iterative structured output

src/strands/agent/agent.py

@@ -16,7 +16,7 @@
 import random
 from concurrent.futures import ThreadPoolExecutor
 from threading import Thread
-from typing import Any, AsyncIterator, Callable, Dict, List, Mapping, Optional, Type, TypeVar, Union
+from typing import Any, AsyncIterator, Callable, Dict, List, Mapping, Optional, Type, TypeVar, Union, cast
 from uuid import uuid4
 
 from opentelemetry import trace
@@ -423,7 +423,12 @@ def structured_output(self, output_model: Type[T], prompt: Optional[str] = None)
             messages.append({"role": "user", "content": [{"text": prompt}]})
 
         # get the structured output from the model
-        return self.model.structured_output(output_model, messages, self.callback_handler)
+        events = self.model.structured_output(output_model, messages)
+        for event in events:
+            if "callback" in event:
+                self.callback_handler(**cast(dict, event["callback"]))
+
+        return event["output"]
 
     async def stream_async(self, prompt: str, **kwargs: Any) -> AsyncIterator[Any]:
         """Process a natural language prompt and yield events as an async iterator.

src/strands/models/anthropic.py

@@ -7,14 +7,13 @@
 import json
 import logging
 import mimetypes
-from typing import Any, Callable, Iterable, Optional, Type, TypedDict, TypeVar, cast
+from typing import Any, Generator, Iterable, Optional, Type, TypedDict, TypeVar, Union, cast
 
 import anthropic
 from pydantic import BaseModel
 from typing_extensions import Required, Unpack, override
 
 from ..event_loop.streaming import process_stream
-from ..handlers.callback_handler import PrintingCallbackHandler
 from ..tools import convert_pydantic_to_tool_spec
 from ..types.content import ContentBlock, Messages
 from ..types.exceptions import ContextWindowOverflowException, ModelThrottledException
@@ -378,24 +377,24 @@ def stream(self, request: dict[str, Any]) -> Iterable[dict[str, Any]]:
 
     @override
     def structured_output(
-        self, output_model: Type[T], prompt: Messages, callback_handler: Optional[Callable] = None
-    ) -> T:
+        self, output_model: Type[T], prompt: Messages
+    ) -> Generator[dict[str, Union[T, Any]], None, None]:
         """Get structured output from the model.
 
         Args:
             output_model(Type[BaseModel]): The output model to use for the agent.
             prompt(Messages): The prompt messages to use for the agent.
-            callback_handler(Optional[Callable]): Optional callback handler for processing events. Defaults to None.
+
+        Yields:
+            Model events with the last being the structured output.
         """
-        callback_handler = callback_handler or PrintingCallbackHandler()
         tool_spec = convert_pydantic_to_tool_spec(output_model)
 
         response = self.converse(messages=prompt, tool_specs=[tool_spec])
         for event in process_stream(response, prompt):
-            if "callback" in event:
-                callback_handler(**event["callback"])
-        else:
-            stop_reason, messages, _, _ = event["stop"]
+            yield event
+
+        stop_reason, messages, _, _ = event["stop"]
 
         if stop_reason != "tool_use":
             raise ValueError("No valid tool use or tool use input was found in the Anthropic response.")
@@ -413,4 +412,4 @@ def structured_output(
         if output_response is None:
             raise ValueError("No valid tool use or tool use input was found in the Anthropic response.")
 
-        return output_model(**output_response)
+        yield {"output": output_model(**output_response)}

src/strands/models/bedrock.py

@@ -6,7 +6,7 @@
 import json
 import logging
 import os
-from typing import Any, Callable, Iterable, List, Literal, Optional, Type, TypeVar, cast
+from typing import Any, Generator, Iterable, List, Literal, Optional, Type, TypeVar, Union, cast
 
 import boto3
 from botocore.config import Config as BotocoreConfig
@@ -15,7 +15,6 @@
 from typing_extensions import TypedDict, Unpack, override
 
 from ..event_loop.streaming import process_stream
-from ..handlers.callback_handler import PrintingCallbackHandler
 from ..tools import convert_pydantic_to_tool_spec
 from ..types.content import Messages
 from ..types.exceptions import ContextWindowOverflowException, ModelThrottledException
@@ -521,24 +520,24 @@ def _find_detected_and_blocked_policy(self, input: Any) -> bool:
 
     @override
     def structured_output(
-        self, output_model: Type[T], prompt: Messages, callback_handler: Optional[Callable] = None
-    ) -> T:
+        self, output_model: Type[T], prompt: Messages
+    ) -> Generator[dict[str, Union[T, Any]], None, None]:
         """Get structured output from the model.
 
