Dynamic tools

Makefile

@@ -29,7 +29,8 @@ lint:
 
 .PHONY: typecheck-pyright
 typecheck-pyright:
-	uv run pyright
+	@# PYRIGHT_PYTHON_IGNORE_WARNINGS avoids the overhead of making a request to github on every invocation
+	PYRIGHT_PYTHON_IGNORE_WARNINGS=1 uv run pyright
 
 .PHONY: typecheck-mypy
 typecheck-mypy:

docs/agents.md

@@ -420,7 +420,7 @@ print(dice_result.data)
 ```
 
 1. The simplest way to register tools via the `Agent` constructor is to pass a list of functions, the function signature is inspected to determine if the tool takes [`RunContext`][pydantic_ai.tools.RunContext].
-2. `agent_a` and `agent_b` are identical — but we can use [`Tool`][pydantic_ai.tools.Tool] to give more fine-grained control over how tools are defined, e.g. setting their name or description.
+2. `agent_a` and `agent_b` are identical — but we can use [`Tool`][pydantic_ai.tools.Tool] to reuse tool definitions and give more fine-grained control over how tools are defined, e.g. setting their name or description, or using a custom [`prepare`](#tool-prepare) method.
 
 _(This example is complete, it can be run "as is")_
 
@@ -459,10 +459,10 @@ def foobar(a: int, b: str, c: dict[str, list[float]]) -> str:
 
 
 def print_schema(messages: list[Message], info: AgentInfo) -> ModelAnyResponse:
-    tool = info.function_tools['foobar']
+    tool = info.function_tools[0]
     print(tool.description)
     #> Get me foobar.
-    print(tool.json_schema)
+    print(tool.parameters_json_schema)
     """
     {
         'description': 'Get me foobar.',
@@ -491,7 +491,167 @@ _(This example is complete, it can be run "as is")_
 
 The return type of tool can be anything which Pydantic can serialize to JSON as some models (e.g. Gemini) support semi-structured return values, some expect text (OpenAI) but seem to be just as good at extracting meaning from the data. If a Python object is returned and the model expects a string, the value will be serialized to JSON.
 
-If a tool has a single parameter that can be represented as an object in JSON schema (e.g. dataclass, TypedDict, pydantic model), the schema for the tool is simplified to be just that object. (TODO example)
+If a tool has a single parameter that can be represented as an object in JSON schema (e.g. dataclass, TypedDict, pydantic model), the schema for the tool is simplified to be just that object.
+
+Here's an example, we use [`TestModel.agent_model_function_tools`][pydantic_ai.models.test.TestModel.agent_model_function_tools] to inspect the tool schema that would be passed to the model.
+
+```py title="single_parameter_tool.py"
+from pydantic import BaseModel
+
+from pydantic_ai import Agent
+from pydantic_ai.models.test import TestModel
+
+agent = Agent()
+
+
+class Foobar(BaseModel):
+    """This is a Foobar"""
+
+    x: int
+    y: str
+    z: float = 3.14
+
+
+@agent.tool_plain
+def foobar(f: Foobar) -> str:
+    return str(f)
+
+
+test_model = TestModel()
+result = agent.run_sync('hello', model=test_model)
+print(result.data)
+#> {"foobar":"x=0 y='a' z=3.14"}
+print(test_model.agent_model_function_tools)
+"""
+[
+    ToolDefinition(
+        name='foobar',
+        description='',
+        parameters_json_schema={
+            'description': 'This is a Foobar',
+            'properties': {
+                'x': {'title': 'X', 'type': 'integer'},
+                'y': {'title': 'Y', 'type': 'string'},
+                'z': {'default': 3.14, 'title': 'Z', 'type': 'number'},
+            },
+            'required': ['x', 'y'],
+            'title': 'Foobar',
+            'type': 'object',
+        },
+        outer_typed_dict_key=None,
+    )
+]
+"""
+```
+
+_(This example is complete, it can be run "as is")_
+
+### Dynamic Function tools {#tool-prepare}
+
+Tools can optionally be defined with another function: `prepare`, which is called at each step of a run to
+customize the definition of the tool passed to the model, or omit the tool completely from that step.
