Enable non-strict output types

examples/basic/non_strict_output_type.py

@@ -0,0 +1,81 @@
+import asyncio
+import json
+from dataclasses import dataclass
+from typing import Any
+
+from agents import Agent, AgentOutputSchema, AgentOutputSchemaBase, Runner
+
+"""This example demonstrates how to use an output type that is not in strict mode. Strict mode
+allows us to guarantee valid JSON output, but some schemas are not strict-compatible.
+
+In this example, we define an output type that is not strict-compatible, and then we run the
+agent with strict_json_schema=False.
+
+We also demonstrate a custom output type.
+
+To understand which schemas are strict-compatible, see:
+https://platform.openai.com/docs/guides/structured-outputs?api-mode=responses#supported-schemas
+"""
+
+
+@dataclass
+class OutputType:
+    jokes: dict[int, str]
+    """A list of jokes, indexed by joke number."""
+
+
+class CustomOutputSchema(AgentOutputSchemaBase):
+    """A demonstration of a custom output schema."""
+
+    def is_plain_text(self) -> bool:
+        return False
+
+    def name(self) -> str:
+        return "CustomOutputSchema"
+
+    def json_schema(self) -> dict[str, Any]:
+        return {
+            "type": "object",
+            "properties": {"jokes": {"type": "object", "properties": {"joke": {"type": "string"}}}},
+        }
+
+    def is_strict_json_schema(self) -> bool:
+        return False
+
+    def validate_json(self, json_str: str) -> Any:
+        json_obj = json.loads(json_str)
+        # Just for demonstration, we'll return a list.
+        return list(json_obj["jokes"].values())
+
+
+async def main():
+    agent = Agent(
+        name="Assistant",
+        instructions="You are a helpful assistant.",
+        output_type=OutputType,
+    )
+
+    input = "Tell me 3 short jokes."
+
+    # First, let's try with a strict output type. This should raise an exception.
+    try:
+        result = await Runner.run(agent, input)
+        raise AssertionError("Should have raised an exception")
+    except Exception as e:
+        print(f"Error (expected): {e}")
+
+    # Now let's try again with a non-strict output type. This should work.
+    # In some cases, it will raise an error - the schema isn't strict, so the model may
+    # produce an invalid JSON object.
+    agent.output_type = AgentOutputSchema(OutputType, strict_json_schema=False)
+    result = await Runner.run(agent, input)
+    print(result.final_output)
+
+    # Finally, let's try a custom output type.
+    agent.output_type = CustomOutputSchema()
+    result = await Runner.run(agent, input)
+    print(result.final_output)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

src/agents/__init__.py

@@ -6,7 +6,7 @@
 
 from . import _config
 from .agent import Agent, ToolsToFinalOutputFunction, ToolsToFinalOutputResult
-from .agent_output import AgentOutputSchema
+from .agent_output import AgentOutputSchema, AgentOutputSchemaBase
 from .computer import AsyncComputer, Button, Computer, Environment
 from .exceptions import (
     AgentsException,
@@ -158,6 +158,7 @@ def enable_verbose_stdout_logging():
     "OpenAIProvider",
     "OpenAIResponsesModel",
     "AgentOutputSchema",
+    "AgentOutputSchemaBase",
     "Computer",
     "AsyncComputer",
     "Environment",

src/agents/_run_impl.py

@@ -29,7 +29,7 @@
 from openai.types.responses.response_reasoning_item import ResponseReasoningItem
 
 from .agent import Agent, ToolsToFinalOutputResult
-from .agent_output import AgentOutputSchema
+from .agent_output import AgentOutputSchemaBase
 from .computer import AsyncComputer, Computer
 from .exceptions import AgentsException, ModelBehaviorError, UserError
 from .guardrail import InputGuardrail, InputGuardrailResult, OutputGuardrail, OutputGuardrailResult
@@ -195,7 +195,7 @@ async def execute_tools_and_side_effects(
         pre_step_items: list[RunItem],
         new_response: ModelResponse,
         processed_response: ProcessedResponse,
-        output_schema: AgentOutputSchema | None,
+        output_schema: AgentOutputSchemaBase | None,
         hooks: RunHooks[TContext],
         context_wrapper: RunContextWrapper[TContext],
         run_config: RunConfig,
@@ -335,7 +335,7 @@ def process_model_response(
         agent: Agent[Any],
         all_tools: list[Tool],
         response: ModelResponse,
-        output_schema: AgentOutputSchema | None,
+        output_schema: AgentOutputSchemaBase | None,
         handoffs: list[Handoff],
     ) -> ProcessedResponse:
         items: list[RunItem] = []

src/agents/agent.py

@@ -8,6 +8,7 @@
 
 from typing_extensions import NotRequired, TypeAlias, TypedDict
 
+from .agent_output import AgentOutputSchemaBase
 from .guardrail import InputGuardrail, OutputGuardrail
 from .handoffs import Handoff
 from .items import ItemHelpers
@@ -141,8 +142,14 @@ class Agent(Generic[TContext]):
     Runs only if the agent produces a final output.
     """
 
