Error Recovery for AI Agents

This package ships error handling and recovery patterns for improved reliability of AI Agents, as a native Pydantic AI 2.0 capability.

[!NOTE] This package is in early development, the API might still change before v1.0.

Motivation

Agentic tool calling can fail for many reasons, and adding error handling and retries to every tool is cumbersome. For third-party tools and MCPs, it's often impossible. ToolErrorRecovery is a standardized capability for AI agents to recover from errors during tool calls. Similar in spirit to FastAPI's exception handlers, but for agents.

Related Work

This package combines the ideas and vocabulary of pydantic-ai-harness PR #171 with its own mechanics: it uses a classify callable rather than a per-tool strategy map for more flexibility; a single wrap_tool_execute hook rather than two; and does not expose str(error) to the agent by default for added security. It is intended as a stand-alone capability, but also as a proposal of how to make progress on PR #171.

Quick Start

pip install pydantic-ai-error-recovery

from pydantic_ai import Agent
from pydantic_ai_error_recovery import ToolErrorRecovery

agent = Agent('openai:gpt-5', capabilities=[ToolErrorRecovery()])

Out of the box, connection errors are retried, bugs are propagated, and everything else is reported to the model, preventing a crash of the agent run. Continue reading to learn how to customize the capability.

Features

The ToolErrorRecovery capability supports four different recovery outcomes:

Error Classification

ToolErrorRecovery intercepts tool execution errors and applies a per-error reaction. A classify callable you supply inspects each failure and returns the intended recovery outcome:

def classify(ctx: RunContext[Any], call: ToolCallPart, error: BaseException) -> RecoveryOutcome:
    if isinstance(error, SomeException):
        return RecoveryOutcome.inform(
            label='Tool call ran into SomeException',
            expose_message=False,
        )
    # and so on

Notes:

• You can classify on a tool's name and other properties of the tool call, on the exception type, or both.
• The ctx: RunContext parameter is included for cases in which your classification logic additionally depends on the run context.
• To not interfere with existing error handling, the following exceptions from the Pydantic AI control flow are never classified: SkipToolExecution, CallDeferred, ApprovalRequired, ModelRetry, ToolRetryError, ToolFailed, and ToolFailedError.
• Special care must be taken when classifying an exception which is a subclass, be careful about the order of isinstance(...) calls, or use type(...) is instead.

Default Behaviour

As mentioned above, control flow exceptions are never classified. Our default classifier is:

def classify(ctx: RunContext[Any], call: ToolCallPart, error: BaseException) -> RecoveryOutcome:
    if isinstance(error, DEFAULT_BUG_TYPES):
        return RecoveryOutcome.propagate()
    if isinstance(error, HookTimeoutError):
        return RecoveryOutcome.inform()
    if isinstance(error, DEFAULT_TRANSIENT_TYPES):
        return RecoveryOutcome.retry(3)
    return RecoveryOutcome.inform()

The classification function is the key piece required to construct your own RecoveryPolicy via RecoveryPolicy(classify=classify). A fully custom example could look like this:

from pydantic_ai import Agent
from pydantic_ai_error_recovery import ToolErrorRecovery

def classify(...):
    # your classification logic

agent = Agent('openai:gpt-5', capabilities=[
    ToolErrorRecovery(
        policy=RecoveryPolicy(
            classify=classify,
            format_error=my_formatter,
            max_message_len=450,
            include_traceback=False,
            logger=my_logger,
        ),
        max_recoveries=10,
        per_tool_recoveries={'my_tool_name': 5},
    )
])

Contributing

We welcome community contributions under Apache-2.0. Please create an issue to discuss an improvement, or submit a pull request. Clone the repository, then run:

make install   # uv sync --extra dev
make all       # lint, format, typecheck, and tests with 100% branch coverage

Please ensure that make all passes before committing. Coding standards, file layout, and testing patterns can be found in AGENTS.md.

License

Apache-2.0