         Args:
             output_model(Type[BaseModel]): The output model to use for the agent.
             prompt(Messages): The prompt messages to use for the agent.
-            callback_handler(Optional[Callable]): Optional callback handler for processing events. Defaults to None.
+
+        Yields:
+            Model events with the last being the structured output.
         """
-        callback_handler = callback_handler or PrintingCallbackHandler()
         tool_spec = convert_pydantic_to_tool_spec(output_model)
 
         response = self.converse(messages=prompt, tool_specs=[tool_spec])
         for event in process_stream(response, prompt):
-            if "callback" in event:
-                callback_handler(**event["callback"])
-        else:
-            stop_reason, messages, _, _ = event["stop"]
+            yield event
+
+        stop_reason, messages, _, _ = event["stop"]
 
         if stop_reason != "tool_use":
             raise ValueError("No valid tool use or tool use input was found in the Bedrock response.")
@@ -556,4 +555,4 @@ def structured_output(
         if output_response is None:
             raise ValueError("No valid tool use or tool use input was found in the Bedrock response.")
 
-        return output_model(**output_response)
+        yield {"output": output_model(**output_response)}

src/strands/models/litellm.py

@@ -5,7 +5,7 @@
 
 import json
 import logging
-from typing import Any, Callable, Optional, Type, TypedDict, TypeVar, cast
+from typing import Any, Generator, Optional, Type, TypedDict, TypeVar, Union, cast
 
 import litellm
 from litellm.utils import supports_response_schema
@@ -105,15 +105,16 @@ def format_request_message_content(cls, content: ContentBlock) -> dict[str, Any]
 
     @override
     def structured_output(
-        self, output_model: Type[T], prompt: Messages, callback_handler: Optional[Callable] = None
-    ) -> T:
+        self, output_model: Type[T], prompt: Messages
+    ) -> Generator[dict[str, Union[T, Any]], None, None]:
         """Get structured output from the model.
 
         Args:
             output_model(Type[BaseModel]): The output model to use for the agent.
             prompt(Messages): The prompt messages to use for the agent.
-            callback_handler(Optional[Callable]): Optional callback handler for processing events. Defaults to None.
 
+        Yields:
+            Model events with the last being the structured output.
         """
         # The LiteLLM `Client` inits with Chat().
         # Chat() inits with self.completions
@@ -136,7 +137,8 @@ def structured_output(
                     # Parse the tool call content as JSON
                     tool_call_data = json.loads(choice.message.content)
                     # Instantiate the output model with the parsed data
-                    return output_model(**tool_call_data)
+                    yield {"output": output_model(**tool_call_data)}
+                    return
                 except (json.JSONDecodeError, TypeError, ValueError) as e:
                     raise ValueError(f"Failed to parse or load content into model: {e}") from e
 

src/strands/models/llamaapi.py

@@ -8,7 +8,7 @@
 import json
 import logging
 import mimetypes
-from typing import Any, Callable, Iterable, Optional, Type, TypeVar, cast
+from typing import Any, Generator, Iterable, Optional, Type, TypeVar, Union, cast
 
 import llama_api_client
 from llama_api_client import LlamaAPIClient
@@ -390,14 +390,16 @@ def stream(self, request: dict[str, Any]) -> Iterable[dict[str, Any]]:
 
     @override
     def structured_output(
-        self, output_model: Type[T], prompt: Messages, callback_handler: Optional[Callable] = None
-    ) -> T:
+        self, output_model: Type[T], prompt: Messages
+    ) -> Generator[dict[str, Union[T, Any]], None, None]:
         """Get structured output from the model.
 
         Args:
             output_model(Type[BaseModel]): The output model to use for the agent.
             prompt(Messages): The prompt messages to use for the agent.
-            callback_handler(Optional[Callable]): Optional callback handler for processing events. Defaults to None.
+
+        Yields:
+            Model events with the last being the structured output.
 
         Raises:
             NotImplementedError: Structured output is not currently supported for LlamaAPI models.

src/strands/models/ollama.py

@@ -5,7 +5,7 @@
 
 import json
 import logging
-from typing import Any, Callable, Iterable, Optional, Type, TypeVar, cast
+from typing import Any, Generator, Iterable, Optional, Type, TypeVar, Union, cast
 
 from ollama import Client as OllamaClient
 from pydantic import BaseModel
@@ -316,14 +316,16 @@ def stream(self, request: dict[str, Any]) -> Iterable[dict[str, Any]]:
 
     @override
     def structured_output(
-        self, output_model: Type[T], prompt: Messages, callback_handler: Optional[Callable] = None
-    ) -> T:
+        self, output_model: Type[T], prompt: Messages
+    ) -> Generator[dict[str, Union[T, Any]], None, None]:
         """Get structured output from the model.
 
         Args:
             output_model(Type[BaseModel]): The output model to use for the agent.
             prompt(Messages): The prompt messages to use for the agent.
-            callback_handler(Optional[Callable]): Optional callback handler for processing events. Defaults to None.
+
+        Yields:
+            Model events with the last being the structured output.
         """
         formatted_request = self.format_request(messages=prompt)
         formatted_request["format"] = output_model.model_json_schema()
@@ -332,6 +334,6 @@ def structured_output(
 
         try:
             content = response.message.content.strip()
-            return output_model.model_validate_json(content)
+            yield {"output": output_model.model_validate_json(content)}
         except Exception as e:
             raise ValueError(f"Failed to parse or load content into model: {e}") from e

src/strands/models/openai.py

@@ -4,7 +4,7 @@
 """
 
 import logging
-from typing import Any, Callable, Iterable, Optional, Protocol, Type, TypedDict, TypeVar, cast
+from typing import Any, Generator, Iterable, Optional, Protocol, Type, TypedDict, TypeVar, Union, cast
 
 import openai
 from openai.types.chat.parsed_chat_completion import ParsedChatCompletion
@@ -133,14 +133,16 @@ def stream(self, request: dict[str, Any]) -> Iterable[dict[str, Any]]:
 
     @override
     def structured_output(
-        self, output_model: Type[T], prompt: Messages, callback_handler: Optional[Callable] = None
-    ) -> T:
+        self, output_model: Type[T], prompt: Messages
+    ) -> Generator[dict[str, Union[T, Any]], None, None]:
         """Get structured output from the model.
 
         Args:
             output_model(Type[BaseModel]): The output model to use for the agent.
             prompt(Messages): The prompt messages to use for the agent.
-            callback_handler(Optional[Callable]): Optional callback handler for processing events. Defaults to None.
+
+        Yields:
+            Model events with the last being the structured output.
         """
         response: ParsedChatCompletion = self.client.beta.chat.completions.parse(  # type: ignore
             model=self.get_config()["model_id"],
@@ -159,6 +161,6 @@ def structured_output(
                 break
 
         if parsed:
-            return parsed
+            yield {"output": parsed}
         else:
             raise ValueError("No valid tool use or tool use input was found in the OpenAI response.")

src/strands/types/models/model.py

@@ -2,7 +2,7 @@
 
 import abc
 import logging
-from typing import Any, Callable, Iterable, Optional, Type, TypeVar
+from typing import Any, Generator, Iterable, Optional, Type, TypeVar, Union
 
 from pydantic import BaseModel
 
@@ -45,17 +45,16 @@ def get_config(self) -> Any:
     @abc.abstractmethod
     # pragma: no cover
     def structured_output(
-        self, output_model: Type[T], prompt: Messages, callback_handler: Optional[Callable] = None
-    ) -> T:
+        self, output_model: Type[T], prompt: Messages
+    ) -> Generator[dict[str, Union[T, Any]], None, None]:
         """Get structured output from the model.
 
         Args:
             output_model(Type[BaseModel]): The output model to use for the agent.
             prompt(Messages): The prompt messages to use for the agent.
-            callback_handler(Optional[Callable]): Optional callback handler for processing events. Defaults to None.
 
-        Returns:
-            The structured output as a serialized instance of the output model.
+        Yields:
+            Model events with the last being the structured output.
 
         Raises:
             ValidationException: The response format from the model does not match the output_model

src/strands/types/models/openai.py

@@ -11,7 +11,7 @@
 import json
 import logging
 import mimetypes
-from typing import Any, Callable, Optional, Type, TypeVar, cast
+from typing import Any, Generator, Optional, Type, TypeVar, Union, cast
 
 from pydantic import BaseModel
 from typing_extensions import override
@@ -295,13 +295,15 @@ def format_chunk(self, event: dict[str, Any]) -> StreamEvent:
 
     @override
     def structured_output(
-        self, output_model: Type[T], prompt: Messages, callback_handler: Optional[Callable] = None
-    ) -> T:
+        self, output_model: Type[T], prompt: Messages
+    ) -> Generator[dict[str, Union[T, Any]], None, None]:
         """Get structured output from the model.
 
         Args:
             output_model(Type[BaseModel]): The output model to use for the agent.
             prompt(Messages): The prompt to use for the agent.
-            callback_handler(Optional[Callable]): Optional callback handler for processing events. Defaults to None.
+
+        Yields:
+            Model events with the last being the structured output.
         """
-        return output_model()
+        yield {"output": output_model()}

tests/strands/agent/test_agent.py

@@ -898,17 +898,15 @@ class User(BaseModel):
 def test_agent_method_structured_output(agent):
     # Mock the structured_output method on the model
     expected_user = User(name="Jane Doe", age=30, email="jane@doe.com")
-    agent.model.structured_output = unittest.mock.Mock(return_value=expected_user)
+    agent.model.structured_output = unittest.mock.Mock(return_value=[{"output": expected_user}])
 
     prompt = "Jane Doe is 30 years old and her email is jane@doe.com"
 
     result = agent.structured_output(User, prompt)
     assert result == expected_user
 
     # Verify the model's structured_output was called with correct arguments
-    agent.model.structured_output.assert_called_once_with(
-        User, [{"role": "user", "content": [{"text": prompt}]}], agent.callback_handler
-    )
+    agent.model.structured_output.assert_called_once_with(User, [{"role": "user", "content": [{"text": prompt}]}])
 
 
 @pytest.mark.asyncio