Errors

Agents can raise errors, for example when the underlying model fails to generate a response or when there are content moderation issues.

All errors are wrapped in a WorkflowAIError that contains details about what happened. The most interesting fields are:

• code is a string that identifies the type of error, see the errors.py file for more details

• message is a human readable message that describes the error

The WorkflowAIError is raised when the agent is called, so you can handle it like any other exception.

from datetime import date
import workflowai
from pydantic import BaseModel, Field
from workflowai import Model, WorkflowAIError

# define your input and output fields
class Input(BaseModel):
    transcript: str
    call_date: date

class Output(BaseModel):
    positive_points: list[str] = Field(description="List of positive points from the call", default_factory=list)
    negative_points: list[str] = Field(description="List of negative points from the call", default_factory=list)

# define your agent
@workflowai.agent(model=Model.GEMINI_2_0_FLASH_LATEST)
async def analyze_call_feedback(input: Input) -> Output:
    """
    Analyze customer call feedback and extract positive and negative points.
    """
    ...

try:
    await analyze_call_feedback(
        CallFeedbackInput(
            transcript="[00:01:15] Customer: The product is great!",
            call_date=date(2024, 1, 15)
        )
    )
except WorkflowAIError as e:
    print(e.code)
    print(e.message)

Recoverable errors

Sometimes, the LLM outputs an object that is partially valid, good examples are:

• the model context window was exceeded during the generation

• the model decided that a tool call result was a failure

In this case, an agent that returns an output only will always raise an InvalidGenerationError which subclasses WorkflowAIError.

However, an agent that returns a full run object will try to recover from the error by using the partial output.

run = await agent(input=Input(name="John"))

# The run will have an error
assert run.error is not None

# The run will have a partial output
assert run.output is not None