Support Pydantic validation context

docs/output.md

@@ -121,7 +121,7 @@ Instead of plain text or structured data, you may want the output of your agent
 
 Output functions are similar to [function tools](tools.md), but the model is forced to call one of them, the call ends the agent run, and the result is not passed back to the model.
 
-As with tool functions, output function arguments provided by the model are validated using Pydantic, they can optionally take [`RunContext`][pydantic_ai.tools.RunContext] as the first argument, and they can raise [`ModelRetry`][pydantic_ai.exceptions.ModelRetry] to ask the model to try again with modified arguments (or with a different output type).
+As with tool functions, output function arguments provided by the model are validated using Pydantic which can be influenced by a [validation context](#validation-context), they can optionally take [`RunContext`][pydantic_ai.tools.RunContext] as the first argument, and they can raise [`ModelRetry`][pydantic_ai.exceptions.ModelRetry] to ask the model to try again with modified arguments (or with a different output type).
 
 To specify output functions, you set the agent's `output_type` to either a single function (or bound instance method), or a list of functions. The list can also contain other output types like simple scalars or entire Pydantic models.
 You typically do not want to also register your output function as a tool (using the `@agent.tool` decorator or `tools` argument), as this could confuse the model about which it should be calling.
@@ -416,6 +416,62 @@ result = agent.run_sync('Create a person')
 #> {'name': 'John Doe', 'age': 30}
 ```
 
+### Validation context {#validation-context}
+
+Some validation relies on an extra Pydantic [context](https://docs.pydantic.dev/latest/concepts/validators/#validation-context) object. You can pass such an object to an `Agent` at definition-time via its [`validation_context`][pydantic_ai.Agent.__init__] parameter. It will be used in the validation of both structured outputs and [tool arguments](tools-advanced.md#tool-retries).
+
+This validation context can be either:
+
+- the context object itself (`Any`), used as-is to validate outputs, or
+- a function that takes the [`RunContext`][pydantic_ai.tools.RunContext] and returns a context object (`Any`). This function will be called automatically before each validation, allowing you to build a dynamic validation context.
+
+!!! warning "Don't confuse this _validation_ context with the _LLM_ context"
+    This Pydantic [context](https://docs.pydantic.dev/latest/concepts/validators/#validation-data) object is only used internally by Pydantic AI for output validation. In particular, it is **not** included in the prompts or messages sent to the language model.
+
+```python {title="validation_context.py"}
+from dataclasses import dataclass
+
+from pydantic import BaseModel, ValidationInfo, field_validator
+
+from pydantic_ai import Agent
+
+
+class Value(BaseModel):
+    x: int
+
+    @field_validator('x')
+    def increment_value(cls, value: int, info: ValidationInfo):
+        return value + (info.context or 0)
+
+
+agent = Agent(
+    'google-gla:gemini-2.5-flash',
+    output_type=Value,
+    validation_context=10,
+)
+result = agent.run_sync('Give me a value of 5.')
+print(repr(result.output))  # 5 from the model + 10 from the validation context
+#> Value(x=15)
+
+
+@dataclass
+class Deps:
+    increment: int
+
+
+agent = Agent(
+    'google-gla:gemini-2.5-flash',
+    output_type=Value,
+    deps_type=Deps,
+    validation_context=lambda ctx: ctx.deps.increment,
+)
+result = agent.run_sync('Give me a value of 5.', deps=Deps(increment=10))
+print(repr(result.output))  # 5 from the model + 10 from the validation context
+#> Value(x=15)
+```
+
+_(This example is complete, it can be run "as is")_
+
 ### Output validators {#output-validator-functions}
 
 Some validation is inconvenient or impossible to do in Pydantic validators, in particular when the validation requires IO and is asynchronous. Pydantic AI provides a way to add validation functions via the [`agent.output_validator`][pydantic_ai.Agent.output_validator] decorator.

docs/tools-advanced.md

@@ -353,7 +353,7 @@ If both per-tool `prepare` and agent-wide `prepare_tools` are used, the per-tool
 
 ## Tool Execution and Retries {#tool-retries}
 
-When a tool is executed, its arguments (provided by the LLM) are first validated against the function's signature using Pydantic. If validation fails (e.g., due to incorrect types or missing required arguments), a `ValidationError` is raised, and the framework automatically generates a [`RetryPromptPart`][pydantic_ai.messages.RetryPromptPart] containing the validation details. This prompt is sent back to the LLM, informing it of the error and allowing it to correct the parameters and retry the tool call.
