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feat(weave): OpenAI Agents SDK integration #3882

Merged
merged 16 commits into from
Mar 20, 2025
Merged

feat(weave): OpenAI Agents SDK integration #3882

merged 16 commits into from
Mar 20, 2025

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andrewtruong
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@andrewtruong andrewtruong commented Mar 17, 2025 •
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https://wandb.atlassian.net/browse/WB-23857

Adds basic support for the OpenAI Agents SDK

TODO:

  1. Responses API should be wrapped in OAI patcher and then consumed here
  2. Consider emulating errors to get the UI to show failed or problematic steps
  3. Consider moving more data out into metadata
    image

Summary by CodeRabbit

Summary by CodeRabbit

  • New Features
    • Introduced an integration that captures and logs detailed agent interactions, including various execution stages.
    • Enhanced configuration settings now automatically manage the deployment and cleanup of the integration for better trace management and diagnostic visibility.
    • Added support for additional configuration related to OpenAI agents in the autopatching system.
    • Expanded testing capabilities to include OpenAI agents.
    • Added a new optional dependency for OpenAI agents to enhance project functionalities.
    • Introduced new test cases to validate the functionality of OpenAI agents, including homework-related queries.

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coderabbitai bot commented Mar 17, 2025 •
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Walkthrough

The pull request introduces a new integration module for OpenAI Agents in Weave. It adds a tracing processor to capture and log various spans from agent execution, along with a patcher class to manage its lifecycle. Additionally, the autopatch system in Weave is extended to include configuration and handling for this new OpenAI Agents integration, ensuring that patching and unpatching operate correctly. Furthermore, optional dependencies are updated to include the openai-agents package.

Changes

File(s) Summary of Changes
weave/integrations/openai_agents/openai_agents.py Added a new module with WeaveTracingProcessor for logging trace and span data from OpenAI Agents. Introduced the OpenAIAgentsPatcher class and get_openai_agents_patcher function for managing processor lifecycle, with error handling, shutdown, and flushing routines.
weave/trace/autopatch.py Extended AutopatchSettings by adding an openai_agents field with default integration settings. Updated autopatch() and reset_autopatch() functions to include patching and unpatching logic for OpenAI Agents integration.
noxfile.py Added "openai_agents" to the @nox.parametrize decorator in the tests function to include tests related to OpenAI Agents.
pyproject.toml Introduced a new optional dependency openai_agents = ["openai-agents>=0.0.4"] to specify the required package for OpenAI Agents integration.
tests/integrations/openai_agents/cassettes/openai_agents_test/test_openai_agents_quickstart.yaml Created a new YAML file for testing OpenAI API interactions, including requests and expected responses for generating responses.
tests/integrations/openai_agents/cassettes/openai_agents_test/test_openai_agents_quickstart_homework.yaml Added a YAML file for testing OpenAI agent functionality with homework-related queries, simulating user-agent communication and validating response accuracy.
tests/integrations/openai_agents/openai_agents_test.py Introduced a new test file with functions for testing OpenAI agents, including synchronous and asynchronous tests for various agent interactions, validating outputs and behavior.

Suggested Reviewers

  • jamie-rasmussen
  • tssweeney

Poem

I'm just a rabbit in a field of code,
Hopping through changes on a bright new road.
Logging spans and traces with a joyful beat,
Making OpenAI Agents integration oh so neat.
With whiskers twitching at each bug set free,
I celebrate these changes, happy as can be! 🐇✨

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Actionable comments posted: 0

🧹 Nitpick comments (3)
weave/integrations/openai_agents/openai_agents.py (3)

53-456: Overall design of WeaveTracingProcessor is thorough.

  • The processor systematically handles trace creation, span capture, and finalization for OpenAI Agent interactions.
  • Methods like _log_data properly discriminate among span data types to produce structured logs.
  • Internal dictionaries (_trace_data, _trace_calls, _span_calls) effectively track call hierarchies, though be mindful of potential threading scenarios if concurrency arises.
  • Consider logging the exception (if any) when referencing external span attributes like span.data_usage.usage, to gracefully handle unexpected library changes or missing attributes.

458-490: OpenAIAgentsPatcher implementation follows the established patcher model.

  • Installs a custom WeaveTracingProcessor and marks itself as patched.
  • Suggest logging the exception details in attempt_patch() for easier debugging.

492-517: Global patcher instance usage.

  • The lazy initialization with a global _openai_agents_patcher is straightforward but be cautious about race conditions if multiple threads might invoke get_openai_agents_patcher concurrently.
  • Overall, this function aligns with patterns from other integrations, returning a NoOpPatcher when disabled.
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📥 Commits

Reviewing files that changed from the base of the PR and between 52e7b82 and b188b06.

📒 Files selected for processing (2)
  • weave/integrations/openai_agents/openai_agents.py (1 hunks)
  • weave/trace/autopatch.py (5 hunks)
🧰 Additional context used
📓 Path-based instructions (1)
`**/*.py`: Focus on pythonic code patterns. Check for proper...

**/*.py: Focus on pythonic code patterns.
Check for proper exception handling.
Verify type hints usage where applicable.
Look for potential performance improvements.
Don't comment on formatting if black/isort is configured.
Check for proper dependency injection patterns.
Verify proper async handling if applicable.

  • weave/trace/autopatch.py
  • weave/integrations/openai_agents/openai_agents.py
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🔇 Additional comments (7)
weave/trace/autopatch.py (5)

50-50: Good addition to AutopatchSettings.

