Documentation Index
Fetch the complete documentation index at: https://docs.sudoiq.com/llms.txt
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Output format validators constrain structured outputs (for example validating a JSON shape after a node runs). Manage them with client.agent_validation.
Upsert from Pydantic models
Pass Pydantic classes (not instances). The client uses each class __name__ as the validator name and model_json_schema() as the JSON Schema.
from pydantic import BaseModel, Field
class Invoice(BaseModel):
vendor: str = Field(...)
total: float = Field(...)
result = await client.agent_validation.upsert_validators([Invoice])
Raw JSON Schema dicts
await client.agent_validation.upsert_validators([
{"name": "Invoice", "json_schema": {"type": "object", "properties": {}}},
])
List and fetch
validators = await client.agent_validation.list_validators(limit=20)
v = await client.agent_validation.get_validator(validators.validators[0].id)
Attach validators to nodes in your agent graph configuration in the product UI or API so failed validation can trigger retries where your platform supports it.