+When a tool is executed, its arguments (provided by the LLM) are first validated against the function's signature using Pydantic. Note that the [validation context](output.md#validation-context) - if provided - will be used during this validation. If validation fails (e.g., due to incorrect types or missing required arguments), a `ValidationError` is raised, and the framework automatically generates a [`RetryPromptPart`][pydantic_ai.messages.RetryPromptPart] containing the validation details. This prompt is sent back to the LLM, informing it of the error and allowing it to correct the parameters and retry the tool call.
 
 Beyond automatic validation errors, the tool's own internal logic can also explicitly request a retry by raising the [`ModelRetry`][pydantic_ai.exceptions.ModelRetry] exception. This is useful for situations where the parameters were technically valid, but an issue occurred during execution (like a transient network error, or the tool determining the initial attempt needs modification).
 

pydantic_ai_slim/pydantic_ai/_agent_graph.py

@@ -144,6 +144,7 @@ class GraphAgentDeps(Generic[DepsT, OutputDataT]):
 
     output_schema: _output.OutputSchema[OutputDataT]
     output_validators: list[_output.OutputValidator[DepsT, OutputDataT]]
+    validation_context: Any | Callable[[RunContext[DepsT]], Any]
 
     history_processors: Sequence[HistoryProcessor[DepsT]]
 
@@ -736,7 +737,7 @@ async def _handle_text_response(
     ) -> ModelRequestNode[DepsT, NodeRunEndT] | End[result.FinalResult[NodeRunEndT]]:
         run_context = build_run_context(ctx)
 
-        result_data = await text_processor.process(text, run_context)
+        result_data = await text_processor.process(text, run_context=run_context)
 
         for validator in ctx.deps.output_validators:
             result_data = await validator.validate(result_data, run_context)
@@ -781,12 +782,13 @@ async def run(
 
 def build_run_context(ctx: GraphRunContext[GraphAgentState, GraphAgentDeps[DepsT, Any]]) -> RunContext[DepsT]:
     """Build a `RunContext` object from the current agent graph run context."""
-    return RunContext[DepsT](
+    run_context = RunContext[DepsT](
         deps=ctx.deps.user_deps,
         model=ctx.deps.model,
         usage=ctx.state.usage,
         prompt=ctx.deps.prompt,
         messages=ctx.state.message_history,
+        validation_context=None,
         tracer=ctx.deps.tracer,
         trace_include_content=ctx.deps.instrumentation_settings is not None
         and ctx.deps.instrumentation_settings.include_content,
@@ -796,6 +798,21 @@ def build_run_context(ctx: GraphRunContext[GraphAgentState, GraphAgentDeps[DepsT
         run_step=ctx.state.run_step,
         run_id=ctx.state.run_id,
     )
+    validation_context = build_validation_context(ctx.deps.validation_context, run_context)
+    run_context = replace(run_context, validation_context=validation_context)
+    return run_context
+
+
+def build_validation_context(
+    validation_ctx: Any | Callable[[RunContext[DepsT]], Any],
+    run_context: RunContext[DepsT],
+) -> Any:
+    """Build a Pydantic validation context, potentially from the current agent run context."""
+    if callable(validation_ctx):
+        fn = cast(Callable[[RunContext[DepsT]], Any], validation_ctx)
+        return fn(run_context)
+    else:
+        return validation_ctx
 
 
 async def process_tool_calls(  # noqa: C901

pydantic_ai_slim/pydantic_ai/_output.py

@@ -522,6 +522,7 @@ class BaseOutputProcessor(ABC, Generic[OutputDataT]):
     async def process(
         self,
         data: str,
+        *,
         run_context: RunContext[AgentDepsT],
         allow_partial: bool = False,
         wrap_validation_errors: bool = True,
@@ -609,6 +610,7 @@ def __init__(
     async def process(
         self,
         data: str | dict[str, Any] | None,
+        *,
         run_context: RunContext[AgentDepsT],
         allow_partial: bool = False,
         wrap_validation_errors: bool = True,
@@ -628,7 +630,7 @@ async def process(
             data = _utils.strip_markdown_fences(data)
 
         try:
-            output = self.validate(data, allow_partial)
+            output = self.validate(data, allow_partial=allow_partial, validation_context=run_context.validation_context)
