Don't require anthropic dependency when using Anthropic model with other provider

pydantic_ai_slim/pydantic_ai/profiles/anthropic.py

@@ -1,8 +1,5 @@
 from __future__ import annotations as _annotations
 
-from dataclasses import dataclass
-
-from .._json_schema import JsonSchema, JsonSchemaTransformer
 from . import ModelProfile
 
 
@@ -22,54 +19,4 @@ def anthropic_model_profile(model_name: str) -> ModelProfile | None:
     return ModelProfile(
         thinking_tags=('<thinking>', '</thinking>'),
         supports_json_schema_output=supports_json_schema_output,
-        json_schema_transformer=AnthropicJsonSchemaTransformer,
     )
-
-
-@dataclass(init=False)
-class AnthropicJsonSchemaTransformer(JsonSchemaTransformer):
-    """Transforms schemas to the subset supported by Anthropic structured outputs.
-
-    Transformation is applied when:
-    - `NativeOutput` is used as the `output_type` of the Agent
-    - `strict=True` is set on the `Tool`
-
-    The behavior of this transformer differs from the OpenAI one in that it sets `Tool.strict=False` by default when not explicitly set to True.
-
-    Example:
-        ```python
-        from pydantic_ai import Agent
-
-        agent = Agent('anthropic:claude-sonnet-4-5')
-
-        @agent.tool_plain  # -> defaults to strict=False
-        def my_tool(x: str) -> dict[str, int]:
-            ...
-        ```
-
-    Anthropic's SDK `transform_schema()` automatically:
-    - Adds `additionalProperties: false` to all objects (required by API)
-    - Removes unsupported constraints (minLength, pattern, etc.)
-    - Moves removed constraints to description field
-    - Removes title and $schema fields
-    """
-
-    def walk(self) -> JsonSchema:
-        from anthropic import transform_schema
-
-        schema = super().walk()
-
-        # The caller (pydantic_ai.models._customize_tool_def or _customize_output_object) coalesces
-        # - output_object.strict = self.is_strict_compatible
-        # - tool_def.strict = self.is_strict_compatible
-        # the reason we don't default to `strict=True` is that the transformation could be lossy
-        # so in order to change the behavior (default to True), we need to come up with logic that will check for lossiness
-        # https://github.com/pydantic/pydantic-ai/issues/3541
-        self.is_strict_compatible = self.strict is True  # not compatible when strict is False/None
-
-        return transform_schema(schema) if self.strict is True else schema
-
-    def transform(self, schema: JsonSchema) -> JsonSchema:
-        schema.pop('title', None)
-        schema.pop('$schema', None)
-        return schema

pydantic_ai_slim/pydantic_ai/providers/anthropic.py

@@ -1,6 +1,7 @@
 from __future__ import annotations as _annotations
 
 import os
+from dataclasses import dataclass
 from typing import TypeAlias, overload
 
 import httpx
@@ -11,6 +12,8 @@
 from pydantic_ai.profiles.anthropic import anthropic_model_profile
 from pydantic_ai.providers import Provider
 
+from .._json_schema import JsonSchema, JsonSchemaTransformer
+
 try:
     from anthropic import AsyncAnthropic, AsyncAnthropicBedrock, AsyncAnthropicVertex
 except ImportError as _import_error:
@@ -39,7 +42,8 @@ def client(self) -> AsyncAnthropicClient:
         return self._client
 
     def model_profile(self, model_name: str) -> ModelProfile | None:
-        return anthropic_model_profile(model_name)
+        profile = anthropic_model_profile(model_name)
+        return ModelProfile(json_schema_transformer=AnthropicJsonSchemaTransformer).update(profile)
 
     @overload
     def __init__(self, *, anthropic_client: AsyncAnthropicClient | None = None) -> None: ...
@@ -83,3 +87,55 @@ def __init__(
             else:
                 http_client = cached_async_http_client(provider='anthropic')
                 self._client = AsyncAnthropic(api_key=api_key, base_url=base_url, http_client=http_client)
+
+
+@dataclass(init=False)
+class AnthropicJsonSchemaTransformer(JsonSchemaTransformer):
+    """Transforms schemas to the subset supported by Anthropic structured outputs.
+
+    Transformation is applied when:
+    - `NativeOutput` is used as the `output_type` of the Agent
+    - `strict=True` is set on the `Tool`
+
+    The behavior of this transformer differs from the OpenAI one in that it sets `Tool.strict=False` by default when not explicitly set to True.
+
+    Example:
+        ```python
+        from pydantic_ai import Agent
+
+        agent = Agent('anthropic:claude-sonnet-4-5')
+
+        @agent.tool_plain  # -> defaults to strict=False
+        def my_tool(x: str) -> dict[str, int]:
+            ...
+        ```
+
+    Anthropic's SDK `transform_schema()` automatically:
+    - Adds `additionalProperties: false` to all objects (required by API)
+    - Removes unsupported constraints (minLength, pattern, etc.)
+    - Moves removed constraints to description field
+    - Removes title and $schema fields
+    """
+
+    def walk(self) -> JsonSchema:
+        schema = super().walk()
+
+        # The caller (pydantic_ai.models._customize_tool_def or _customize_output_object) coalesces
+        # - output_object.strict = self.is_strict_compatible
+        # - tool_def.strict = self.is_strict_compatible
+        # the reason we don't default to `strict=True` is that the transformation could be lossy
+        # so in order to change the behavior (default to True), we need to come up with logic that will check for lossiness
+        # https://github.com/pydantic/pydantic-ai/issues/3541
+        self.is_strict_compatible = self.strict is True  # not compatible when strict is False/None
+
+        if self.strict is True:
+            from anthropic import transform_schema
+
+            return transform_schema(schema)
+        else:
+            return schema
+
+    def transform(self, schema: JsonSchema) -> JsonSchema:
+        schema.pop('title', None)
+        schema.pop('$schema', None)
+        return schema