+
+A `prepare` method can be registered via the `prepare` kwarg to any of the tool registration mechanisms:
+
+* [`@agent.tool`][pydantic_ai.Agent.tool] decorator
+* [`@agent.tool_plain`][pydantic_ai.Agent.tool_plain] decorator
+* [`Tool`][pydantic_ai.tools.Tool] dataclass
+
+The `prepare` method, should be of type [`ToolPrepareFunc`][pydantic_ai.tools.ToolPrepareFunc], a function which takes [`RunContext`][pydantic_ai.tools.RunContext] and a pre-built [`ToolDefinition`][pydantic_ai.tools.ToolDefinition], and should either return that `ToolDefinition` with or without modifying it, return a new `ToolDefinition`, or return `None` to indicate this tools should not be registered for that step.
+
+Here's a simple `prepare` method that only includes the tool if the value of the dependency is `42`.
+
+As with the previous example, we use [`TestModel`][pydantic_ai.models.test.TestModel] to demonstrate the behavior without calling a real model.
+
+```py title="tool_only_if_42.py"
+from typing import Union
+
+from pydantic_ai import Agent, RunContext
+from pydantic_ai.tools import ToolDefinition
+
+agent = Agent('test')
+
+
+async def only_if_42(
+    ctx: RunContext[int], tool_def: ToolDefinition
+) -> Union[ToolDefinition, None]:
+    if ctx.deps == 42:
+        return tool_def
+
+
+@agent.tool(prepare=only_if_42)
+def hitchhiker(ctx: RunContext[int], answer: str) -> str:
+    return f'{ctx.deps} {answer}'
+
+
+result = agent.run_sync('testing...', deps=41)
+print(result.data)
+#> success (no tool calls)
+result = agent.run_sync('testing...', deps=42)
+print(result.data)
+#> {"hitchhiker":"42 a"}
+```
+
+_(This example is complete, it can be run "as is")_
+
+Here's a more complex example where we change the description of the `name` parameter to based on the value of `deps`
+
+For the sake of variation, we create this tool using the [`Tool`][pydantic_ai.tools.Tool] dataclass.
+
+```py title="customize_name.py"
+from __future__ import annotations
+
+from typing import Literal
+
+from pydantic_ai import Agent, RunContext
+from pydantic_ai.models.test import TestModel
+from pydantic_ai.tools import Tool, ToolDefinition
+
+
+def greet(name: str) -> str:
+    return f'hello {name}'
+
+
+async def prepare_greet(
+    ctx: RunContext[Literal['human', 'machine']], tool_def: ToolDefinition
+) -> ToolDefinition | None:
+    d = f'Name of the {ctx.deps} to greet.'
+    tool_def.parameters_json_schema['properties']['name']['description'] = d
+    return tool_def
+
+
+greet_tool = Tool(greet, prepare=prepare_greet)
+test_model = TestModel()
+agent = Agent(test_model, tools=[greet_tool], deps_type=Literal['human', 'machine'])
+
+result = agent.run_sync('testing...', deps='human')
+print(result.data)
+#> {"greet":"hello a"}
+print(test_model.agent_model_function_tools)
+"""
+[
+    ToolDefinition(
+        name='greet',
+        description='',
+        parameters_json_schema={
+            'properties': {
+                'name': {
+                    'title': 'Name',
+                    'type': 'string',
+                    'description': 'Name of the human to greet.',
+                }
+            },
+            'required': ['name'],
+            'type': 'object',
+            'additionalProperties': False,
+        },
+        outer_typed_dict_key=None,
+    )
+]
+"""
+```
+
+_(This example is complete, it can be run "as is")_
 
 ## Reflection and self-correction
 

docs/testing-evals.md

@@ -209,8 +209,6 @@ def call_weather_forecast(  # (1)!
