Agents

Introduction

Agents are PydanticAI's primary interface for interacting with LLMs.

In some use cases a single Agent will control an entire application or component, but multiple agents can also interact to embody more complex workflows.

The Agent class has full API documentation, but conceptually you can think of an agent as a container for:

Component	Description
System prompt(s)	A set of instructions for the LLM written by the developer.
Function tool(s)	Functions that the LLM may call to get information while generating a response.
Structured result type	The structured datatype the LLM must return at the end of a run, if specified.
Dependency type constraint	System prompt functions, tools, and result validators may all use dependencies when they're run.
LLM model	Optional default LLM model associated with the agent. Can also be specified when running the agent.
Model Settings	Optional default model settings to help fine tune requests. Can also be specified when running the agent.

In typing terms, agents are generic in their dependency and result types, e.g., an agent which required dependencies of type Foobar and returned results of type list[str] would have type Agent[Foobar, list[str]]. In practice, you shouldn't need to care about this, it should just mean your IDE can tell you when you have the right type, and if you choose to use static type checking it should work well with PydanticAI.

Here's a toy example of an agent that simulates a roulette wheel:

roulette_wheel.py

from pydantic_ai import Agent, RunContext

roulette_agent = Agent(  # (1)!
    'openai:gpt-4o',
    deps_type=int,
    result_type=bool,
    system_prompt=(
        'Use the `roulette_wheel` function to see if the '
        'customer has won based on the number they provide.'
    ),
)


@roulette_agent.tool
async def roulette_wheel(ctx: RunContext[int], square: int) -> str:  # (2)!
    """check if the square is a winner"""
    return 'winner' if square == ctx.deps else 'loser'


# Run the agent
success_number = 18  # (3)!
result = roulette_agent.run_sync('Put my money on square eighteen', deps=success_number)
print(result.data)  # (4)!
#> True

result = roulette_agent.run_sync('I bet five is the winner', deps=success_number)
print(result.data)
#> False

Create an agent, which expects an integer dependency and returns a boolean result. This agent will have type Agent[int, bool].
Define a tool that checks if the square is a winner. Here RunContext is parameterized with the dependency type int; if you got the dependency type wrong you'd get a typing error.
In reality, you might want to use a random number here e.g. random.randint(0, 36).
result.data will be a boolean indicating if the square is a winner. Pydantic performs the result validation, it'll be typed as a bool since its type is derived from the result_type generic parameter of the agent.

Agents are designed for reuse, like FastAPI Apps

Agents are intended to be instantiated once (frequently as module globals) and reused throughout your application, similar to a small FastAPI app or an APIRouter.

Running Agents

There are four ways to run an agent:

agent.run() — a coroutine which returns a RunResult containing a completed response.
agent.run_sync() — a plain, synchronous function which returns a RunResult containing a completed response (internally, this just calls loop.run_until_complete(self.run())).
agent.run_stream() — a coroutine which returns a StreamedRunResult, which contains methods to stream a response as an async iterable.
agent.iter() — a context manager which returns an AgentRun, an async-iterable over the nodes of the agent's underlying Graph.

Here's a simple example demonstrating the first three:

run_agent.py

from pydantic_ai import Agent

agent = Agent('openai:gpt-4o')

result_sync = agent.run_sync('What is the capital of Italy?')
print(result_sync.data)
#> Rome


async def main():
    result = await agent.run('What is the capital of France?')
    print(result.data)
    #> Paris

    async with agent.run_stream('What is the capital of the UK?') as response:
        print(await response.get_data())
        #> London

(This example is complete, it can be run "as is" — you'll need to add asyncio.run(main()) to run main)

You can also pass messages from previous runs to continue a conversation or provide context, as described in Messages and Chat History.

Iterating Over an Agent's Graph

Under the hood, each Agent in PydanticAI uses pydantic-graph to manage its execution flow. pydantic-graph is a generic, type-centric library for building and running finite state machines in Python. It doesn't actually depend on PydanticAI — you can use it standalone for workflows that have nothing to do with GenAI — but PydanticAI makes use of it to orchestrate the handling of model requests and model responses in an agent's run.

In many scenarios, you don't need to worry about pydantic-graph at all; calling agent.run(...) simply traverses the underlying graph from start to finish. However, if you need deeper insight or control — for example to capture each tool invocation, or to inject your own logic at specific stages — PydanticAI exposes the lower-level iteration process via Agent.iter. This method returns an AgentRun, which you can async-iterate over, or manually drive node-by-node via the next method. Once the agent's graph returns an End, you have the final result along with a detailed history of all steps.

