Agents - Complete Reference

Agent Constructor

from agents import Agent

agent = Agent(
    # Required
    name: str,                          # Agent identifier

    # Instructions (choose one)
    instructions: str | Callable,       # System prompt or dynamic function

    # Model Configuration
    model: str = "gpt-4.1",            # Model to use
    model_settings: ModelSettings,      # Temperature, top_p, etc.

    # Capabilities
    tools: list[Tool] = [],            # Function tools
    mcp_servers: list[MCPServer] = [], # MCP server tools
    handoffs: list[Agent | Handoff],   # Delegation targets

    # Output
    output_type: type[BaseModel],      # Structured output schema

    # Safety
    input_guardrails: list[Guardrail],
    output_guardrails: list[Guardrail],

    # Advanced
    reset_tool_choice: bool = True,    # Prevent tool loops
    hooks: AgentHooks,                 # Lifecycle callbacks
)

Dynamic Instructions

Instructions can be a string or a function for runtime customization:

def dynamic_instructions(
    context: RunContextWrapper[UserContext],
    agent: Agent[UserContext]
) -> str:
    user = context.context
    return f"""You are a financial assistant for {user.name}.
    User ID: {user.user_id}
    Premium status: {user.is_premium}

    Help them manage their finances."""

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

Context (Dependency Injection)

Context passes data to agents, tools, and handoffs without exposing to LLM:

from dataclasses import dataclass
from agents import Agent, Runner, RunContextWrapper, function_tool

@dataclass
class AppContext:
    user_id: str
    db_connection: DatabasePool
    logger: Logger

# Tool receives context
@function_tool
async def get_user_data(ctx: RunContextWrapper[AppContext]) -> str:
    """Fetch user data."""
    db = ctx.context.db_connection
    user_id = ctx.context.user_id
    return await db.fetch_user(user_id)

# Agent typed with context
agent = Agent[AppContext](
    name="DataAgent",
    instructions="Fetch and analyze user data.",
    tools=[get_user_data],
)

# Pass context at runtime
app_ctx = AppContext(
    user_id="user_123",
    db_connection=pool,
    logger=logger,
)

result = await Runner.run(agent, "Get my data", context=app_ctx)

ToolContext (Extended Context)

For tool-specific metadata:

from agents import function_tool
from agents.tool import ToolContext

@function_tool
def log_tool_call(ctx: ToolContext[AppContext], data: str) -> str:
    """Tool with access to call metadata."""
    print(f"Tool: {ctx.tool_name}")
    print(f"Call ID: {ctx.tool_call_id}")
    print(f"Arguments: {ctx.tool_arguments}")
    return f"Processed: {data}"

Structured Output

Force agent to return typed data:

from pydantic import BaseModel, Field
from agents import Agent

class TransactionAnalysis(BaseModel):
    total_amount: float = Field(description="Sum of all transactions")
    category_breakdown: dict[str, float] = Field(description="Spending by category")
    insights: list[str] = Field(description="Key observations")
    risk_level: str = Field(description="low, medium, or high")

agent = Agent(
    name="Transaction Analyzer",
    instructions="Analyze the provided transactions.",
    output_type=TransactionAnalysis,
)

result = await Runner.run(agent, transactions_text)
analysis: TransactionAnalysis = result.final_output
print(f"Total: {analysis.total_amount}")
print(f"Risk: {analysis.risk_level}")

Model Settings

from agents import Agent, ModelSettings
from agents.models import Reasoning

# Basic settings
agent = Agent(
    model="gpt-4.1",
    model_settings=ModelSettings(
        temperature=0.7,
        top_p=0.9,
        max_tokens=4096,
    ),
)

# GPT-5 with reasoning
agent = Agent(
    model="gpt-5.2",
    model_settings=ModelSettings(
        reasoning=Reasoning(effort="high"),
        verbosity="low",
    ),
)

# Force specific tool
agent = Agent(
    tools=[get_weather, get_news],
    model_settings=ModelSettings(tool_choice="get_weather"),
)

Tool Choice Options

Agent Cloning

Create variations of an agent:

base_agent = Agent(
    name="Base",
    instructions="You are helpful.",
    tools=[get_data],
)

# Clone with modifications
formal_agent = base_agent.clone(
    name="Formal",
    instructions="You are helpful. Always use formal language.",
)

casual_agent = base_agent.clone(
    name="Casual",
    instructions="You are helpful. Be casual and friendly.",
)

Lifecycle Hooks

Monitor agent execution:

from agents import Agent, AgentHooks

class MyHooks(AgentHooks):
    async def on_run_start(self, context, agent):
        print(f"Starting run with {agent.name}")

    async def on_run_end(self, context, agent, result):
        print(f"Completed with: {result.final_output[:100]}")

    async def on_tool_start(self, context, agent, tool):
        print(f"Calling tool: {tool.name}")

    async def on_tool_end(self, context, agent, tool, result):
        print(f"Tool returned: {result[:100]}")

agent = Agent(
    name="Monitored Agent",
    hooks=MyHooks(),
)

Using Non-OpenAI Models

LiteLLM Integration

pip install "openai-agents[litellm]"

from agents import Agent

# Anthropic Claude
claude_agent = Agent(
    name="Claude Agent",
    model="litellm/anthropic/claude-3-5-sonnet-20240620",
    instructions="You are helpful.",
)

# Google Gemini
gemini_agent = Agent(
    name="Gemini Agent",
    model="litellm/gemini/gemini-2.5-flash-preview-04-17",
    instructions="You are helpful.",
)

Custom OpenAI Client

from openai import AsyncOpenAI
from agents import set_default_openai_client

# Use custom endpoint (Azure, local, etc.)
client = AsyncOpenAI(
    api_key="your-key",
    base_url="https://your-endpoint.com/v1",
)

set_default_openai_client(client)

Prompt Templates (OpenAI Platform)

Reference stored prompts:

agent = Agent(
    name="Templated Agent",
    prompt={
        "id": "pmpt_abc123",
        "version": "1",
        "variables": {
            "tone": "professional",
            "domain": "finance",
        },
    },
)