 ) -> ModelAnyResponse:
     if len(messages) == 2:
         # first call, call the weather forecast tool
-        assert set(info.function_tools.keys()) == {'weather_forecast'}
-
         user_prompt = messages[1]
         m = re.search(r'\d{4}-\d{2}-\d{2}', user_prompt.content)
         assert m is not None

pydantic_ai_slim/pydantic_ai/_pydantic.py

@@ -17,10 +17,10 @@
 from pydantic_core import SchemaValidator, core_schema
 
 from ._griffe import doc_descriptions
-from ._utils import ObjectJsonSchema, check_object_json_schema, is_model_like
+from ._utils import check_object_json_schema, is_model_like
 
 if TYPE_CHECKING:
-    pass
+    from .tools import ObjectJsonSchema
 
 
 __all__ = 'function_schema', 'LazyTypeAdapter'
@@ -168,11 +168,13 @@ def takes_ctx(function: Callable[..., Any]) -> bool:
     """
     sig = signature(function)
     try:
-        _, first_param = next(iter(sig.parameters.items()))
+        first_param_name = next(iter(sig.parameters.keys()))
     except StopIteration:
         return False
     else:
-        return first_param.annotation is not sig.empty and _is_call_ctx(first_param.annotation)
+        type_hints = _typing_extra.get_function_type_hints(function)
+        annotation = type_hints[first_param_name]
+        return annotation is not sig.empty and _is_call_ctx(annotation)
 
 
 def _build_schema(

pydantic_ai_slim/pydantic_ai/_result.py

@@ -14,7 +14,7 @@
 from .exceptions import ModelRetry
 from .messages import ModelStructuredResponse, ToolCall
 from .result import ResultData
-from .tools import AgentDeps, ResultValidatorFunc, RunContext
+from .tools import AgentDeps, ResultValidatorFunc, RunContext, ToolDefinition
 
 
 @dataclass
@@ -94,10 +94,7 @@ def build(cls, response_type: type[ResultData], name: str, description: str | No
             allow_text_result = False
 
         def _build_tool(a: Any, tool_name_: str, multiple: bool) -> ResultTool[ResultData]:
-            return cast(
-                ResultTool[ResultData],
-                ResultTool.build(a, tool_name_, description, multiple),  # pyright: ignore[reportUnknownMemberType]
-            )
+            return cast(ResultTool[ResultData], ResultTool(a, tool_name_, description, multiple))
 
         tools: dict[str, ResultTool[ResultData]] = {}
         if args := get_union_args(response_type):
@@ -121,38 +118,38 @@ def tool_names(self) -> list[str]:
         """Return the names of the tools."""
         return list(self.tools.keys())
 
+    def tool_defs(self) -> list[ToolDefinition]:
+        """Get tool definitions to register with the model."""
+        return [t.tool_def for t in self.tools.values()]
+
 
 DEFAULT_DESCRIPTION = 'The final response which ends this conversation'
 
 
-@dataclass
+@dataclass(init=False)
 class ResultTool(Generic[ResultData]):
-    name: str
-    description: str
+    tool_def: ToolDefinition
     type_adapter: TypeAdapter[Any]
-    json_schema: _utils.ObjectJsonSchema
-    outer_typed_dict_key: str | None
 
-    @classmethod
-    def build(cls, response_type: type[ResultData], name: str, description: str | None, multiple: bool) -> Self | None:
+    def __init__(self, response_type: type[ResultData], name: str, description: str | None, multiple: bool):
         """Build a ResultTool dataclass from a response type."""