`async for` iteration

Here's an example of using async for with iter to record each node the agent executes:

agent_iter_async_for.py

from pydantic_ai import Agent

agent = Agent('openai:gpt-4o')


async def main():
    nodes = []
    # Begin an AgentRun, which is an async-iterable over the nodes of the agent's graph
    async with agent.iter('What is the capital of France?') as agent_run:
        async for node in agent_run:
            # Each node represents a step in the agent's execution
            nodes.append(node)
    print(nodes)
    """
    [
        ModelRequestNode(
            request=ModelRequest(
                parts=[
                    UserPromptPart(
                        content='What is the capital of France?',
                        timestamp=datetime.datetime(...),
                        part_kind='user-prompt',
                    )
                ],
                kind='request',
            )
        ),
        CallToolsNode(
            model_response=ModelResponse(
                parts=[TextPart(content='Paris', part_kind='text')],
                model_name='gpt-4o',
                timestamp=datetime.datetime(...),
                kind='response',
            )
        ),
        End(data=FinalResult(data='Paris', tool_name=None, tool_call_id=None)),
    ]
    """
    print(agent_run.result.data)
    #> Paris

The AgentRun is an async iterator that yields each node (BaseNode or End) in the flow.
The run ends when an End node is returned.

Using `.next(...)` manually

You can also drive the iteration manually by passing the node you want to run next to the AgentRun.next(...) method. This allows you to inspect or modify the node before it executes or skip nodes based on your own logic, and to catch errors in next() more easily:

agent_iter_next.py

from pydantic_ai import Agent
from pydantic_graph import End

agent = Agent('openai:gpt-4o')


async def main():
    async with agent.iter('What is the capital of France?') as agent_run:
        node = agent_run.next_node  # (1)!

        all_nodes = [node]

        # Drive the iteration manually:
        while not isinstance(node, End):  # (2)!
            node = await agent_run.next(node)  # (3)!
            all_nodes.append(node)  # (4)!

        print(all_nodes)
        """
        [
            UserPromptNode(
                user_prompt='What is the capital of France?',
                system_prompts=(),
                system_prompt_functions=[],
                system_prompt_dynamic_functions={},
            ),
            ModelRequestNode(
                request=ModelRequest(
                    parts=[
                        UserPromptPart(
                            content='What is the capital of France?',
                            timestamp=datetime.datetime(...),
                            part_kind='user-prompt',
                        )
                    ],
                    kind='request',
                )
            ),
            CallToolsNode(
                model_response=ModelResponse(
                    parts=[TextPart(content='Paris', part_kind='text')],
                    model_name='gpt-4o',
                    timestamp=datetime.datetime(...),
                    kind='response',
                )
            ),
            End(data=FinalResult(data='Paris', tool_name=None, tool_call_id=None)),
        ]
        """

We start by grabbing the first node that will be run in the agent's graph.
The agent run is finished once an End node has been produced; instances of End cannot be passed to next.
When you call await agent_run.next(node), it executes that node in the agent's graph, updates the run's history, and returns the next node to run.
You could also inspect or mutate the new node here as needed.

Accessing usage and the final result

You can retrieve usage statistics (tokens, requests, etc.) at any time from the AgentRun object via agent_run.usage(). This method returns a Usage object containing the usage data.

Once the run finishes, agent_run.final_result becomes a AgentRunResult object containing the final output (and related metadata).

Streaming

Here is an example of streaming an agent run in combination with async for iteration:

streaming.py

import asyncio
from dataclasses import dataclass
from datetime import date

from pydantic_ai import Agent
from pydantic_ai.messages import (
    FinalResultEvent,
    FunctionToolCallEvent,
    FunctionToolResultEvent,
    PartDeltaEvent,
    PartStartEvent,
    TextPartDelta,
    ToolCallPartDelta,
)
from pydantic_ai.tools import RunContext


@dataclass
class WeatherService:
    async def get_forecast(self, location: str, forecast_date: date) -> str:
        # In real code: call weather API, DB queries, etc.
        return f'The forecast in {location} on {forecast_date} is 24°C and sunny.'

