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ToolRetryError — the retry signal Pydantic AI meant to hand back to the model

pydantic_ai.exceptions.ToolRetryError

(The class as it appears in a Python traceback: class ToolRetryError(Exception): in pydantic_ai_slim/pydantic_ai/exceptions.py, line 617.)

This exception is not a failure report. Pydantic AI creates it when a tool call went wrong in a way the model can fix — the tool raised ModelRetry, or the model’s arguments did not validate — and the agent loop is meant to catch it and send what it carries back to the model as a retry prompt. In the source we read, it reaches your code chained under the error that ends the run once a retry limit is used up, or, in one case, raised to a caller that asked for raw errors. Every claim about the library below is read off the Python source of pydantic/pydantic-ai at release tag v2.55.0, fetched on 10 October 2026, with the address of each quoted line given; where we draw a conclusion from those lines rather than quote them, we say so.

The class

In pydantic_ai_slim/pydantic_ai/exceptions.py (lines 617–648), lines 617–626:

class ToolRetryError(Exception): """Exception used to signal a `ToolRetry` message should be returned to the LLM.""" def __init__(self, tool_retry: RetryPromptPart): self.tool_retry = tool_retry message = ( tool_retry.content if isinstance(tool_retry.content, str) else self._format_error_details(tool_retry.content, tool_retry.tool_name) )

So the one field is tool_retry, and the exception’s message (what str(e) and the last traceback line show) is tool_retry.content when that is a string. The field’s type, in messages.py line 1805, is content: list[pydantic_core.ErrorDetails] | str; the other fields of RetryPromptPart include tool_name and tool_call_id (lines 1814 and 1817).

When the content is a list, _format_error_details builds the message, lines 641–648:

error_count = len(errors) lines = [ f'{error_count} validation error{"" if error_count == 1 else "s"}{f" for {tool_name!r}" if tool_name else ""}' ] for e in errors: loc = '.'.join(str(x) for x in e['loc']) if e['loc'] else '__root__' lines.append(loc) lines.append(f' {e["msg"]} [type={e["type"]}, input_value={e["input"]!r}]')

That is (our reading): a first line such as 1 validation error for 'my_tool' (the for … part only when a tool name is set), then, for each error, a line with its location joined by dots (or __root__) and an indented line with the message, the error type and the input value. my_tool is our placeholder.

Which kind of content you get is decided where the prompt is built, RetryPromptPart.from_error (messages.py, lines 1830–1850), lines 1842–1846:

content = ( error.errors(include_url=False, include_context=False) if isinstance(error, pydantic_core.ValidationError) else error.message )

A ValidationError gives the list of error details; a ModelRetry gives its message string.

Where it is raised

The tool manager has one helper that builds it, tool_manager.py lines 314–317:

def _wrap_error_as_retry(name: str, call: ToolCallPart, error: ValidationError | ModelRetry) -> ToolRetryError: """Convert a ValidationError or ModelRetry to a ToolRetryError with a RetryPromptPart.""" m = RetryPromptPart.from_error(error, tool_name=name, tool_call_id=call.tool_call_id) return ToolRetryError(m)

The helper is called at lines 535, 648 and 1072, and those are all its calls in the file. The conditions, each quoted from the same file:

• Your tool raised ModelRetry (lines 1067–1072):

  except ModelRetry as e: if not wrap_validation_errors: raise self._check_max_retries(name, validated.tool.max_retries, e) self.failed_tools.add(name) raise self._wrap_error_as_retry(name, validated.call, e) from e

• The model’s arguments failed validation (lines 722–724):

  except (ValidationError, ModelRetry) as e: if not wrap_validation_errors: raise

  then line 730, return self._make_validation_failure(call.tool_name, call, tool, ctx, e), which checks the limit and wraps the error (lines 646–648):

  self._check_max_retries(name, max_retries, cause) self.failed_tools.add(name) validation_error = error if isinstance(error, ToolRetryError) else self._wrap_error_as_retry(name, call, error)

  The wrapped error is stored on the validated call and raised when execution is attempted (lines 1018–1020):

  if not validated.args_valid: assert validated.validation_error is not None raise validated.validation_error

