Toolsets

A toolset represents a collection of tools that can be registered with an agent in one go. They can be reused by different agents, swapped out at runtime or during testing, and composed in order to dynamically filter which tools are available, modify tool definitions, or change tool execution behavior. A toolset can contain locally defined functions, depend on an external service to provide them, or implement custom logic to list available tools and handle them being called.

Toolsets are used (among many other things) to define MCP servers available to an agent. Pydantic AI includes many kinds of toolsets which are described below, and you can define a custom toolset by inheriting from the AbstractToolset class.

The toolsets that will be available during an agent run can be specified in four different ways:

• at agent construction time, via the toolsets keyword argument to Agent, which takes toolset instances as well as functions that generate toolsets dynamically based on the agent run context
• at agent run time, via the toolsets keyword argument to agent.run(), agent.run_sync(), agent.run_stream(), or agent.iter(). These toolsets will be additional to those registered on the Agent
• dynamically, via the @agent.toolset decorator which lets you build a toolset based on the agent run context
• as a contextual override, via the toolsets keyword argument to the agent.override() context manager. These toolsets will replace those provided at agent construction or run time during the life of the context manager

toolsets.py

from pydantic_ai import Agent
from pydantic_ai.models.test import TestModel
from pydantic_ai.toolsets import FunctionToolset


def agent_tool():
    return "I'm registered directly on the agent"


def extra_tool():
    return "I'm passed as an extra tool for a specific run"


def override_tool():
    return 'I override all other tools'


agent_toolset = FunctionToolset(tools=[agent_tool]) # (1)!
extra_toolset = FunctionToolset(tools=[extra_tool])
override_toolset = FunctionToolset(tools=[override_tool])

test_model = TestModel() # (2)!
agent = Agent(test_model, toolsets=[agent_toolset])

result = agent.run_sync('What tools are available?')
print([t.name for t in test_model.last_model_request_parameters.function_tools])
#> ['agent_tool']

result = agent.run_sync('What tools are available?', toolsets=[extra_toolset])
print([t.name for t in test_model.last_model_request_parameters.function_tools])
#> ['agent_tool', 'extra_tool']

with agent.override(toolsets=[override_toolset]):
    result = agent.run_sync('What tools are available?', toolsets=[extra_toolset]) # (3)!
    print([t.name for t in test_model.last_model_request_parameters.function_tools])
    #> ['override_tool']

1. The FunctionToolset will be explained in detail in the next section.
2. We're using TestModel here because it makes it easy to see which tools were available on each run.
3. This extra_toolset will be ignored because we're inside an override context.

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

Function Toolset

As the name suggests, a FunctionToolset makes locally defined functions available as tools.

Functions can be added as tools in three different ways:

• via the @toolset.tool decorator
• via the tools keyword argument to the constructor which can take either plain functions, or instances of Tool
• via the toolset.add_function() and toolset.add_tool() methods which can take a plain function or an instance of Tool respectively

Functions registered in any of these ways can define an initial ctx: RunContext argument in order to receive the agent run context. The add_function() and add_tool() methods can also be used from a tool function to dynamically register new tools during a run to be available in future run steps.

function_toolset.py

from datetime import datetime

from pydantic_ai import Agent, RunContext
from pydantic_ai.models.test import TestModel
from pydantic_ai.toolsets import FunctionToolset


def temperature_celsius(city: str) -> float:
    return 21.0


def temperature_fahrenheit(city: str) -> float:
    return 69.8


weather_toolset = FunctionToolset(tools=[temperature_celsius, temperature_fahrenheit])


@weather_toolset.tool
def conditions(ctx: RunContext, city: str) -> str:
    if ctx.run_step % 2 == 0:
        return "It's sunny"
    else:
        return "It's raining"


datetime_toolset = FunctionToolset()
datetime_toolset.add_function(lambda: datetime.now(), name='now')

test_model = TestModel()  # (1)!
agent = Agent(test_model)

result = agent.run_sync('What tools are available?', toolsets=[weather_toolset])
print([t.name for t in test_model.last_model_request_parameters.function_tools])
#> ['temperature_celsius', 'temperature_fahrenheit', 'conditions']

result = agent.run_sync('What tools are available?', toolsets=[datetime_toolset])
print([t.name for t in test_model.last_model_request_parameters.function_tools])
#> ['now']

1. We're using TestModel here because it makes it easy to see which tools were available on each run.

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

Toolset Composition

Toolsets can be composed to dynamically filter which tools are available, modify tool definitions, or change tool execution behavior. Multiple toolsets can also be combined into one.