-    output_type: type[Any] | None = None
-    """The type of the output object. If not provided, the output will be `str`."""
+    output_type: type[Any] | AgentOutputSchemaBase | None = None
+    """The type of the output object. If not provided, the output will be `str`. In most cases,
+    you should pass a regular Python type (e.g. a dataclass, Pydantic model, TypedDict, etc).
+    You can customize this in two ways:
+    1. If you want non-strict schemas, pass `AgentOutputSchema(MyClass, strict_json_schema=False)`.
+    2. If you want to use a custom JSON schema (i.e. without using the SDK's automatic schema)
+       creation, subclass and pass an `AgentOutputSchemaBase` subclass.
+    """
 
     hooks: AgentHooks[TContext] | None = None
     """A class that receives callbacks on various lifecycle events for this agent.

src/agents/agent_output.py

@@ -1,3 +1,4 @@
+import abc
 from dataclasses import dataclass
 from typing import Any
 
@@ -12,8 +13,46 @@
 _WRAPPER_DICT_KEY = "response"
 
 
+class AgentOutputSchemaBase(abc.ABC):
+    """An object that captures the JSON schema of the output, as well as validating/parsing JSON
+    produced by the LLM into the output type.
+    """
+
+    @abc.abstractmethod
+    def is_plain_text(self) -> bool:
+        """Whether the output type is plain text (versus a JSON object)."""
+        pass
+
+    @abc.abstractmethod
+    def name(self) -> str:
+        """The name of the output type."""
+        pass
+
+    @abc.abstractmethod
+    def json_schema(self) -> dict[str, Any]:
+        """Returns the JSON schema of the output. Will only be called if the output type is not
+        plain text.
+        """
+        pass
+
+    @abc.abstractmethod
+    def is_strict_json_schema(self) -> bool:
+        """Whether the JSON schema is in strict mode. Strict mode constrains the JSON schema
+        features, but guarantees valis JSON. See here for details:
+        https://platform.openai.com/docs/guides/structured-outputs#supported-schemas
+        """
+        pass
+
+    @abc.abstractmethod
+    def validate_json(self, json_str: str) -> Any:
+        """Validate a JSON string against the output type. You must return the validated object,
+        or raise a `ModelBehaviorError` if the JSON is invalid.
+        """
+        pass
+
+
 @dataclass(init=False)
-class AgentOutputSchema:
+class AgentOutputSchema(AgentOutputSchemaBase):
     """An object that captures the JSON schema of the output, as well as validating/parsing JSON
     produced by the LLM into the output type.
     """
@@ -32,7 +71,7 @@ class AgentOutputSchema:
     _output_schema: dict[str, Any]
     """The JSON schema of the output."""
 
-    strict_json_schema: bool
+    _strict_json_schema: bool
     """Whether the JSON schema is in strict mode. We **strongly** recommend setting this to True,
     as it increases the likelihood of correct JSON input.
     """
@@ -45,7 +84,7 @@ def __init__(self, output_type: type[Any], strict_json_schema: bool = True):
                 setting this to True, as it increases the likelihood of correct JSON input.
         """
         self.output_type = output_type
-        self.strict_json_schema = strict_json_schema
+        self._strict_json_schema = strict_json_schema
 
         if output_type is None or output_type is str:
             self._is_wrapped = False
@@ -70,24 +109,35 @@ def __init__(self, output_type: type[Any], strict_json_schema: bool = True):
             self._type_adapter = TypeAdapter(output_type)
             self._output_schema = self._type_adapter.json_schema()
 
-        if self.strict_json_schema:
-            self._output_schema = ensure_strict_json_schema(self._output_schema)
+        if self._strict_json_schema:
+            try:
+                self._output_schema = ensure_strict_json_schema(self._output_schema)
+            except UserError as e:
+                raise UserError(
+                    "Strict JSON schema is enabled, but the output type is not valid. "
+                    "Either make the output type strict, or pass output_schema_strict=False to "
+                    "your Agent()"
+                ) from e
 
     def is_plain_text(self) -> bool:
         """Whether the output type is plain text (versus a JSON object)."""
         return self.output_type is None or self.output_type is str
 
+    def is_strict_json_schema(self) -> bool:
+        """Whether the JSON schema is in strict mode."""
+        return self._strict_json_schema
+
     def json_schema(self) -> dict[str, Any]:
         """The JSON schema of the output type."""
         if self.is_plain_text():
             raise UserError("Output type is plain text, so no JSON schema is available")
         return self._output_schema
 
-    def validate_json(self, json_str: str, partial: bool = False) -> Any:
+    def validate_json(self, json_str: str) -> Any:
         """Validate a JSON string against the output type. Returns the validated object, or raises
         a `ModelBehaviorError` if the JSON is invalid.
         """
-        validated = _json.validate_json(json_str, self._type_adapter, partial)
+        validated = _json.validate_json(json_str, self._type_adapter, partial=False)
         if self._is_wrapped:
             if not isinstance(validated, dict):
                 _error_tracing.attach_error_to_current_span(
@@ -113,7 +163,7 @@ def validate_json(self, json_str: str, partial: bool = False) -> Any:
             return validated[_WRAPPER_DICT_KEY]
         return validated
 