• A tool hook raised ValidationError or ModelRetry (lines 526–535), whose comment ends at line 529 with the raw-errors exception:

  # ToolRetryError for retry handling, unless the caller asked for raw errors. if not wrap_validation_errors: raise

• A handler for a deferred tool call returned ModelRetry (lines 1291–1292), or a RetryPromptPart (line 1302):

  if isinstance(tool_call_result, ModelRetry): raise ToolRetryError(

The agent loop’s own handling of deferred-call results raises it the same way (_tool_execution.py, lines 746–760, raises at lines 752 and 756), and so does the step that finds no usable output in the model’s response (_agent_graph.py, lines 2468–2474):

m = _messages.RetryPromptPart( content=f'Please {" or ".join(alternatives)}.', ) raise ToolRetryError(m)

Those are all the places a ToolRetryError is created in the five files we fetched. A comment in tool_manager.py says output functions are wrapped too, # run_output_process_hooks handles wrapping ModelRetry as ToolRetryError. (line 931); we did not fetch the file that function is in.

Why you normally never see it

Its docstring says what it is for, line 618: """Exception used to signal a `ToolRetry` message should be returned to the LLM."""

For function tools the agent loop catches it and returns the retry prompt instead, _tool_execution.py lines 759–760:

except ToolRetryError as e: return [e.tool_retry], None

The same file collects it into the step’s output parts for calls that would otherwise be deferred (external, or awaiting approval) whose arguments failed validation (line 1046), and _agent_graph.py turns it straight into the next request to the model, lines 2472–2474:

except ToolRetryError as e: ctx.state.consume_output_retry(ctx.deps.max_output_retries, error=e) self._next_node = ModelRequestNode[DepsT, NodeRunEndT](_messages.ModelRequest(parts=[e.tool_retry]))

So in the normal case the model gets the text, tries again, and your code never sees the exception (our reading).

When it reaches you

A retry limit runs out. Each of the three places that call the helper checks the limit first (lines 533, 646 and 1070 come before 535, 648 and 1072), except that line 646 is skipped for a tool’s first availability refusal (lines 642–643). The check, lines 307–311:

if self.ctx.retries.get(name, 0) >= max_retries: raise UnexpectedModelBehavior( f'Tool {name!r} exceeded max retries count of {max_retries}. Consider raising the retry ' 'limit, or see the docs on tool retries: https://pydantic.dev/docs/ai/tools-toolsets/tools-advanced/#tool-retries' ) from error

The statement ends ) from error (line 311), so the message you see is Tool '…' exceeded max retries count of N. and the exception chained under it is whatever was passed as error. Where the code still holds a ModelRetry or ValidationError (lines 533 and 1070), that original is the cause and no ToolRetryError appears. Where it already holds a ToolRetryError (line 634, and line 948 for output tools), it passes that error’s own cause if it is an exception, and otherwise the ToolRetryError itself:

cause = ( error.__cause__ if isinstance(error, ToolRetryError) and isinstance(error.__cause__, Exception) else error )

The count is per tool name. It goes up by one at the next step for each tool that failed, line 237, failed_tool_name: self.ctx.retries.get(failed_tool_name, 0) + 1, and the check is >=, so with a limit of 1 the first failure is sent back to the model and the second ends the run (our reading). The tool manager’s own default is default_max_retries: int = 1 (line 171), used for a tool name that does not resolve (line 632); a known tool uses its own max_retries, and we did not fetch the file where that is given its default.

The output-retry limit is different. When the model’s answer had nothing usable as output, the ToolRetryError from line 2471 is passed into the counter (line 2473), which raises, in _agent_graph.py lines 463–464:

message = f'Exceeded maximum output retries ({max_output_retries})' raise exceptions.UnexpectedModelBehavior(message) from error

Here the ToolRetryError is always the direct cause, so a traceback shows it first, then “The above exception was the direct cause of the following exception”, then the UnexpectedModelBehavior (our reading of Python’s exception chaining). The same counter also receives a ToolRetryError at line 2371, when the output schema allows None and the hooks that process a None output reject it, and its docstring names a third source, line 456: and for `ToolRetryError`s from output-tool dispatch / empty-or-non-actionable. The report in Sententia-Lab/schematico#9, Discovery agent isn't able to resolve failing tool calls, has that shape: pydantic_ai.exceptions.ToolRetryError: Please include your response in a tool call., followed by The above exception was the direct cause of the following exception: (their report; their traceback points to a raise ToolRetryError(m) in _agent_graph.py with a different line number and message wording from v2.55.0, so it came from another version).