tests/profiles/test_anthropic.py

@@ -22,10 +22,12 @@
 from inline_snapshot import snapshot
 from pydantic import BaseModel, Field
 
+from pydantic_ai.providers.anthropic import AnthropicJsonSchemaTransformer
+
 from ..conftest import try_import
 
 with try_import() as imports_successful:
-    from pydantic_ai.profiles.anthropic import AnthropicJsonSchemaTransformer, anthropic_model_profile
+    from pydantic_ai.profiles.anthropic import anthropic_model_profile
 
 pytestmark = [
     pytest.mark.skipif(not imports_successful(), reason='anthropic not installed'),

tests/providers/test_bedrock.py

@@ -60,11 +60,16 @@ def test_bedrock_provider_model_profile(env: TestEnv, mocker: MockerFixture):
     anthropic_model_profile_mock.assert_called_with('claude-3-5-sonnet-20240620')
     assert isinstance(anthropic_profile, BedrockModelProfile)
     assert anthropic_profile.bedrock_supports_tool_choice is True
+    # Bedrock does not support native structured output, even for models that support it via direct Anthropic API
+    assert anthropic_profile.supports_json_schema_output is False
+    assert anthropic_profile.json_schema_transformer is None
 
     anthropic_profile = provider.model_profile('anthropic.claude-instant-v1')
     anthropic_model_profile_mock.assert_called_with('claude-instant')
     assert isinstance(anthropic_profile, BedrockModelProfile)
     assert anthropic_profile.bedrock_supports_tool_choice is True
+    assert anthropic_profile.supports_json_schema_output is False
+    assert anthropic_profile.json_schema_transformer is None
 
     mistral_profile = provider.model_profile('mistral.mistral-large-2407-v1:0')
     mistral_model_profile_mock.assert_called_with('mistral-large-2407')

tests/providers/test_openrouter.py

@@ -9,7 +9,7 @@
 from pydantic_ai.agent import Agent
 from pydantic_ai.exceptions import UserError
 from pydantic_ai.profiles.amazon import amazon_model_profile
-from pydantic_ai.profiles.anthropic import AnthropicJsonSchemaTransformer, anthropic_model_profile
+from pydantic_ai.profiles.anthropic import anthropic_model_profile
 from pydantic_ai.profiles.cohere import cohere_model_profile
 from pydantic_ai.profiles.deepseek import deepseek_model_profile
 from pydantic_ai.profiles.google import google_model_profile
@@ -127,7 +127,7 @@ def test_openrouter_provider_model_profile(mocker: MockerFixture):
     anthropic_profile = provider.model_profile('anthropic/claude-3.5-sonnet')
     anthropic_model_profile_mock.assert_called_with('claude-3.5-sonnet')
     assert anthropic_profile is not None
-    assert anthropic_profile.json_schema_transformer == AnthropicJsonSchemaTransformer
+    assert anthropic_profile.json_schema_transformer == OpenAIJsonSchemaTransformer
 
     mistral_profile = provider.model_profile('mistralai/mistral-large-2407')
     mistral_model_profile_mock.assert_called_with('mistral-large-2407')

tests/providers/test_vercel.py

@@ -7,7 +7,7 @@
 from pydantic_ai._json_schema import InlineDefsJsonSchemaTransformer
 from pydantic_ai.exceptions import UserError
 from pydantic_ai.profiles.amazon import amazon_model_profile
-from pydantic_ai.profiles.anthropic import AnthropicJsonSchemaTransformer, anthropic_model_profile
+from pydantic_ai.profiles.anthropic import anthropic_model_profile
 from pydantic_ai.profiles.cohere import cohere_model_profile
 from pydantic_ai.profiles.deepseek import deepseek_model_profile
 from pydantic_ai.profiles.google import GoogleJsonSchemaTransformer, google_model_profile
@@ -82,7 +82,7 @@ def test_vercel_provider_model_profile(mocker: MockerFixture):
     profile = provider.model_profile('anthropic/claude-sonnet-4-5')
     anthropic_mock.assert_called_with('claude-sonnet-4-5')
     assert profile is not None
-    assert profile.json_schema_transformer == AnthropicJsonSchemaTransformer
+    assert profile.json_schema_transformer == OpenAIJsonSchemaTransformer
 
     # Test bedrock provider
     profile = provider.model_profile('bedrock/anthropic.claude-sonnet-4-5')