         except ValidationError as e:
             if wrap_validation_errors:
                 m = _messages.RetryPromptPart(
@@ -645,13 +647,19 @@ async def process(
     def validate(
         self,
         data: str | dict[str, Any] | None,
+        *,
         allow_partial: bool = False,
+        validation_context: Any | None = None,
     ) -> dict[str, Any]:
         pyd_allow_partial: Literal['off', 'trailing-strings'] = 'trailing-strings' if allow_partial else 'off'
         if isinstance(data, str):
-            return self.validator.validate_json(data or '{}', allow_partial=pyd_allow_partial)
+            return self.validator.validate_json(
+                data or '{}', allow_partial=pyd_allow_partial, context=validation_context
+            )
         else:
-            return self.validator.validate_python(data or {}, allow_partial=pyd_allow_partial)
+            return self.validator.validate_python(
+                data or {}, allow_partial=pyd_allow_partial, context=validation_context
+            )
 
     async def call(
         self,
@@ -770,12 +778,16 @@ def __init__(
     async def process(
         self,
         data: str,
+        *,
         run_context: RunContext[AgentDepsT],
         allow_partial: bool = False,
         wrap_validation_errors: bool = True,
     ) -> OutputDataT:
         union_object = await self._union_processor.process(
-            data, run_context, allow_partial=allow_partial, wrap_validation_errors=wrap_validation_errors
+            data,
+            run_context=run_context,
+            allow_partial=allow_partial,
+            wrap_validation_errors=wrap_validation_errors,
         )
 
         result = union_object.result
@@ -791,15 +803,20 @@ async def process(
                 raise
 
         return await processor.process(
-            inner_data, run_context, allow_partial=allow_partial, wrap_validation_errors=wrap_validation_errors
+            inner_data,
+            run_context=run_context,
+            allow_partial=allow_partial,
+            wrap_validation_errors=wrap_validation_errors,
         )
 
 
 class TextOutputProcessor(BaseOutputProcessor[OutputDataT]):
     async def process(
         self,
         data: str,
+        *,
         run_context: RunContext[AgentDepsT],
+        validation_context: Any | None = None,
         allow_partial: bool = False,
         wrap_validation_errors: bool = True,
     ) -> OutputDataT:
@@ -830,14 +847,22 @@ def __init__(
     async def process(
         self,
         data: str,
+        *,
         run_context: RunContext[AgentDepsT],
+        validation_context: Any | None = None,
         allow_partial: bool = False,
         wrap_validation_errors: bool = True,
     ) -> OutputDataT:
         args = {self._str_argument_name: data}
         data = await execute_traced_output_function(self._function_schema, run_context, args, wrap_validation_errors)
 
-        return await super().process(data, run_context, allow_partial, wrap_validation_errors)
+        return await super().process(
+            data,
+            run_context=run_context,
+            validation_context=validation_context,
+            allow_partial=allow_partial,
+            wrap_validation_errors=wrap_validation_errors,
+        )
 
 
 @dataclass(init=False)

pydantic_ai_slim/pydantic_ai/_run_context.py

@@ -3,7 +3,7 @@
 import dataclasses
 from collections.abc import Sequence
 from dataclasses import field
-from typing import TYPE_CHECKING, Generic
+from typing import TYPE_CHECKING, Any, Generic
 
 from opentelemetry.trace import NoOpTracer, Tracer
 from typing_extensions import TypeVar
@@ -38,6 +38,8 @@ class RunContext(Generic[RunContextAgentDepsT]):
     """The original user prompt passed to the run."""
     messages: list[_messages.ModelMessage] = field(default_factory=list)
     """Messages exchanged in the conversation so far."""
+    validation_context: Any = None
+    """Pydantic [validation context](https://docs.pydantic.dev/latest/concepts/validators/#validation-context) for tool args and run outputs."""
     tracer: Tracer = field(default_factory=NoOpTracer)
     """The tracer to use for tracing the run."""
