Improve OpenAI error handling

src/strands/models/openai.py

@@ -15,6 +15,7 @@
 from typing_extensions import Unpack, override
 
 from ..types.content import ContentBlock, Messages
+from ..types.exceptions import ContextWindowOverflowException, ModelThrottledException
 from ..types.streaming import StreamEvent
 from ..types.tools import ToolChoice, ToolResult, ToolSpec, ToolUse
 from ._validation import validate_config_keys
@@ -372,6 +373,10 @@ async def stream(
 
         Yields:
             Formatted message chunks from the model.
+
+        Raises:
+            ContextWindowOverflowException: If the input exceeds the model's context window.
+            ModelThrottledException: If the request is throttled by OpenAI (rate limits).
         """
         logger.debug("formatting request")
         request = self.format_request(messages, tool_specs, system_prompt, tool_choice)
@@ -383,7 +388,20 @@ async def stream(
         # client. The asyncio event loop does not allow connections to be shared. For more details, please refer to
         # https://github.com/encode/httpx/discussions/2959.
         async with openai.AsyncOpenAI(**self.client_args) as client:
-            response = await client.chat.completions.create(**request)
+            try:
+                response = await client.chat.completions.create(**request)
+            except openai.BadRequestError as e:
+                # Check if this is a context length exceeded error
+                if hasattr(e, "code") and e.code == "context_length_exceeded":
+                    logger.warning("OpenAI threw context window overflow error")
+                    raise ContextWindowOverflowException(str(e)) from e
+                # Re-raise other BadRequestError exceptions
+                raise
+            except openai.RateLimitError as e:
+                # All rate limit errors should be treated as throttling, not context overflow
+                # Rate limits (including TPM) require waiting/retrying, not context reduction
+                logger.warning("OpenAI threw rate limit error")
+                raise ModelThrottledException(str(e)) from e
 
             logger.debug("got response from model")
             yield self.format_chunk({"chunk_type": "message_start"})
@@ -452,16 +470,33 @@ async def structured_output(
 
         Yields:
             Model events with the last being the structured output.
+
+        Raises:
+            ContextWindowOverflowException: If the input exceeds the model's context window.
+            ModelThrottledException: If the request is throttled by OpenAI (rate limits).
         """
         # We initialize an OpenAI context on every request so as to avoid connection sharing in the underlying httpx
         # client. The asyncio event loop does not allow connections to be shared. For more details, please refer to
         # https://github.com/encode/httpx/discussions/2959.
         async with openai.AsyncOpenAI(**self.client_args) as client:
-            response: ParsedChatCompletion = await client.beta.chat.completions.parse(
-                model=self.get_config()["model_id"],
-                messages=self.format_request(prompt, system_prompt=system_prompt)["messages"],
-                response_format=output_model,
-            )
+            try:
+                response: ParsedChatCompletion = await client.beta.chat.completions.parse(
+                    model=self.get_config()["model_id"],
+                    messages=self.format_request(prompt, system_prompt=system_prompt)["messages"],
+                    response_format=output_model,
+                )
+            except openai.BadRequestError as e:
+                # Check if this is a context length exceeded error
+                if hasattr(e, "code") and e.code == "context_length_exceeded":
+                    logger.warning("OpenAI threw context window overflow error")
+                    raise ContextWindowOverflowException(str(e)) from e
+                # Re-raise other BadRequestError exceptions
+                raise
+            except openai.RateLimitError as e:
+                # All rate limit errors should be treated as throttling, not context overflow
+                # Rate limits (including TPM) require waiting/retrying, not context reduction
+                logger.warning("OpenAI threw rate limit error")
+                raise ModelThrottledException(str(e)) from e
 
         parsed: T | None = None
         # Find the first choice with tool_calls

tests/strands/models/test_openai.py

@@ -1,10 +1,12 @@
 import unittest.mock
 
+import openai
 import pydantic
 import pytest
 
 import strands
 from strands.models.openai import OpenAIModel
+from strands.types.exceptions import ContextWindowOverflowException, ModelThrottledException
 