Combining Toolsets

CombinedToolset takes a list of toolsets and lets them be used as one.

combined_toolset.py

from pydantic_ai import Agent
from pydantic_ai.models.test import TestModel
from pydantic_ai.toolsets import CombinedToolset

from function_toolset import datetime_toolset, weather_toolset

combined_toolset = CombinedToolset([weather_toolset, datetime_toolset])

test_model = TestModel() # (1)!
agent = Agent(test_model, toolsets=[combined_toolset])
result = agent.run_sync('What tools are available?')
print([t.name for t in test_model.last_model_request_parameters.function_tools])
#> ['temperature_celsius', 'temperature_fahrenheit', 'conditions', 'now']

1. We're using TestModel here because it makes it easy to see which tools were available on each run.

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

Filtering Tools

FilteredToolset wraps a toolset and filters available tools ahead of each step of the run based on a user-defined function that is passed the agent run context and each tool's ToolDefinition and returns a boolean to indicate whether or not a given tool should be available.

To easily chain different modifications, you can also call filtered() on any toolset instead of directly constructing a FilteredToolset.

filtered_toolset.py

from pydantic_ai import Agent
from pydantic_ai.models.test import TestModel

from combined_toolset import combined_toolset

filtered_toolset = combined_toolset.filtered(lambda ctx, tool_def: 'fahrenheit' not in tool_def.name)

test_model = TestModel() # (1)!
agent = Agent(test_model, toolsets=[filtered_toolset])
result = agent.run_sync('What tools are available?')
print([t.name for t in test_model.last_model_request_parameters.function_tools])
#> ['weather_temperature_celsius', 'weather_conditions', 'datetime_now']

1. We're using TestModel here because it makes it easy to see which tools were available on each run.

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

Prefixing Tool Names

PrefixedToolset wraps a toolset and adds a prefix to each tool name to prevent tool name conflicts between different toolsets.

To easily chain different modifications, you can also call prefixed() on any toolset instead of directly constructing a PrefixedToolset.

combined_toolset.py

from pydantic_ai import Agent
from pydantic_ai.models.test import TestModel
from pydantic_ai.toolsets import CombinedToolset

from function_toolset import datetime_toolset, weather_toolset

combined_toolset = CombinedToolset(
    [
        weather_toolset.prefixed('weather'),
        datetime_toolset.prefixed('datetime')
    ]
)

test_model = TestModel() # (1)!
agent = Agent(test_model, toolsets=[combined_toolset])
result = agent.run_sync('What tools are available?')
print([t.name for t in test_model.last_model_request_parameters.function_tools])
"""
[
    'weather_temperature_celsius',
    'weather_temperature_fahrenheit',
    'weather_conditions',
    'datetime_now',
]
"""

1. We're using TestModel here because it makes it easy to see which tools were available on each run.

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

Renaming Tools

RenamedToolset wraps a toolset and lets you rename tools using a dictionary mapping new names to original names. This is useful when the names provided by a toolset are ambiguous or would conflict with tools defined by other toolsets, but prefixing them creates a name that is unnecessarily long or could be confusing to the model.

To easily chain different modifications, you can also call renamed() on any toolset instead of directly constructing a RenamedToolset.

renamed_toolset.py

from pydantic_ai import Agent
from pydantic_ai.models.test import TestModel

from combined_toolset import combined_toolset

renamed_toolset = combined_toolset.renamed(
    {
        'current_time': 'datetime_now',
        'temperature_celsius': 'weather_temperature_celsius',
        'temperature_fahrenheit': 'weather_temperature_fahrenheit'
    }
)

test_model = TestModel() # (1)!
agent = Agent(test_model, toolsets=[renamed_toolset])
result = agent.run_sync('What tools are available?')
print([t.name for t in test_model.last_model_request_parameters.function_tools])
"""
['temperature_celsius', 'temperature_fahrenheit', 'weather_conditions', 'current_time']
"""

1. We're using TestModel here because it makes it easy to see which tools were available on each run.

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

Dynamic Tool Definitions

PreparedToolset lets you modify the entire list of available tools ahead of each step of the agent run using a user-defined function that takes the agent run context and a list of ToolDefinitions and returns a list of modified ToolDefinitions.

