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Initial Checks

• I'm using the latest version of Pydantic AI
• I've searched for my issue in the issue tracker before opening this issue

Description

Initial Checks

• I confirm that I'm using the latest version of Pydantic AI
• I confirm that I searched for my issue in https://github.com/pydantic/pydantic-ai/issues before opening this issue

Description

When setting retries=0 on the Agent, the value is not propagated to toolsets passed via the toolsets= parameter. The toolset retains its own default max_retries=1, so tools inside it get 1 retry instead of 0.

The docs state:

"Both ValidationError and ModelRetry respect the retries setting configured on the Tool or Agent."

This is accurate for tools registered directly on the agent (@agent.tool, tools=[...]), but not for tools inside user-provided toolsets.

Root Cause

In Agent.__init__, the agent's retries value is passed to _AgentFunctionToolset (for directly registered tools), but user-provided toolsets are stored as-is without overriding their max_retries:

self._max_tool_retries = retries

# ✅ Agent's own tools get the value:
self._function_toolset = _AgentFunctionToolset(
    tools, max_retries=self._max_tool_retries, ...
)

# ❌ User toolsets keep their own default (1):
self._user_toolsets = [toolset for toolset in toolsets or [] if isinstance(toolset, AbstractToolset)]

Then in _tool_manager.py, the retry check uses tool.max_retries which comes from the toolset's default:

except (ValidationError, ModelRetry) as e:
    max_retries = tool.max_retries if tool is not None else 1
    current_retry = self.ctx.retries.get(name, 0)
    if current_retry == max_retries:
        raise UnexpectedModelBehavior(...)

Reproduction

from typing import NoReturn
from pydantic_ai import Agent, FunctionToolset, RunContext
from pydantic_ai.exceptions import ModelRetry

test_toolset = FunctionToolset()

@test_toolset.tool()
async def always_fails(ctx: RunContext) -> NoReturn:
    raise ModelRetry("Always fails")

agent = Agent('test', toolsets=[test_toolset], retries=0)

# Expected: fails immediately (0 retries → no retry allowed)
# Actual: allows 1 retry, then fails with "exceeded max retries count of 1"
result = agent.run_sync('call always_fails')

Suggested Fix

Have FunctionToolset default max_retries to None instead of 1, and resolve to the agent's value at runtime — similar to how individual Tool.max_retries=None already falls back to the toolset's max_retries. This creates a consistent resolution chain: Tool → Toolset → Agent.

Workaround

Set max_retries explicitly on the toolset:

test_toolset = FunctionToolset(max_retries=0)

Or per-tool:

@test_toolset.tool(retries=0)
async def always_fails(ctx: RunContext) -> NoReturn:
    raise ModelRetry("Always fails")

Label: bug

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Minimal, Reproducible Example

Logfire Trace

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Python, Pydantic AI & LLM client version

• Python:
• Pydantic AI:
• LLM provider SDK: