Agents

Agents#

Agents are the core building block in your apps. An agent is a large language model (LLM), configured with instructions and tools.

Basic configuration#

The most common properties of an agent you’ll configure are:

instructions: also known as a developer message or system prompt.
model: which LLM to use, and optional model_settings to configure model tuning parameters like temperature, top_p, etc.
tools: Tools that the agent can use to achieve its tasks.

from agents import Agent, ModelSettings, function_tool

@function_tool
def get_weather(city: str) -> str:
    return f"The weather in {city} is sunny"

agent = Agent(
    name="Haiku agent",
    instructions="Always respond in haiku form",
    model="o3-mini",
    tools=[get_weather],
)

Context#

Agents are generic on their context type. Context is a dependency-injection tool: it’s an object you create and pass to Runner.run(), that is passed to every agent, tool, handoff etc, and it serves as a grab bag of dependencies and state for the agent run. You can provide any Python object as the context.

@dataclass
class UserContext:
    uid: str
    is_pro_user: bool

    async def fetch_purchases() -> list[Purchase]:
        return ...

agent = Agent[UserContext](
    ...,
)

Output types#

By default, agents produce plain text (i.e. str) outputs. If you want the agent to produce a particular type of output, you can use the output_type parameter. A common choice is to use Pydantic objects, but we support any type that can be wrapped in a Pydantic TypeAdapter - dataclasses, lists, TypedDict, etc.

from pydantic import BaseModel
from agents import Agent


class CalendarEvent(BaseModel):
    name: str
    date: str
    participants: list[str]

agent = Agent(
    name="Calendar extractor",
    instructions="Extract calendar events from text",
    output_type=CalendarEvent,
)

!!! note

When you pass an `output_type`, that tells the model to use [structured outputs](https://platform.openai.com/docs/guides/structured-outputs) instead of regular plain text responses.

Handoffs#

Handoffs are sub-agents that the agent can delegate to. You provide a list of handoffs, and the agent can choose to delegate to them if relevant. This is a powerful pattern that allows orchestrating modular, specialized agents that excel at a single task. Read more in the handoffs documentation.

from agents import Agent

booking_agent = Agent(...)
refund_agent = Agent(...)

triage_agent = Agent(
    name="Triage agent",
    instructions=(
        "Help the user with their questions."
        "If they ask about booking, handoff to the booking agent."
        "If they ask about refunds, handoff to the refund agent."
    ),
    handoffs=[booking_agent, refund_agent],
)

Dynamic instructions#

In most cases, you can provide instructions when you create the agent. However, you can also provide dynamic instructions via a function. The function will receive the agent and context, and must return the prompt. Both regular and async functions are accepted.

def dynamic_instructions(
    context: RunContextWrapper[UserContext], agent: Agent[UserContext]
) -> str:
    return f"The user's name is {context.context.name}. Help them with their questions."


agent = Agent[UserContext](
    name="Triage agent",
    instructions=dynamic_instructions,
)

Lifecycle events (hooks)#

Sometimes, you want to observe the lifecycle of an agent. For example, you may want to log events, or pre-fetch data when certain events occur. You can hook into the agent lifecycle with the hooks property. Subclass the [AgentHooks][agents.lifecycle.AgentHooks] class, and override the methods you’re interested in.

Guardrails#

Guardrails allow you to run checks/validations on user input, in parallel to the agent running. For example, you could screen the user’s input for relevance. Read more in the guardrails documentation.

Cloning/copying agents#

By using the clone() method on an agent, you can duplicate an Agent, and optionally change any properties you like.

pirate_agent = Agent(
    name="Pirate",
    instructions="Write like a pirate",
    model="o3-mini",
)

robot_agent = pirate_agent.clone(
    name="Robot",
    instructions="Write like a robot",
)

Forcing tool use#

Supplying a list of tools doesn’t always mean the LLM will use a tool. You can force tool use by setting [ModelSettings.tool_choice][agents.model_settings.ModelSettings.tool_choice]. Valid values are:

auto, which allows the LLM to decide whether or not to use a tool.
required, which requires the LLM to use a tool (but it can intelligently decide which tool).
none, which requires the LLM to not use a tool.
Setting a specific string e.g. my_tool, which requires the LLM to use that specific tool.