This is the toolset-specific equivalent of the prepare_tools argument to Agent that prepares all tool definitions registered on an agent across toolsets.

Note that it is not possible to add or rename tools using PreparedToolset. Instead, you can use FunctionToolset.add_function() or RenamedToolset.

To easily chain different modifications, you can also call prepared() on any toolset instead of directly constructing a PreparedToolset.

prepared_toolset.py

from dataclasses import replace

from pydantic_ai import Agent, RunContext, ToolDefinition
from pydantic_ai.models.test import TestModel

from renamed_toolset import renamed_toolset

descriptions = {
    'temperature_celsius': 'Get the temperature in degrees Celsius',
    'temperature_fahrenheit': 'Get the temperature in degrees Fahrenheit',
    'weather_conditions': 'Get the current weather conditions',
    'current_time': 'Get the current time',
}

async def add_descriptions(ctx: RunContext, tool_defs: list[ToolDefinition]) -> list[ToolDefinition] | None:
    return [
        replace(tool_def, description=description)
        if (description := descriptions.get(tool_def.name, None))
        else tool_def
        for tool_def
        in tool_defs
    ]

prepared_toolset = renamed_toolset.prepared(add_descriptions)

test_model = TestModel() # (1)!
agent = Agent(test_model, toolsets=[prepared_toolset])
result = agent.run_sync('What tools are available?')
print(test_model.last_model_request_parameters.function_tools)
"""
[
    ToolDefinition(
        name='temperature_celsius',
        parameters_json_schema={
            'additionalProperties': False,
            'properties': {'city': {'type': 'string'}},
            'required': ['city'],
            'type': 'object',
        },
        description='Get the temperature in degrees Celsius',
    ),
    ToolDefinition(
        name='temperature_fahrenheit',
        parameters_json_schema={
            'additionalProperties': False,
            'properties': {'city': {'type': 'string'}},
            'required': ['city'],
            'type': 'object',
        },
        description='Get the temperature in degrees Fahrenheit',
    ),
    ToolDefinition(
        name='weather_conditions',
        parameters_json_schema={
            'additionalProperties': False,
            'properties': {'city': {'type': 'string'}},
            'required': ['city'],
            'type': 'object',
        },
        description='Get the current weather conditions',
    ),
    ToolDefinition(
        name='current_time',
        parameters_json_schema={
            'additionalProperties': False,
            'properties': {},
            'type': 'object',
        },
        description='Get the current time',
    ),
]
"""

1. We're using TestModel here because it makes it easy to see which tools were available on each run.

Requiring Tool Approval

ApprovalRequiredToolset wraps a toolset and lets you dynamically require approval for a given tool call based on a user-defined function that is passed the agent run context, the tool's ToolDefinition, and the validated tool call arguments. If no function is provided, all tool calls will require approval.

To easily chain different modifications, you can also call approval_required() on any toolset instead of directly constructing a ApprovalRequiredToolset.

See the Human-in-the-Loop Tool Approval documentation for more information on how to handle agent runs that call tools that require approval and how to pass in the results.

approval_required_toolset.py

from pydantic_ai import Agent, DeferredToolRequests, DeferredToolResults
from pydantic_ai.models.test import TestModel

from prepared_toolset import prepared_toolset

approval_required_toolset = prepared_toolset.approval_required(lambda ctx, tool_def, tool_args: tool_def.name.startswith('temperature'))

test_model = TestModel(call_tools=['temperature_celsius', 'temperature_fahrenheit']) # (1)!
agent = Agent(
    test_model,
    toolsets=[approval_required_toolset],
    output_type=[str, DeferredToolRequests],
)
result = agent.run_sync('Call the temperature tools')
messages = result.all_messages()
print(result.output)
"""
DeferredToolRequests(
    calls=[],
    approvals=[
        ToolCallPart(
            tool_name='temperature_celsius',
            args={'city': 'a'},
            tool_call_id='pyd_ai_tool_call_id__temperature_celsius',
        ),
        ToolCallPart(
            tool_name='temperature_fahrenheit',
            args={'city': 'a'},
            tool_call_id='pyd_ai_tool_call_id__temperature_fahrenheit',
        ),
    ],
)
"""

result = agent.run_sync(
    message_history=messages,
    deferred_tool_results=DeferredToolResults(
        approvals={
            'pyd_ai_tool_call_id__temperature_celsius': True,
            'pyd_ai_tool_call_id__temperature_fahrenheit': False,
        }
    )
)
print(result.output)
#> {"temperature_celsius":21.0,"temperature_fahrenheit":"The tool call was denied."}

1. We're using TestModel here because it makes it easy to specify which tools to call.

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

Changing Tool Execution

WrapperToolset wraps another toolset and delegates all responsibility to it.

It is is a no-op by default, but you can subclass WrapperToolset to change the wrapped toolset's tool execution behavior by overriding the call_tool() method.

logging_toolset.py

import asyncio

from typing_extensions import Any

from pydantic_ai import Agent, RunContext
from pydantic_ai.models.test import TestModel
from pydantic_ai.toolsets import ToolsetTool, WrapperToolset

from prepared_toolset import prepared_toolset

LOG = []

class LoggingToolset(WrapperToolset):
    async def call_tool(self, name: str, tool_args: dict[str, Any], ctx: RunContext, tool: ToolsetTool) -> Any:
        LOG.append(f'Calling tool {name!r} with args: {tool_args!r}')
        try:
            await asyncio.sleep(0.1 * len(LOG)) # (1)!

            result = await super().call_tool(name, tool_args, ctx, tool)
            LOG.append(f'Finished calling tool {name!r} with result: {result!r}')
        except Exception as e:
            LOG.append(f'Error calling tool {name!r}: {e}')
            raise e
        else:
            return result


logging_toolset = LoggingToolset(prepared_toolset)

agent = Agent(TestModel(), toolsets=[logging_toolset]) # (2)!
result = agent.run_sync('Call all the tools')
print(LOG)
"""
[
    "Calling tool 'temperature_celsius' with args: {'city': 'a'}",
    "Calling tool 'temperature_fahrenheit' with args: {'city': 'a'}",
    "Calling tool 'weather_conditions' with args: {'city': 'a'}",
    "Calling tool 'current_time' with args: {}",
    "Finished calling tool 'temperature_celsius' with result: 21.0",
    "Finished calling tool 'temperature_fahrenheit' with result: 69.8",
    'Finished calling tool \'weather_conditions\' with result: "It\'s raining"',
    "Finished calling tool 'current_time' with result: datetime.datetime(...)",
]
"""

1. All docs examples are tested in CI and their their output is verified, so we need LOG to always have the same order whenever this code is run. Since the tools could finish in any order, we sleep an increasing amount of time based on which number tool call we are to ensure that they finish (and log) in the same order they were called in.
2. We use TestModel here as it will automatically call each tool.

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

External Toolset

If your agent needs to be able to call external tools that are provided and executed by an upstream service or frontend, you can build an ExternalToolset from a list of ToolDefinitions containing the tool names, arguments JSON schemas, and descriptions.

When the model calls an external tool, the call is considered to be "deferred", and the agent run will end with a DeferredToolRequests output object with a calls list holding ToolCallParts containing the tool name, validated arguments, and a unique tool call ID, which are expected to be passed to the upstream service or frontend that will produce the results.

When the tool call results are received from the upstream service or frontend, you can build a DeferredToolResults object with a calls dictionary that maps each tool call ID to an arbitrary value to be returned to the model, a ToolReturn object, or a ModelRetry exception in case the tool call failed and the model should try again. This DeferredToolResults object can then be provided to one of the agent run methods as deferred_tool_results, alongside the original run's message history.

Note that you need to add DeferredToolRequests to the Agent's or agent.run()'s output_type so that the possible types of the agent run output are correctly inferred. For more information, see the Deferred Tools documentation.