         assert response_type is not str, 'ResultTool does not support str as a response type'
 
         if _utils.is_model_like(response_type):
-            type_adapter = TypeAdapter(response_type)
+            self.type_adapter = TypeAdapter(response_type)
             outer_typed_dict_key: str | None = None
             # noinspection PyArgumentList
-            json_schema = _utils.check_object_json_schema(type_adapter.json_schema())
+            parameters_json_schema = _utils.check_object_json_schema(self.type_adapter.json_schema())
         else:
             response_data_typed_dict = TypedDict('response_data_typed_dict', {'response': response_type})  # noqa
-            type_adapter = TypeAdapter(response_data_typed_dict)
+            self.type_adapter = TypeAdapter(response_data_typed_dict)
             outer_typed_dict_key = 'response'
             # noinspection PyArgumentList
-            json_schema = _utils.check_object_json_schema(type_adapter.json_schema())
+            parameters_json_schema = _utils.check_object_json_schema(self.type_adapter.json_schema())
             # including `response_data_typed_dict` as a title here doesn't add anything and could confuse the LLM
-            json_schema.pop('title')
+            parameters_json_schema.pop('title')
 
-        if json_schema_description := json_schema.pop('description', None):
+        if json_schema_description := parameters_json_schema.pop('description', None):
             if description is None:
                 tool_description = json_schema_description
             else:
@@ -162,11 +159,10 @@ def build(cls, response_type: type[ResultData], name: str, description: str | No
             if multiple:
                 tool_description = f'{union_arg_name(response_type)}: {tool_description}'
 
-        return cls(
+        self.tool_def = ToolDefinition(
             name=name,
             description=tool_description,
-            type_adapter=type_adapter,
-            json_schema=json_schema,
+            parameters_json_schema=parameters_json_schema,
             outer_typed_dict_key=outer_typed_dict_key,
         )
 
@@ -204,7 +200,7 @@ def validate(
             else:
                 raise
         else:
-            if k := self.outer_typed_dict_key:
+            if k := self.tool_def.outer_typed_dict_key:
                 result = result[k]
             return result
 

pydantic_ai_slim/pydantic_ai/_system_prompt.py

@@ -21,7 +21,7 @@ def __post_init__(self):
 
     async def run(self, deps: AgentDeps) -> str:
         if self._takes_ctx:
-            args = (RunContext(deps, 0, None),)
+            args = (RunContext(deps, 0),)
         else:
             args = ()
 

pydantic_ai_slim/pydantic_ai/_utils.py

@@ -8,12 +8,15 @@
 from datetime import datetime, timezone
 from functools import partial
 from types import GenericAlias
-from typing import Any, Callable, Generic, TypeVar, Union, cast, overload
+from typing import TYPE_CHECKING, Any, Callable, Generic, TypeVar, Union, cast, overload
 
 from pydantic import BaseModel
 from pydantic.json_schema import JsonSchemaValue
 from typing_extensions import ParamSpec, TypeAlias, TypeGuard, is_typeddict
 
+if TYPE_CHECKING:
+    from .tools import ObjectJsonSchema
+
 _P = ParamSpec('_P')
 _R = TypeVar('_R')
 
@@ -39,10 +42,6 @@ def is_model_like(type_: Any) -> bool:
     )
 
 
-# With PEP-728 this should be a TypedDict with `type: Literal['object']`, and `extra_items=Any`
-ObjectJsonSchema: TypeAlias = dict[str, Any]
-
-
 def check_object_json_schema(schema: JsonSchemaValue) -> ObjectJsonSchema:
     from .exceptions import UserError
 
@@ -127,6 +126,12 @@ def is_left(self) -> bool:
     def whichever(self) -> Left | Right:
         return self._left.value if self._left is not None else self.right
 
+    def __repr__(self):
+        if left := self._left:
+            return f'Either(left={left.value!r})'
+        else:
+            return f'Either(right={self.right!r})'
+
 
 @asynccontextmanager
 async def group_by_temporal(
@@ -218,7 +223,7 @@ async def async_iter_groups() -> AsyncIterator[list[T]]:
 
     try:
         yield async_iter_groups()
-    finally:
+    finally:  # pragma: no cover
         # after iteration if a tasks still exists, cancel it, this will only happen if an error occurred
         if task:
             task.cancel('Cancelling due to error in iterator')