chore(pydantic-ai): Drop support for < 1.76

MIGRATION_GUIDE.md

@@ -113,6 +113,7 @@ Looking to upgrade from Sentry SDK 2.x to 3.x? Here's a comprehensive list of wh
 - Dropped support for Litestar below 2.0.
 - Dropped support for PySpark below 3.0.
 - Dropped support for redis-py below 4.2.
+- Dropped support for Pydantic AI below 1.76.
 - Removed the RedisIntegration `max_data_size` option.
 - Removed the possibility to supply a specific client to the LaunchDarklyIntegration.
 - The `enable_tracing` option was removed. Use `traces_sample_rate=1.0` instead.

sentry_sdk/integrations/__init__.py

@@ -157,7 +157,7 @@ def iter_default_integrations(
     "openai": (1, 0, 0),
     "openai_agents": (0, 0, 19),
     "openfeature": (0, 7, 1),
-    "pydantic_ai": (1, 0, 0),
+    "pydantic_ai": (1, 76, 0),
     "pymongo": (3, 5, 0),
     "pyramid": (2, 0),
     "pyreqwest": (0, 11, 6),

sentry_sdk/integrations/pydantic_ai/__init__.py

@@ -15,7 +15,6 @@
 
 from .patches import (
     _patch_agent_run,
-    _patch_graph_nodes,
     _patch_tool_execution,
 )
 from .spans.ai_client import ai_client_span, update_ai_client_span
@@ -121,11 +120,6 @@ def setup_once() -> None:
         _patch_agent_run()
         _patch_tool_execution()
 
-        # ModelRequestContext.model added in https://github.com/pydantic/pydantic-ai/commit/f1260dfe09907f17688eee1646daf898fc428d4c
-        if PYDANTIC_AI_VERSION < (1, 73):
-            _patch_graph_nodes()
-            return
-
         try:
             from pydantic_ai.capabilities import Hooks
         except ImportError:

sentry_sdk/integrations/pydantic_ai/patches/__init__.py

@@ -1,3 +1,2 @@
 from .agent_run import _patch_agent_run  # noqa: F401
-from .graph_nodes import _patch_graph_nodes  # noqa: F401
 from .tools import _patch_tool_execution  # noqa: F401

sentry_sdk/integrations/pydantic_ai/patches/tools.py

@@ -28,10 +28,6 @@ def _patch_tool_execution() -> None:
     if hasattr(ToolManager, "execute_tool_call"):
         _patch_execute_tool_call()
 
-    elif hasattr(ToolManager, "_call_tool"):
-        # older versions
-        _patch_call_tool()
-
 
 def _patch_execute_tool_call() -> None:
     original_execute_tool_call = ToolManager.execute_tool_call
@@ -102,82 +98,3 @@ async def wrapped_execute_tool_call(
         return await original_execute_tool_call(self, validated, *args, **kwargs)
 
     ToolManager.execute_tool_call = wrapped_execute_tool_call  # type: ignore[method-assign]
-
-
-def _patch_call_tool() -> None:
-    """
-    Patch ToolManager._call_tool to create execute_tool spans.
-
-    This is the single point where ALL tool calls flow through in pydantic_ai,
-    regardless of toolset type (function, MCP, combined, wrapper, etc.).
-
-    By patching here, we avoid:
-    - Patching multiple toolset classes
-    - Dealing with signature mismatches from instrumented MCP servers
-    - Complex nested toolset handling
-    """
-    original_call_tool = ToolManager._call_tool  # type: ignore[attr-defined]
-
-    @wraps(original_call_tool)
-    async def wrapped_call_tool(
-        self: "Any", call: "Any", *args: "Any", **kwargs: "Any"
-    ) -> "Any":
-        # Extract tool info before calling original
-        name = call.tool_name
-        tool = self.tools.get(name) if self.tools else None
-        selected_tool_definition = getattr(tool, "tool_def", None)
-
-        # Get agent from contextvar
-        agent = get_current_agent()
-
-        if agent and tool:
-            try:
-                args_dict = call.args_as_dict()
-            except Exception:
-                args_dict = call.args if isinstance(call.args, dict) else {}
-
-            # Create execute_tool span
-            # Nesting is handled by isolation_scope() to ensure proper parent-child relationships
-            with sentry_sdk.isolation_scope():
-                with execute_tool_span(
-                    name,
-                    args_dict,
-                    agent,
-                    tool_definition=selected_tool_definition,
-                ) as span:
-                    try:
-                        result = await original_call_tool(
-                            self,
-                            call,
-                            *args,
-                            **kwargs,
-                        )
-                        update_execute_tool_span(span, result)
-                        return result
-                    except ToolRetryError as exc:
-                        exc_info = sys.exc_info()
-                        with capture_internal_exceptions():
-                            # Avoid circular import due to multi-file integration structure
-                            from sentry_sdk.integrations.pydantic_ai import (
-                                PydanticAIIntegration,
-                            )
-
-                            integration = sentry_sdk.get_client().get_integration(
-                                PydanticAIIntegration
-                            )
-                            if (
-                                integration is not None
-                                and integration.handled_tool_call_exceptions
-                            ):
-                                _capture_exception(exc, handled=True)
-                        reraise(*exc_info)
-
-        # No span context - just call original
-        return await original_call_tool(
-            self,
-            call,
-            *args,
-            **kwargs,
-        )
-
-    ToolManager._call_tool = wrapped_call_tool  # type: ignore[attr-defined]

sentry_sdk/integrations/pydantic_ai/spans/ai_client.py

@@ -31,6 +31,8 @@
     from typing import Any, Dict, List, Optional, Union
 
     from pydantic_ai.messages import ModelMessage, ModelResponse, SystemPromptPart
+    from pydantic_ai.models import Model
+    from pydantic_ai.settings import ModelSettings
 
     from sentry_sdk import _types
 
@@ -99,7 +101,7 @@ def _get_system_instructions(
     return permanent_instructions, current_instructions
 
 
-def _set_input_messages(span: "StreamedSpan", messages: "Any") -> None:
+def _set_input_messages(span: "StreamedSpan", messages: "list[ModelMessage]") -> None:
     """Set input messages data on a span."""
     if not _should_send_inputs():
         return
@@ -268,7 +270,10 @@ def _set_output_data(
 
 
 def ai_client_span(
-    messages: "Any", agent: "Any", model: "Any", model_settings: "Any"
+    messages: "list[ModelMessage]",
+    agent: "Any",
+    model: "Model",
+    model_settings: "Optional[ModelSettings]",
 ) -> "StreamedSpan":
     """Create a span for an AI client call (model request).
 

sentry_sdk/integrations/pydantic_ai/utils.py

@@ -14,6 +14,8 @@
 if TYPE_CHECKING:
     from typing import Any, Optional
 
+    from pydantic_ai.models import Model
+
 
 # Store the current agent context in a contextvar for re-entrant safety
 # Using a list as a stack to support nested agent calls
@@ -107,7 +109,7 @@ def _set_agent_data(span: "StreamedSpan", agent: "Any") -> None:
         span.set_attribute(SPANDATA.GEN_AI_AGENT_NAME, agent_obj.name)
 
 
-def _get_model_name(model_obj: "Any") -> "Optional[str]":
+def _get_model_name(model_obj: "Model") -> "Optional[str]":
     """Extract model name from a model object.
 
     Args: