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01 — Simple Agent

Create an agent with just a model and instructions. 
from definable.agents import Agent
from definable.models.openai import OpenAIChat

agent = Agent(
    model=OpenAIChat(id="gpt-4o-mini"),
    instructions="You are a helpful assistant.",
)

output = agent.run("What is the capital of Japan?")
print(output.content)
print(f"Tokens: {output.metrics.total_tokens}")

python definable/examples/agents/01_simple_agent.py

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02 — Agent with Tools

Give the agent tools to call during execution. 
from definable.agents import Agent
from definable.models.openai import OpenAIChat
from definable.tools.decorator import tool

@tool
def add(a: int, b: int) -> int:
    """Add two numbers together."""
    return a + b

@tool
def multiply(a: int, b: int) -> int:
    """Multiply two numbers together."""
    return a * b

agent = Agent(
    model=OpenAIChat(id="gpt-4o-mini"),
    tools=[add, multiply],
    instructions="You are a calculator. Use the provided tools.",
)

output = agent.run("What is 15 + 27, then multiply the result by 3?")
print(output.content)

python definable/examples/agents/02_agent_with_tools.py

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03 — Agent with Toolkit

Bundle related tools into a Toolkit class. 
from definable.agents import Agent, Toolkit
from definable.models.openai import OpenAIChat
from definable.tools.decorator import tool

class MathToolkit(Toolkit):
    @tool
    def add(self, a: int, b: int) -> int:
        """Add two numbers."""
        return a + b

    @tool
    def multiply(self, a: int, b: int) -> int:
        """Multiply two numbers."""
        return a * b

agent = Agent(
    model=OpenAIChat(id="gpt-4o-mini"),
    toolkits=[MathToolkit()],
)

output = agent.run("What is 5 + 3, then multiply by 2?")
print(output.content)

python definable/examples/agents/03_agent_with_toolkit.py

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04 — Multi-Turn Conversation

Maintain context across multiple interactions. 
from definable.agents import Agent
from definable.models.openai import OpenAIChat

agent = Agent(
    model=OpenAIChat(id="gpt-4o-mini"),
    instructions="You are a math tutor.",
)

# First turn
output1 = agent.run("What is 15 * 23?")
print(f"Turn 1: {output1.content}")

# Second turn — carries forward conversation
output2 = agent.run("Now divide that by 5.", messages=output1.messages)
print(f"Turn 2: {output2.content}")

python definable/examples/agents/04_multi_turn.py

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05 — Streaming Agent

Stream agent output in real time with typed events. 
from definable.agents import Agent
from definable.models.openai import OpenAIChat

agent = Agent(
    model=OpenAIChat(id="gpt-4o-mini"),
    instructions="You are a storyteller.",
)

for event in agent.run_stream("Tell me a short story about a robot."):
    if event.event == "RunContent":
        print(event.content, end="", flush=True)
    elif event.event == "ToolCallStarted":
        print(f"\n[Calling {event.tool_name}...]")
    elif event.event == "RunCompleted":
        print(f"\n\nDone. Tokens: {event.output.metrics.total_tokens}")

python definable/examples/agents/05_streaming_agent.py

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06 — Async Agent

Run agents asynchronously with arun() and arun_stream(). 
import asyncio
from definable.agents import Agent
from definable.models.openai import OpenAIChat

async def main():
    agent = Agent(
        model=OpenAIChat(id="gpt-4o-mini"),
        instructions="You are a helpful assistant.",
    )

    # Async run
    output = await agent.arun("Hello!")
    print(output.content)

    # Async streaming
    async for event in agent.arun_stream("Tell me a joke."):
        if event.event == "RunContent":
            print(event.content, end="", flush=True)

asyncio.run(main())

python definable/examples/agents/06_async_agent.py