-    def output_type_name(self) -> str:
+    def name(self) -> str:
         """The name of the output type."""
         return _type_to_str(self.output_type)
 

src/agents/extensions/models/litellm_model.py

@@ -29,7 +29,7 @@
 from openai.types.responses import Response
 
 from ... import _debug
-from ...agent_output import AgentOutputSchema
+from ...agent_output import AgentOutputSchemaBase
 from ...handoffs import Handoff
 from ...items import ModelResponse, TResponseInputItem, TResponseStreamEvent
 from ...logger import logger
@@ -68,7 +68,7 @@ async def get_response(
         input: str | list[TResponseInputItem],
         model_settings: ModelSettings,
         tools: list[Tool],
-        output_schema: AgentOutputSchema | None,
+        output_schema: AgentOutputSchemaBase | None,
         handoffs: list[Handoff],
         tracing: ModelTracing,
         previous_response_id: str | None,
@@ -139,7 +139,7 @@ async def stream_response(
         input: str | list[TResponseInputItem],
         model_settings: ModelSettings,
         tools: list[Tool],
-        output_schema: AgentOutputSchema | None,
+        output_schema: AgentOutputSchemaBase | None,
         handoffs: list[Handoff],
         tracing: ModelTracing,
         *,
@@ -186,7 +186,7 @@ async def _fetch_response(
         input: str | list[TResponseInputItem],
         model_settings: ModelSettings,
         tools: list[Tool],
-        output_schema: AgentOutputSchema | None,
+        output_schema: AgentOutputSchemaBase | None,
         handoffs: list[Handoff],
         span: Span[GenerationSpanData],
         tracing: ModelTracing,
@@ -200,7 +200,7 @@ async def _fetch_response(
         input: str | list[TResponseInputItem],
         model_settings: ModelSettings,
         tools: list[Tool],
-        output_schema: AgentOutputSchema | None,
+        output_schema: AgentOutputSchemaBase | None,
         handoffs: list[Handoff],
         span: Span[GenerationSpanData],
         tracing: ModelTracing,
@@ -213,7 +213,7 @@ async def _fetch_response(
         input: str | list[TResponseInputItem],
         model_settings: ModelSettings,
         tools: list[Tool],
-        output_schema: AgentOutputSchema | None,
+        output_schema: AgentOutputSchemaBase | None,
         handoffs: list[Handoff],
         span: Span[GenerationSpanData],
         tracing: ModelTracing,

src/agents/models/chatcmpl_converter.py

@@ -36,7 +36,7 @@
 )
 from openai.types.responses.response_input_param import FunctionCallOutput, ItemReference, Message
 
-from ..agent_output import AgentOutputSchema
+from ..agent_output import AgentOutputSchemaBase
 from ..exceptions import AgentsException, UserError
 from ..handoffs import Handoff
 from ..items import TResponseInputItem, TResponseOutputItem
@@ -67,7 +67,7 @@ def convert_tool_choice(
 
     @classmethod
     def convert_response_format(
-        cls, final_output_schema: AgentOutputSchema | None
+        cls, final_output_schema: AgentOutputSchemaBase | None
     ) -> ResponseFormat | NotGiven:
         if not final_output_schema or final_output_schema.is_plain_text():
             return NOT_GIVEN
@@ -76,7 +76,7 @@ def convert_response_format(
             "type": "json_schema",
             "json_schema": {
                 "name": "final_output",
-                "strict": final_output_schema.strict_json_schema,
+                "strict": final_output_schema.is_strict_json_schema(),
                 "schema": final_output_schema.json_schema(),
             },
         }

src/agents/models/interface.py

@@ -5,7 +5,7 @@
 from collections.abc import AsyncIterator
 from typing import TYPE_CHECKING
 
-from ..agent_output import AgentOutputSchema
+from ..agent_output import AgentOutputSchemaBase
 from ..handoffs import Handoff
 from ..items import ModelResponse, TResponseInputItem, TResponseStreamEvent
 from ..tool import Tool
@@ -41,7 +41,7 @@ async def get_response(
         input: str | list[TResponseInputItem],
         model_settings: ModelSettings,
         tools: list[Tool],
-        output_schema: AgentOutputSchema | None,
+        output_schema: AgentOutputSchemaBase | None,
         handoffs: list[Handoff],
         tracing: ModelTracing,
         *,
@@ -72,7 +72,7 @@ def stream_response(
         input: str | list[TResponseInputItem],
         model_settings: ModelSettings,
         tools: list[Tool],
-        output_schema: AgentOutputSchema | None,
+        output_schema: AgentOutputSchemaBase | None,
         handoffs: list[Handoff],
         tracing: ModelTracing,
         *,