To demonstrate, let us first define a simple agent without deferred tools:

deferred_toolset_agent.py

from pydantic import BaseModel

from pydantic_ai import Agent
from pydantic_ai.toolsets.function import FunctionToolset

toolset = FunctionToolset()


@toolset.tool
def get_default_language():
    return 'en-US'


@toolset.tool
def get_user_name():
    return 'David'


class PersonalizedGreeting(BaseModel):
    greeting: str
    language_code: str


agent = Agent('openai:gpt-4o', toolsets=[toolset], output_type=PersonalizedGreeting)

result = agent.run_sync('Greet the user in a personalized way')
print(repr(result.output))
#> PersonalizedGreeting(greeting='Hello, David!', language_code='en-US')

Next, let's define a function that represents a hypothetical "run agent" API endpoint that can be called by the frontend and takes a list of messages to send to the model, a list of frontend tool definitions, and optional deferred tool results. This is where ExternalToolset, DeferredToolRequests, and DeferredToolResults come in:

deferred_toolset_api.py

from pydantic_ai import DeferredToolRequests, DeferredToolResults, ToolDefinition
from pydantic_ai.messages import ModelMessage
from pydantic_ai.toolsets import ExternalToolset

from deferred_toolset_agent import PersonalizedGreeting, agent


def run_agent(
    messages: list[ModelMessage] = [],
    frontend_tools: list[ToolDefinition] = {},
    deferred_tool_results: DeferredToolResults | None = None,
) -> tuple[PersonalizedGreeting | DeferredToolRequests, list[ModelMessage]]:
    deferred_toolset = ExternalToolset(frontend_tools)
    result = agent.run_sync(
        toolsets=[deferred_toolset], # (1)!
        output_type=[agent.output_type, DeferredToolRequests], # (2)!
        message_history=messages, # (3)!
        deferred_tool_results=deferred_tool_results,
    )
    return result.output, result.new_messages()

1. As mentioned in the Deferred Tools documentation, these toolsets are additional to those provided to the Agent constructor
2. As mentioned in the Deferred Tools documentation, this output_type overrides the one provided to the Agent constructor, so we have to make sure to not lose it
3. We don't include an user_prompt keyword argument as we expect the frontend to provide it via messages

Now, imagine that the code below is implemented on the frontend, and run_agent stands in for an API call to the backend that runs the agent. This is where we actually execute the deferred tool calls and start a new run with the new result included:

deferred_tools.py

from pydantic_ai import (
    DeferredToolRequests,
    DeferredToolResults,
    ModelRetry,
    ToolDefinition,
)
from pydantic_ai.messages import ModelMessage, ModelRequest, UserPromptPart

from deferred_toolset_api import run_agent

frontend_tool_definitions = [
    ToolDefinition(
        name='get_preferred_language',
        parameters_json_schema={'type': 'object', 'properties': {'default_language': {'type': 'string'}}},
        description="Get the user's preferred language from their browser",
    )
]

def get_preferred_language(default_language: str) -> str:
    return 'es-MX' # (1)!

frontend_tool_functions = {'get_preferred_language': get_preferred_language}

messages: list[ModelMessage] = [
    ModelRequest(
        parts=[
            UserPromptPart(content='Greet the user in a personalized way')
        ]
    )
]

deferred_tool_results: DeferredToolResults | None = None

final_output = None
while True:
    output, new_messages = run_agent(messages, frontend_tool_definitions, deferred_tool_results)
    messages += new_messages

    if not isinstance(output, DeferredToolRequests):
        final_output = output
        break

    print(output.calls)
    """
    [
        ToolCallPart(
            tool_name='get_preferred_language',
            args={'default_language': 'en-US'},
            tool_call_id='pyd_ai_tool_call_id',
        )
    ]
    """
    deferred_tool_results = DeferredToolResults()
    for tool_call in output.calls:
        if function := frontend_tool_functions.get(tool_call.tool_name):
            result = function(**tool_call.args_as_dict())
        else:
            result = ModelRetry(f'Unknown tool {tool_call.tool_name!r}')
        deferred_tool_results.calls[tool_call.tool_call_id] = result

print(repr(final_output))
"""
PersonalizedGreeting(greeting='Hola, David! Espero que tengas un gran día!', language_code='es-MX')
"""

1. Imagine that this returns the frontend navigator.language.

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

Dynamically Building a Toolset

Toolsets can be built dynamically ahead of each agent run or run step using a function that takes the agent run context and returns a toolset or None. This is useful when a toolset (like an MCP server) depends on information specific to an agent run, like its dependencies.