 
 @pytest.fixture
@@ -752,3 +754,149 @@ def test_tool_choice_none_no_warning(model, messages, captured_warnings):
     model.format_request(messages, tool_choice=None)
 
     assert len(captured_warnings) == 0
+
+
+@pytest.mark.asyncio
+async def test_stream_context_overflow_exception(openai_client, model, messages):
+    """Test that OpenAI context overflow errors are properly converted to ContextWindowOverflowException."""
+    # Create a mock OpenAI BadRequestError with context_length_exceeded code
+    mock_error = openai.BadRequestError(
+        message="This model's maximum context length is 4096 tokens. However, your messages resulted in 5000 tokens.",
+        response=unittest.mock.MagicMock(),
+        body={"error": {"code": "context_length_exceeded"}},
+    )
+    mock_error.code = "context_length_exceeded"
+
+    # Configure the mock client to raise the context overflow error
+    openai_client.chat.completions.create.side_effect = mock_error
+
+    # Test that the stream method converts the error properly
+    with pytest.raises(ContextWindowOverflowException) as exc_info:
+        async for _ in model.stream(messages):
+            pass
+
+    # Verify the exception message contains the original error
+    assert "maximum context length" in str(exc_info.value)
+    assert exc_info.value.__cause__ == mock_error
+
+
+@pytest.mark.asyncio
+async def test_stream_other_bad_request_errors_passthrough(openai_client, model, messages):
+    """Test that other BadRequestError exceptions are not converted to ContextWindowOverflowException."""
+    # Create a mock OpenAI BadRequestError with a different error code
+    mock_error = openai.BadRequestError(
+        message="Invalid parameter value",
+        response=unittest.mock.MagicMock(),
+        body={"error": {"code": "invalid_parameter"}},
+    )
+    mock_error.code = "invalid_parameter"
+
+    # Configure the mock client to raise the non-context error
+    openai_client.chat.completions.create.side_effect = mock_error
+
+    # Test that other BadRequestError exceptions pass through unchanged
+    with pytest.raises(openai.BadRequestError) as exc_info:
+        async for _ in model.stream(messages):
+            pass
+
+    # Verify the original exception is raised, not ContextWindowOverflowException
+    assert exc_info.value == mock_error
+
+
+@pytest.mark.asyncio
+async def test_structured_output_context_overflow_exception(openai_client, model, messages, test_output_model_cls):
+    """Test that structured output also handles context overflow properly."""
+    # Create a mock OpenAI BadRequestError with context_length_exceeded code
+    mock_error = openai.BadRequestError(
+        message="This model's maximum context length is 4096 tokens. However, your messages resulted in 5000 tokens.",
+        response=unittest.mock.MagicMock(),
+        body={"error": {"code": "context_length_exceeded"}},
+    )
+    mock_error.code = "context_length_exceeded"
+
+    # Configure the mock client to raise the context overflow error
+    openai_client.beta.chat.completions.parse.side_effect = mock_error
+
+    # Test that the structured_output method converts the error properly
+    with pytest.raises(ContextWindowOverflowException) as exc_info:
+        async for _ in model.structured_output(test_output_model_cls, messages):
+            pass
+
+    # Verify the exception message contains the original error
+    assert "maximum context length" in str(exc_info.value)
+    assert exc_info.value.__cause__ == mock_error
+
+
+@pytest.mark.asyncio
+async def test_stream_rate_limit_as_throttle(openai_client, model, messages):
+    """Test that all rate limit errors are converted to ModelThrottledException."""
+
+    # Create a mock OpenAI RateLimitError (any type of rate limit)
+    mock_error = openai.RateLimitError(
+        message="Request too large for gpt-4o on tokens per min (TPM): Limit 30000, Requested 117505.",
+        response=unittest.mock.MagicMock(),
+        body={"error": {"code": "rate_limit_exceeded"}},
+    )
+    mock_error.code = "rate_limit_exceeded"
+
+    # Configure the mock client to raise the rate limit error
+    openai_client.chat.completions.create.side_effect = mock_error
+
+    # Test that the stream method converts the error properly
+    with pytest.raises(ModelThrottledException) as exc_info:
+        async for _ in model.stream(messages):
+            pass
+
+    # Verify the exception message contains the original error
+    assert "tokens per min" in str(exc_info.value)
+    assert exc_info.value.__cause__ == mock_error
+
+
+@pytest.mark.asyncio
+async def test_stream_request_rate_limit_as_throttle(openai_client, model, messages):
+    """Test that request-based rate limit errors are converted to ModelThrottledException."""
+
+    # Create a mock OpenAI RateLimitError for request-based rate limiting
+    mock_error = openai.RateLimitError(
+        message="Rate limit reached for requests per minute.",
+        response=unittest.mock.MagicMock(),
+        body={"error": {"code": "rate_limit_exceeded"}},
+    )
+    mock_error.code = "rate_limit_exceeded"
+
+    # Configure the mock client to raise the request rate limit error
+    openai_client.chat.completions.create.side_effect = mock_error
+
+    # Test that the stream method converts the error properly
+    with pytest.raises(ModelThrottledException) as exc_info:
+        async for _ in model.stream(messages):
+            pass
+
+    # Verify the exception message contains the original error
+    assert "Rate limit reached" in str(exc_info.value)
+    assert exc_info.value.__cause__ == mock_error
+
+
+@pytest.mark.asyncio
+async def test_structured_output_rate_limit_as_throttle(openai_client, model, messages, test_output_model_cls):
+    """Test that structured output handles rate limit errors properly."""
+
+    # Create a mock OpenAI RateLimitError
+    mock_error = openai.RateLimitError(
+        message="Request too large for gpt-4o on tokens per min (TPM): Limit 30000, Requested 117505.",
+        response=unittest.mock.MagicMock(),
+        body={"error": {"code": "rate_limit_exceeded"}},
+    )
+    mock_error.code = "rate_limit_exceeded"
+
+    # Configure the mock client to raise the rate limit error
+    openai_client.beta.chat.completions.parse.side_effect = mock_error
+
+    # Test that the structured_output method converts the error properly
+    with pytest.raises(ModelThrottledException) as exc_info:
+        async for _ in model.structured_output(test_output_model_cls, messages):
+            pass
+
+    # Verify the exception message contains the original error
+    assert "tokens per min" in str(exc_info.value)
+    assert exc_info.value.__cause__ == mock_error