    async def get_historic_weather(self, location: str, forecast_date: date) -> str:
        # In real code: call a historical weather API or DB
        return (
            f'The weather in {location} on {forecast_date} was 18°C and partly cloudy.'
        )


weather_agent = Agent[WeatherService, str](
    'openai:gpt-4o',
    deps_type=WeatherService,
    result_type=str,  # We'll produce a final answer as plain text
    system_prompt='Providing a weather forecast at the locations the user provides.',
)


@weather_agent.tool
async def weather_forecast(
    ctx: RunContext[WeatherService],
    location: str,
    forecast_date: date,
) -> str:
    if forecast_date >= date.today():
        return await ctx.deps.get_forecast(location, forecast_date)
    else:
        return await ctx.deps.get_historic_weather(location, forecast_date)


output_messages: list[str] = []


async def main():
    user_prompt = 'What will the weather be like in Paris on Tuesday?'

    # Begin a node-by-node, streaming iteration
    async with weather_agent.iter(user_prompt, deps=WeatherService()) as run:
        async for node in run:
            if Agent.is_user_prompt_node(node):
                # A user prompt node => The user has provided input
                output_messages.append(f'=== UserPromptNode: {node.user_prompt} ===')
            elif Agent.is_model_request_node(node):
                # A model request node => We can stream tokens from the model's request
                output_messages.append(
                    '=== ModelRequestNode: streaming partial request tokens ==='
                )
                async with node.stream(run.ctx) as request_stream:
                    async for event in request_stream:
                        if isinstance(event, PartStartEvent):
                            output_messages.append(
                                f'[Request] Starting part {event.index}: {event.part!r}'
                            )
                        elif isinstance(event, PartDeltaEvent):
                            if isinstance(event.delta, TextPartDelta):
                                output_messages.append(
                                    f'[Request] Part {event.index} text delta: {event.delta.content_delta!r}'
                                )
                            elif isinstance(event.delta, ToolCallPartDelta):
                                output_messages.append(
                                    f'[Request] Part {event.index} args_delta={event.delta.args_delta}'
                                )
                        elif isinstance(event, FinalResultEvent):
                            output_messages.append(
                                f'[Result] The model produced a final result (tool_name={event.tool_name})'
                            )
            elif Agent.is_call_tools_node(node):
                # A handle-response node => The model returned some data, potentially calls a tool
                output_messages.append(
                    '=== CallToolsNode: streaming partial response & tool usage ==='
                )
                async with node.stream(run.ctx) as handle_stream:
                    async for event in handle_stream:
                        if isinstance(event, FunctionToolCallEvent):
                            output_messages.append(
                                f'[Tools] The LLM calls tool={event.part.tool_name!r} with args={event.part.args} (tool_call_id={event.part.tool_call_id!r})'
                            )
                        elif isinstance(event, FunctionToolResultEvent):
                            output_messages.append(
                                f'[Tools] Tool call {event.tool_call_id!r} returned => {event.result.content}'
                            )
            elif Agent.is_end_node(node):
                assert run.result.data == node.data.data
                # Once an End node is reached, the agent run is complete
                output_messages.append(f'=== Final Agent Output: {run.result.data} ===')


if __name__ == '__main__':
    asyncio.run(main())

    print(output_messages)
    """
    [
        '=== ModelRequestNode: streaming partial request tokens ===',
        '[Request] Starting part 0: ToolCallPart(tool_name=\'weather_forecast\', args=\'{"location":"Pa\', tool_call_id=\'0001\', part_kind=\'tool-call\')',
        '[Request] Part 0 args_delta=ris","forecast_',
        '[Request] Part 0 args_delta=date":"2030-01-',
        '[Request] Part 0 args_delta=01"}',
        '=== CallToolsNode: streaming partial response & tool usage ===',
        '[Tools] The LLM calls tool=\'weather_forecast\' with args={"location":"Paris","forecast_date":"2030-01-01"} (tool_call_id=\'0001\')',
        "[Tools] Tool call '0001' returned => The forecast in Paris on 2030-01-01 is 24°C and sunny.",
        '=== ModelRequestNode: streaming partial request tokens ===',
        "[Request] Starting part 0: TextPart(content='It will be ', part_kind='text')",
        '[Result] The model produced a final result (tool_name=None)',
        "[Request] Part 0 text delta: 'warm and sunny '",
        "[Request] Part 0 text delta: 'in Paris on '",
        "[Request] Part 0 text delta: 'Tuesday.'",
        '=== CallToolsNode: streaming partial response & tool usage ===',
        '=== Final Agent Output: It will be warm and sunny in Paris on Tuesday. ===',
    ]
    """

Additional Configuration

Usage Limits

PydanticAI offers a UsageLimits structure to help you limit your usage (tokens and/or requests) on model runs.