     trace_include_content: bool = False

pydantic_ai_slim/pydantic_ai/_tool_manager.py

@@ -164,9 +164,13 @@ async def _call_tool(
             pyd_allow_partial = 'trailing-strings' if allow_partial else 'off'
             validator = tool.args_validator
             if isinstance(call.args, str):
-                args_dict = validator.validate_json(call.args or '{}', allow_partial=pyd_allow_partial)
+                args_dict = validator.validate_json(
+                    call.args or '{}', allow_partial=pyd_allow_partial, context=ctx.validation_context
+                )
             else:
-                args_dict = validator.validate_python(call.args or {}, allow_partial=pyd_allow_partial)
+                args_dict = validator.validate_python(
+                    call.args or {}, allow_partial=pyd_allow_partial, context=ctx.validation_context
+                )
 
             result = await self.toolset.call_tool(name, args_dict, ctx, tool)
 

pydantic_ai_slim/pydantic_ai/agent/__init__.py

@@ -147,6 +147,7 @@ class Agent(AbstractAgent[AgentDepsT, OutputDataT]):
     _prepare_output_tools: ToolsPrepareFunc[AgentDepsT] | None = dataclasses.field(repr=False)
     _max_result_retries: int = dataclasses.field(repr=False)
     _max_tool_retries: int = dataclasses.field(repr=False)
+    _validation_context: Any | Callable[[RunContext[AgentDepsT]], Any] = dataclasses.field(repr=False)
 
     _event_stream_handler: EventStreamHandler[AgentDepsT] | None = dataclasses.field(repr=False)
 
@@ -166,6 +167,7 @@ def __init__(
         name: str | None = None,
         model_settings: ModelSettings | None = None,
         retries: int = 1,
+        validation_context: Any | Callable[[RunContext[AgentDepsT]], Any] = None,
         output_retries: int | None = None,
         tools: Sequence[Tool[AgentDepsT] | ToolFuncEither[AgentDepsT, ...]] = (),
         builtin_tools: Sequence[AbstractBuiltinTool] = (),
@@ -192,6 +194,7 @@ def __init__(
         name: str | None = None,
         model_settings: ModelSettings | None = None,
         retries: int = 1,
+        validation_context: Any | Callable[[RunContext[AgentDepsT]], Any] = None,
         output_retries: int | None = None,
         tools: Sequence[Tool[AgentDepsT] | ToolFuncEither[AgentDepsT, ...]] = (),
         builtin_tools: Sequence[AbstractBuiltinTool] = (),
@@ -216,6 +219,7 @@ def __init__(
         name: str | None = None,
         model_settings: ModelSettings | None = None,
         retries: int = 1,
+        validation_context: Any | Callable[[RunContext[AgentDepsT]], Any] = None,
         output_retries: int | None = None,
         tools: Sequence[Tool[AgentDepsT] | ToolFuncEither[AgentDepsT, ...]] = (),
         builtin_tools: Sequence[AbstractBuiltinTool] = (),
@@ -249,6 +253,7 @@ def __init__(
             model_settings: Optional model request settings to use for this agent's runs, by default.
             retries: The default number of retries to allow for tool calls and output validation, before raising an error.
                 For model request retries, see the [HTTP Request Retries](../retries.md) documentation.
+            validation_context: Pydantic [validation context](https://docs.pydantic.dev/latest/concepts/validators/#validation-context) used to validate tool arguments and outputs.
             output_retries: The maximum number of retries to allow for output validation, defaults to `retries`.
             tools: Tools to register with the agent, you can also register tools via the decorators
                 [`@agent.tool`][pydantic_ai.Agent.tool] and [`@agent.tool_plain`][pydantic_ai.Agent.tool_plain].
@@ -314,6 +319,8 @@ def __init__(
         self._max_result_retries = output_retries if output_retries is not None else retries
         self._max_tool_retries = retries
 
+        self._validation_context = validation_context
+
         self._builtin_tools = builtin_tools
 
         self._prepare_tools = prepare_tools
@@ -612,6 +619,7 @@ async def get_instructions(run_context: RunContext[AgentDepsT]) -> str | None:
             end_strategy=self.end_strategy,
             output_schema=output_schema,
             output_validators=output_validators,
+            validation_context=self._validation_context,
             history_processors=self.history_processors,
             builtin_tools=[*self._builtin_tools, *(builtin_tools or [])],
             tool_manager=tool_manager,

pydantic_ai_slim/pydantic_ai/result.py

@@ -198,7 +198,10 @@ async def validate_response_output(
                     text = ''
 
             result_data = await text_processor.process(
-                text, self._run_ctx, allow_partial=allow_partial, wrap_validation_errors=False
+                text,
+                run_context=self._run_ctx,
+                allow_partial=allow_partial,
+                wrap_validation_errors=False,
             )
             for validator in self._output_validators:
                 result_data = await validator.validate(

tests/test_examples.py

@@ -506,6 +506,7 @@ async def call_tool(
     'What is a banana?': ToolCallPart(tool_name='return_fruit', args={'name': 'banana', 'color': 'yellow'}),
     'What is a Ford Explorer?': '{"result": {"kind": "Vehicle", "data": {"name": "Ford Explorer", "wheels": 4}}}',
     'What is a MacBook?': '{"result": {"kind": "Device", "data": {"name": "MacBook", "kind": "laptop"}}}',
+    'Give me a value of 5.': ToolCallPart(tool_name='final_result', args={'x': 5}),
     'Write a creative story about space exploration': 'In the year 2157, Captain Maya Chen piloted her spacecraft through the vast expanse of the Andromeda Galaxy. As she discovered a planet with crystalline mountains that sang in harmony with the cosmic winds, she realized that space exploration was not just about finding new worlds, but about finding new ways to understand the universe and our place within it.',
     'Create a person': ToolCallPart(
         tool_name='final_result',