tests_integ/models/test_model_openai.py

@@ -1,11 +1,13 @@
 import os
+import unittest.mock
 
 import pydantic
 import pytest
 
 import strands
 from strands import Agent, tool
 from strands.models.openai import OpenAIModel
+from strands.types.exceptions import ContextWindowOverflowException, ModelThrottledException
 from tests_integ.models import providers
 
 # these tests only run if we have the openai api key
@@ -167,3 +169,55 @@ def tool_with_image_return():
     # 'user', but this message with role 'tool' contains an image URL."
     # See https://github.com/strands-agents/sdk-python/issues/320 for additional details
     agent("Run the the tool and analyze the image")
+
+
+def test_context_window_overflow_integration():
+    """Integration test for context window overflow with OpenAI.
+
+    This test verifies that when a request exceeds the model's context window,
+    the OpenAI model properly raises a ContextWindowOverflowException.
+    """
+    # Use gpt-4o-mini which has a smaller context window to make this test more reliable
+    mini_model = OpenAIModel(
+        model_id="gpt-4o-mini-2024-07-18",
+        client_args={
+            "api_key": os.getenv("OPENAI_API_KEY"),
+        },
+    )
+
+    agent = Agent(model=mini_model)
+
+    # Create a very long text that should exceed context window
+    # This text is designed to be long enough to exceed context but not hit token rate limits
+    long_text = (
+        "This text is longer than context window, but short enough to not get caught in token rate limit. " * 6800
+    )
+
+    # This should raise ContextWindowOverflowException which gets handled by conversation manager
+    # The agent should attempt to reduce context and retry
+    with pytest.raises(ContextWindowOverflowException):
+        agent(long_text)
+
+
+def test_rate_limit_throttling_integration_no_retries(model):
+    """Integration test for rate limit handling with retries disabled.
+
+    This test verifies that when a request exceeds OpenAI's rate limits,
+    the model properly raises a ModelThrottledException. We disable retries
+    to avoid waiting for the exponential backoff during testing.
+    """
+    # Patch the event loop constants to disable retries for this test
+    with unittest.mock.patch("strands.event_loop.event_loop.MAX_ATTEMPTS", 1):
+        agent = Agent(model=model)
+
+        # Create a message that's very long to trigger token-per-minute rate limits
+        # This should be large enough to exceed TPM limits immediately
+        very_long_text = "Really long text " * 20000
+
+        # This should raise ModelThrottledException without retries
+        with pytest.raises(ModelThrottledException) as exc_info:
+            agent(very_long_text)
+
+        # Verify it's a rate limit error
+        error_message = str(exc_info.value).lower()
+        assert "rate limit" in error_message or "tokens per min" in error_message