To register a dynamic toolset, you can pass a function that takes RunContext to the toolsets argument of the Agent constructor, or you can wrap a compliant function in the @agent.toolset decorator.

By default, the function will be called again ahead of each agent run step. If you are using the decorator, you can optionally provide a per_run_step=False argument to indicate that the toolset only needs to be built once for the entire run.

dynamic_toolset.py

from dataclasses import dataclass
from typing import Literal

from pydantic_ai import Agent, RunContext
from pydantic_ai.models.test import TestModel

from function_toolset import datetime_toolset, weather_toolset


@dataclass
class ToggleableDeps:
    active: Literal['weather', 'datetime']

    def toggle(self):
        if self.active == 'weather':
            self.active = 'datetime'
        else:
            self.active = 'weather'

test_model = TestModel()  # (1)!
agent = Agent(
    test_model,
    deps_type=ToggleableDeps  # (2)!
)

@agent.toolset
def toggleable_toolset(ctx: RunContext[ToggleableDeps]):
    if ctx.deps.active == 'weather':
        return weather_toolset
    else:
        return datetime_toolset

@agent.tool
def toggle(ctx: RunContext[ToggleableDeps]):
    ctx.deps.toggle()

deps = ToggleableDeps('weather')

result = agent.run_sync('Toggle the toolset', deps=deps)
print([t.name for t in test_model.last_model_request_parameters.function_tools])  # (3)!
#> ['toggle', 'now']

result = agent.run_sync('Toggle the toolset', deps=deps)
print([t.name for t in test_model.last_model_request_parameters.function_tools])
#> ['toggle', 'temperature_celsius', 'temperature_fahrenheit', 'conditions']

1. We're using TestModel here because it makes it easy to see which tools were available on each run.
2. We're using the agent's dependencies to give the toggle tool access to the active via the RunContext argument.
3. This shows the available tools after the toggle tool was executed, as the "last model request" was the one that returned the toggle tool result to the model.

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

Building a Custom Toolset

To define a fully custom toolset with its own logic to list available tools and handle them being called, you can subclass AbstractToolset and implement the get_tools() and call_tool() methods.

If you want to reuse a network connection or session across tool listings and calls during an agent run, you can implement __aenter__() and __aexit__().

Third-Party Toolsets

MCP Servers

See the MCP Client documentation for how to use MCP servers with Pydantic AI.

LangChain Tools

If you'd like to use tools or a toolkit from LangChain's community tool library with Pydantic AI, you can use the LangChainToolset which takes a list of LangChain tools. Note that Pydantic AI will not validate the arguments in this case -- it's up to the model to provide arguments matching the schema specified by the LangChain tool, and up to the LangChain tool to raise an error if the arguments are invalid.

You will need to install the langchain-community package and any others required by the tools in question.

from langchain_community.agent_toolkits import SlackToolkit

from pydantic_ai import Agent
from pydantic_ai.ext.langchain import LangChainToolset

toolkit = SlackToolkit()
toolset = LangChainToolset(toolkit.get_tools())

agent = Agent('openai:gpt-4o', toolsets=[toolset])
# ...

ACI.dev Tools

If you'd like to use tools from the ACI.dev tool library with Pydantic AI, you can use the ACIToolset toolset which takes a list of ACI tool names as well as the linked_account_owner_id. Note that Pydantic AI will not validate the arguments in this case -- it's up to the model to provide arguments matching the schema specified by the ACI tool, and up to the ACI tool to raise an error if the arguments are invalid.

You will need to install the aci-sdk package, set your ACI API key in the ACI_API_KEY environment variable, and pass your ACI "linked account owner ID" to the function.

import os

from pydantic_ai import Agent
from pydantic_ai.ext.aci import ACIToolset

toolset = ACIToolset(
    [
        'OPEN_WEATHER_MAP__CURRENT_WEATHER',
        'OPEN_WEATHER_MAP__FORECAST',
    ],
    linked_account_owner_id=os.getenv('LINKED_ACCOUNT_OWNER_ID'),
)

agent = Agent('openai:gpt-4o', toolsets=[toolset])