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Minor fixes: Agent.__aenter__ returns Self, use default instrumentation for MCP sampling model #2765

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Merged
alexmojaki merged 2 commits into from
Sep 2, 2025
Merged

Minor fixes: Agent.__aenter__ returns Self, use default instrumentation for MCP sampling model #2765

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Minor fixes: Agent.__aenter__ returns Self, use default instrumentati…
alexmojaki Sep 2, 2025
9b7654e
Merge branch 'main' into alex/minor
DouweM Sep 2, 2025
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4 changes: 2 additions & 2 deletions pydantic_ai_slim/pydantic_ai/agent/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@
import anyio
from opentelemetry.trace import NoOpTracer, use_span
from pydantic.json_schema import GenerateJsonSchema
from typing_extensions import TypeVar, deprecated
from typing_extensions import Self, TypeVar, deprecated

from pydantic_graph import Graph

Expand Down Expand Up @@ -1355,7 +1355,7 @@ def _prepare_output_schema(

return schema # pyright: ignore[reportReturnType]

async def __aenter__(self) -> AbstractAgent[AgentDepsT, OutputDataT]:
async def __aenter__(self) -> Self:
"""Enter the agent context.

This will start all [`MCPServerStdio`s][pydantic_ai.mcp.MCPServerStdio] registered as `toolsets` so they are ready to be used.
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7 changes: 2 additions & 5 deletions pydantic_ai_slim/pydantic_ai/mcp.py
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Expand Up @@ -20,6 +20,7 @@

from pydantic_ai.tools import RunContext, ToolDefinition

from .direct import model_request
from .toolsets.abstract import AbstractToolset, ToolsetTool

try:
Expand Down Expand Up @@ -329,11 +330,7 @@ async def _sampling_callback(
if stop_sequences := params.stopSequences: # pragma: no branch
model_settings['stop_sequences'] = stop_sequences

model_response = await self.sampling_model.request(
pai_messages,
model_settings,
models.ModelRequestParameters(),
)
model_response = await model_request(self.sampling_model, pai_messages, model_settings=model_settings)
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@alexmojaki alexmojaki Sep 2, 2025

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This way logfire.instrument_pydantic_ai() will instrument the sampling model.

return mcp_types.CreateMessageResult(
role='assistant',
content=_mcp.map_from_model_response(model_response),
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