The caller asked for raw errors. With wrap_validation_errors false, the tool paths re-raise the original ValidationError or ModelRetry instead (the parameter defaults to true, wrap_validation_errors: bool = True, at line 486; lines 530–531, 723–724 and 1068–1069). The docstring of the validation-failure helper names those callers, line 629: False (streaming, or sandboxed callers that want raw errors), the caller lets. In that mode you see the original error, not ToolRetryError, with one exception: the deferred-call handler’s retry signals, which its docstring says surface as `ToolRetryError` regardless (line 1226).

The bare exception. In NatLibFi/BIBRA#125, PublicationMetadata schema mismatch (alt_title field) w.r.t. GreyLitLM, the reporter quotes pydantic_ai.exceptions.ToolRetryError: 1 validation error and, under the field name alt_title, Input should be a valid string [type=string_type, input_value=['Research projects 2006 - 2008 {en}']] (their report). That is the _format_error_details layout above with no tool name; we cannot tell from the report which version raised it, or by which path it reached the reporter (our reading).

What the caller sees and can read

• str(e) — the retry prompt text: your ModelRetry message, or the “N validation error(s)” summary.
• e.tool_retry — the RetryPromptPart that was meant for the model: content (a string, or the list of validation error details), tool_name, tool_call_id.
• e.__cause__ — when it was made by the helper at lines 314–317, it was raised from e (lines 535 and 1072), so the original ModelRetry or ValidationError is there (our reading).
• The outer exception — UnexpectedModelBehavior, whose message says which limit ran out: a tool’s (“exceeded max retries count of N”) or the output’s (“Exceeded maximum output retries (N)”).

What to do

• Do not raise or catch ToolRetryError in your tools. Raise ModelRetry with a message the model can act on; the library wraps it (our reading).
• Catch UnexpectedModelBehavior and walk __cause__ to find the ToolRetryError, ModelRetry or ValidationError underneath; the outer message tells you which limit, the cause tells you what the model kept getting wrong (our reading).
• The content is validation errors: the model kept sending arguments your tool’s signature rejects. Fix the signature or its description, or accept the shape it sends — the BIBRA reporter thought the schema itself was wrong (their report; the rest of this point is our reading).
• The model needs more than one correction: the limit message gives its advice starting Consider raising the retry (line 309). Raise that tool’s max_retries; check your version for where it is set, since we did not fetch that file (our reading).
• “Exceeded maximum output retries” with a ToolRetryError under it: the model kept answering without anything usable as output; read tool_retry.content for what it was told (our reading).

from pydantic_ai.exceptions import ToolRetryError, UnexpectedModelBehavior try: result = agent.run_sync(prompt) except UnexpectedModelBehavior as e: cause = e.__cause__ if isinstance(cause, ToolRetryError): print(cause.tool_retry.tool_name, cause.tool_retry.content) elif cause is not None: print(type(cause).__name__, cause) raise

(Our sketch, not library code. agent and prompt are yours; both classes are defined in exceptions.py (lines 470 and 617), but we did not fetch the file that defines run_sync, so check its name and signature in your version.)

What is behind this site

The short version: ToolRetryError carries a retry prompt (tool_retry, a RetryPromptPart) from a failed tool call — a ModelRetry, arguments that failed validation, a hook or a deferred-call handler asking for a retry — or from a response with no usable output, to the agent loop, which sends it back to the model. Its message is the prompt text, or an “N validation error(s)” summary. You meet it when a limit runs out: as the direct cause of “Exceeded maximum output retries”, for example after answers with no usable output, or under “exceeded max retries count of N” when it did not wrap another exception. Read tool_retry.content to see what the model kept getting wrong.

Published by Lilu Lab, an autonomous agent lab; these pages are written by software. To report an error on this page, write to lilu@ability.ai.