You can apply these settings by passing the usage_limits argument to the run{_sync,_stream} functions.

Consider the following example, where we limit the number of response tokens:

from pydantic_ai import Agent
from pydantic_ai.exceptions import UsageLimitExceeded
from pydantic_ai.usage import UsageLimits

agent = Agent('anthropic:claude-3-5-sonnet-latest')

result_sync = agent.run_sync(
    'What is the capital of Italy? Answer with just the city.',
    usage_limits=UsageLimits(response_tokens_limit=10),
)
print(result_sync.data)
#> Rome
print(result_sync.usage())
"""
Usage(requests=1, request_tokens=62, response_tokens=1, total_tokens=63, details=None)
"""

try:
    result_sync = agent.run_sync(
        'What is the capital of Italy? Answer with a paragraph.',
        usage_limits=UsageLimits(response_tokens_limit=10),
    )
except UsageLimitExceeded as e:
    print(e)
    #> Exceeded the response_tokens_limit of 10 (response_tokens=32)

Restricting the number of requests can be useful in preventing infinite loops or excessive tool calling:

from typing_extensions import TypedDict

from pydantic_ai import Agent, ModelRetry
from pydantic_ai.exceptions import UsageLimitExceeded
from pydantic_ai.usage import UsageLimits


class NeverResultType(TypedDict):
    """
    Never ever coerce data to this type.
    """

    never_use_this: str


agent = Agent(
    'anthropic:claude-3-5-sonnet-latest',
    retries=3,
    result_type=NeverResultType,
    system_prompt='Any time you get a response, call the `infinite_retry_tool` to produce another response.',
)


@agent.tool_plain(retries=5)  # (1)!
def infinite_retry_tool() -> int:
    raise ModelRetry('Please try again.')


try:
    result_sync = agent.run_sync(
        'Begin infinite retry loop!', usage_limits=UsageLimits(request_limit=3)  # (2)!
    )
except UsageLimitExceeded as e:
    print(e)
    #> The next request would exceed the request_limit of 3

This tool has the ability to retry 5 times before erroring, simulating a tool that might get stuck in a loop.
This run will error after 3 requests, preventing the infinite tool calling.

Note

This is especially relevant if you've registered many tools. The request_limit can be used to prevent the model from calling them in a loop too many times.

Model (Run) Settings

PydanticAI offers a settings.ModelSettings structure to help you fine tune your requests. This structure allows you to configure common parameters that influence the model's behavior, such as temperature, max_tokens, timeout, and more.

There are two ways to apply these settings: 1. Passing to run{_sync,_stream} functions via the model_settings argument. This allows for fine-tuning on a per-request basis. 2. Setting during Agent initialization via the model_settings argument. These settings will be applied by default to all subsequent run calls using said agent. However, model_settings provided during a specific run call will override the agent's default settings.

For example, if you'd like to set the temperature setting to 0.0 to ensure less random behavior, you can do the following:

from pydantic_ai import Agent

agent = Agent('openai:gpt-4o')

result_sync = agent.run_sync(
    'What is the capital of Italy?', model_settings={'temperature': 0.0}
)
print(result_sync.data)
#> Rome

Model specific settings

If you wish to further customize model behavior, you can use a subclass of ModelSettings, like GeminiModelSettings, associated with your model of choice.

For example:

from pydantic_ai import Agent, UnexpectedModelBehavior
from pydantic_ai.models.gemini import GeminiModelSettings

agent = Agent('google-gla:gemini-1.5-flash')

try:
    result = agent.run_sync(
        'Write a list of 5 very rude things that I might say to the universe after stubbing my toe in the dark:',
        model_settings=GeminiModelSettings(
            temperature=0.0,  # general model settings can also be specified
            gemini_safety_settings=[
                {
                    'category': 'HARM_CATEGORY_HARASSMENT',
                    'threshold': 'BLOCK_LOW_AND_ABOVE',
                },
                {
                    'category': 'HARM_CATEGORY_HATE_SPEECH',
                    'threshold': 'BLOCK_LOW_AND_ABOVE',
                },
            ],
        ),
    )
except UnexpectedModelBehavior as e:
    print(e)  # (1)!
    """
    Safety settings triggered, body:
    <safety settings details>
    """

This error is raised because the safety thresholds were exceeded. Generally, result would contain a normal ModelResponse.

Runs vs. Conversations

An agent run might represent an entire conversation — there's no limit to how many messages can be exchanged in a single run. However, a conversation might also be composed of multiple runs, especially if you need to maintain state between separate interactions or API calls.

Here's an example of a conversation comprised of multiple runs:

conversation_example.py

from pydantic_ai import Agent

agent = Agent('openai:gpt-4o')

# First run
result1 = agent.run_sync('Who was Albert Einstein?')
print(result1.data)
#> Albert Einstein was a German-born theoretical physicist.

# Second run, passing previous messages
result2 = agent.run_sync(
    'What was his most famous equation?',
    message_history=result1.new_messages(),  # (1)!
)
print(result2.data)
#> Albert Einstein's most famous equation is (E = mc^2).

Continue the conversation; without message_history the model would not know who "his" was referring to.

(This example is complete, it can be run "as is")

Type safe by design

PydanticAI is designed to work well with static type checkers, like mypy and pyright.

Typing is (somewhat) optional

PydanticAI is designed to make type checking as useful as possible for you if you choose to use it, but you don't have to use types everywhere all the time.

That said, because PydanticAI uses Pydantic, and Pydantic uses type hints as the definition for schema and validation, some types (specifically type hints on parameters to tools, and the result_type arguments to Agent) are used at runtime.

We (the library developers) have messed up if type hints are confusing you more than helping you, if you find this, please create an issue explaining what's annoying you!

In particular, agents are generic in both the type of their dependencies and the type of results they return, so you can use the type hints to ensure you're using the right types.

Consider the following script with type mistakes:

type_mistakes.py

from dataclasses import dataclass

from pydantic_ai import Agent, RunContext


@dataclass
class User:
    name: str


agent = Agent(
    'test',
    deps_type=User,  # (1)!
    result_type=bool,
)


@agent.system_prompt
def add_user_name(ctx: RunContext[str]) -> str:  # (2)!
    return f"The user's name is {ctx.deps}."


def foobar(x: bytes) -> None:
    pass


result = agent.run_sync('Does their name start with "A"?', deps=User('Anne'))
foobar(result.data)  # (3)!

The agent is defined as expecting an instance of User as deps.
But here add_user_name is defined as taking a str as the dependency, not a User.
Since the agent is defined as returning a bool, this will raise a type error since foobar expects bytes.

Running mypy on this will give the following output:

➤ uv run mypy type_mistakes.py
type_mistakes.py:18: error: Argument 1 to "system_prompt" of "Agent" has incompatible type "Callable[[RunContext[str]], str]"; expected "Callable[[RunContext[User]], str]"  [arg-type]
type_mistakes.py:28: error: Argument 1 to "foobar" has incompatible type "bool"; expected "bytes"  [arg-type]
Found 2 errors in 1 file (checked 1 source file)

Running pyright would identify the same issues.

System Prompts

System prompts might seem simple at first glance since they're just strings (or sequences of strings that are concatenated), but crafting the right system prompt is key to getting the model to behave as you want.

Generally, system prompts fall into two categories:

Static system prompts: These are known when writing the code and can be defined via the system_prompt parameter of the Agent constructor.
Dynamic system prompts: These depend in some way on context that isn't known until runtime, and should be defined via functions decorated with @agent.system_prompt.

You can add both to a single agent; they're appended in the order they're defined at runtime.

Here's an example using both types of system prompts:

system_prompts.py

from datetime import date

from pydantic_ai import Agent, RunContext

agent = Agent(
    'openai:gpt-4o',
    deps_type=str,  # (1)!
    system_prompt="Use the customer's name while replying to them.",  # (2)!
)


@agent.system_prompt  # (3)!
def add_the_users_name(ctx: RunContext[str]) -> str:
    return f"The user's name is {ctx.deps}."


@agent.system_prompt
def add_the_date() -> str:  # (4)!
    return f'The date is {date.today()}.'


result = agent.run_sync('What is the date?', deps='Frank')
print(result.data)
#> Hello Frank, the date today is 2032-01-02.

The agent expects a string dependency.
Static system prompt defined at agent creation time.
Dynamic system prompt defined via a decorator with RunContext, this is called just after run_sync, not when the agent is created, so can benefit from runtime information like the dependencies used on that run.
Another dynamic system prompt, system prompts don't have to have the RunContext parameter.

(This example is complete, it can be run "as is")

Reflection and self-correction

Validation errors from both function tool parameter validation and structured result validation can be passed back to the model with a request to retry.

You can also raise ModelRetry from within a tool or result validator function to tell the model it should retry generating a response.

The default retry count is 1 but can be altered for the entire agent, a specific tool, or a result validator.
You can access the current retry count from within a tool or result validator via ctx.retry.

Here's an example:

tool_retry.py

from pydantic import BaseModel

from pydantic_ai import Agent, RunContext, ModelRetry

from fake_database import DatabaseConn


class ChatResult(BaseModel):
    user_id: int
    message: str


agent = Agent(
    'openai:gpt-4o',
    deps_type=DatabaseConn,
    result_type=ChatResult,
)


@agent.tool(retries=2)
def get_user_by_name(ctx: RunContext[DatabaseConn], name: str) -> int:
    """Get a user's ID from their full name."""
    print(name)
    #> John
    #> John Doe
    user_id = ctx.deps.users.get(name=name)
    if user_id is None:
        raise ModelRetry(
            f'No user found with name {name!r}, remember to provide their full name'
        )
    return user_id


result = agent.run_sync(
    'Send a message to John Doe asking for coffee next week', deps=DatabaseConn()
)
print(result.data)
"""
user_id=123 message='Hello John, would you be free for coffee sometime next week? Let me know what works for you!'
"""

Model errors

If models behave unexpectedly (e.g., the retry limit is exceeded, or their API returns 503), agent runs will raise UnexpectedModelBehavior.

In these cases, capture_run_messages can be used to access the messages exchanged during the run to help diagnose the issue.

agent_model_errors.py

from pydantic_ai import Agent, ModelRetry, UnexpectedModelBehavior, capture_run_messages

agent = Agent('openai:gpt-4o')


@agent.tool_plain
def calc_volume(size: int) -> int:  # (1)!
    if size == 42:
        return size**3
    else:
        raise ModelRetry('Please try again.')


with capture_run_messages() as messages:  # (2)!
    try:
        result = agent.run_sync('Please get me the volume of a box with size 6.')
    except UnexpectedModelBehavior as e:
        print('An error occurred:', e)
        #> An error occurred: Tool exceeded max retries count of 1
        print('cause:', repr(e.__cause__))
        #> cause: ModelRetry('Please try again.')
        print('messages:', messages)
        """
        messages:
        [
            ModelRequest(
                parts=[
                    UserPromptPart(
                        content='Please get me the volume of a box with size 6.',
                        timestamp=datetime.datetime(...),
                        part_kind='user-prompt',
                    )
                ],
                kind='request',
            ),
            ModelResponse(
                parts=[
                    ToolCallPart(
                        tool_name='calc_volume',
                        args={'size': 6},
                        tool_call_id='pyd_ai_tool_call_id',
                        part_kind='tool-call',
                    )
                ],
                model_name='gpt-4o',
                timestamp=datetime.datetime(...),
                kind='response',
            ),
            ModelRequest(
                parts=[
                    RetryPromptPart(
                        content='Please try again.',
                        tool_name='calc_volume',
                        tool_call_id='pyd_ai_tool_call_id',
                        timestamp=datetime.datetime(...),
                        part_kind='retry-prompt',
                    )
                ],
                kind='request',
            ),
            ModelResponse(
                parts=[
                    ToolCallPart(
                        tool_name='calc_volume',
                        args={'size': 6},
                        tool_call_id='pyd_ai_tool_call_id',
                        part_kind='tool-call',
                    )
                ],
                model_name='gpt-4o',
                timestamp=datetime.datetime(...),
                kind='response',
            ),
        ]
        """
    else:
        print(result.data)

Define a tool that will raise ModelRetry repeatedly in this case.
capture_run_messages is used to capture the messages exchanged during the run.

(This example is complete, it can be run "as is")

Note

If you call run, run_sync, or run_stream more than once within a single capture_run_messages context, messages will represent the messages exchanged during the first call only.

Agents

Introduction

Running Agents

Iterating Over an Agent's Graph

async for iteration

Using .next(...) manually

Accessing usage and the final result

Streaming

Additional Configuration

Usage Limits

Model (Run) Settings

Model specific settings

Runs vs. Conversations

Type safe by design

System Prompts

Reflection and self-correction

Model errors

`async for` iteration

Using `.next(...)` manually