentBase, Generic[TContext]):
    """An agent is an AI model configured with instructions, tools, guardrails, handoffs and more.

    We strongly recommend passing `instructions`, which is the "system prompt" for the agent. In
    addition, you can pass `handoff_description`, which is a human-readable description of the
    agent, used when the agent is used inside tools/handoffs.

    Agents are generic on the context type. The context is a (mutable) object you create. It is
    passed to tool functions, handoffs, guardrails, etc.

    See `AgentBase` for base parameters that are shared with `RealtimeAgent`s.
    """

    instructions: (
        str
        | Callable[
            [RunContextWrapper[TContext], Agent[TContext]],
            MaybeAwaitable[str],
        ]
        | None
    ) = None
    """The instructions for the agent. Will be used as the "system prompt" when this agent is
    invoked. Describes what the agent should do, and how it responds.

    Can either be a string, or a function that dynamically generates instructions for the agent. If
    you provide a function, it will be called with the context and the agent instance. It must
    return a string.
    """

    prompt: Prompt | DynamicPromptFunction | None = None
    """A prompt object (or a function that returns a Prompt). Prompts allow you to dynamically
    configure the instructions, tools and other config for an agent outside of your code. Only
    usable with OpenAI models, using the Responses API.
    """

    handoffs: list[Agent[Any] | Handoff[TContext, Any]] = field(default_factory=list)
    """Handoffs are sub-agents that the agent can delegate to. You can provide a list of handoffs,
    and the agent can choose to delegate to them if relevant. Allows for separation of concerns and
    modularity.
    """

    model: str | Model | None = None
    """The model implementation to use when invoking the LLM.

    By default, if not set, the agent will use the default model configured in
    `agents.models.get_default_model()` (currently "gpt-5.4-mini").
    """

    model_settings: ModelSettings = field(default_factory=get_default_model_settings)
    """Configures model-specific tuning parameters (e.g. temperature, top_p).
    """

    input_guardrails: list[InputGuardrail[TContext]] = field(default_factory=list)
    """A list of checks that run in parallel to the agent's execution, before generating a
    response. Runs only if the agent is the first agent in the chain.
    """

    output_guardrails: list[OutputGuardrail[TContext]] = field(default_factory=list)