This new field aligns with the existing pattern of integration settings.

83-83: Import statement looks appropriate.

Importing get_openai_agents_patcher is consistent with the other integrations and follows the established pattern for autopatching.

100-100: Consistent call to the patcher.

Invoking attempt_patch() mirrors the approach used for other integrations and correctly respects settings.openai_agents.enabled.

128-128: Reset import is consistent.

Re-importing the patcher for reset aligns well with the pattern established in this function.

145-145: Undo patch logic adheres to the existing pattern.

Though OpenAI Agents lacks a de-registration mechanism, calling undo_patch() keeps the interface consistent.

weave/integrations/openai_agents/openai_agents.py (2)

1-42: Module-level definitions look solid.

The module docstring, _call_type, and _call_name functions are concise and clear. They integrate well with the broader weaving/tracing paradigm to classify and name spans.

45-51: TypedDict structure is consistent.

WeaveDataFormat captures inputs, outputs, metadata, metrics, and errors in a well-defined dictionary schema.

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Actionable comments posted: 2

🧹 Nitpick comments (2)
weave/integrations/openai_agents/openai_agents.py (2)

456-487: Log exception details in attempt_patch method.

While the exception handling in attempt_patch prevents crashes during installation, it silently swallows the exception details, which could make debugging difficult. Consider logging the exception.

     def attempt_patch(self) -> bool:
         """Install a WeaveTracingProcessor in the OpenAI Agents tracing system."""
         if self.patched:
             return True

         try:
             self.processor = WeaveTracingProcessor()
             add_trace_processor(self.processor)
             self.patched = True
         except Exception as e:
+            import logging
+            logging.getLogger(__name__).warning(f"Failed to patch OpenAI Agents: {e}")
             self.processor = None
             return False
         else:
             return True

484-487: Document the potential resource leak in undo_patch.

The comment mentions that OpenAI Agents doesn't have a way to de-register a processor, but it's important to document this limitation more explicitly and potentially suggest alternatives.

     def undo_patch(self) -> bool:
-        # OpenAI Agents doesn't have a way to de-register a processor yet...
+        # OpenAI Agents doesn't have a way to de-register a processor yet,
+        # which may lead to resource leaks if the application is started and stopped
+        # multiple times. This is a limitation of the OpenAI Agents SDK.
+        # TODO: Update this when OpenAI Agents supports de-registering processors.
+        if self.processor:
+            self.processor = None
+            self.patched = False
         return True
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`**/*.py`: Focus on pythonic code patterns. Check for proper...

**/*.py: Focus on pythonic code patterns.
Check for proper exception handling.
Verify type hints usage where applicable.
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Don't comment on formatting if black/isort is configured.
Check for proper dependency injection patterns.
Verify proper async handling if applicable.

  • weave/integrations/openai_agents/openai_agents.py
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🔇 Additional comments (12)
weave/integrations/openai_agents/openai_agents.py (12)

8-22: Import organization looks well structured.

The imports are clearly organized with standard library imports first, followed by external dependencies, and then internal imports. The typing imports and explicit imports from the agents library provide good clarity for the module dependencies.

26-42: Helper functions are well-implemented for determining span types and names.

These utility functions correctly handle the different types of spans from the OpenAI Agents SDK. The name resolution function properly cascades through different conditions to determine the appropriate name.

45-51: Good use of TypedDict for structured data.

Using TypedDict for the WeaveDataFormat creates a clear contract for the data structure and improves type checking. The structure appropriately separates inputs, outputs, metadata, metrics, and error information.

94-115: Guard clauses are used effectively.

The early returns in on_trace_end are well-implemented to prevent potential issues with missing trace data or calls.

116-131: All data extraction methods follow a consistent structure.

Each of the data extraction methods (_agent_log_data, _function_log_data, etc.) follows a consistent pattern and properly extracts relevant data from different span types. This promotes maintainability and makes the code easier to understand.

Also applies to: 176-186, 188-201, 203-213, 215-232, 234-271

132-174: The response data extraction is thorough and handles all cases.

The _response_log_data method effectively extracts all relevant information from response spans, handling the presence of input, output, metadata, and usage statistics appropriately.

273-297: Excellent type handling in the _log_data method.

The dispatcher method _log_data correctly handles all span types and delegates to the appropriate extraction method. The fallback case returns an empty data format, which is a safe approach.

298-317: Robust parent call resolution logic.

The _get_parent_call method effectively navigates the parent-child relationship between spans and traces to find the appropriate parent call.

318-351: Special handling for Response spans in on_span_start is well-implemented.

The early return for ResponseSpanData spans to defer call creation until the end of the span is a good design choice that allows including input data in the call.

352-418: Error handling for span completion is properly implemented.

The span completion logic correctly handles error states and ensures all relevant data is included in the output. The special case handling for Response spans is also well-implemented.

419-453: The cleanup methods ensure no unfinished calls remain.

The shutdown and force_flush methods properly handle unfinished calls, which is important for ensuring data integrity when the application stops unexpectedly.

489-514: The factory function ensures singleton behavior appropriately.

The get_openai_agents_patcher function correctly implements the singleton pattern to ensure only one patcher instance exists, while also handling the case when integration is disabled.

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Actionable comments posted: 2

🧹 Nitpick comments (3)
weave/integrations/openai_agents/openai_agents.py (3)

92-113: Missing early return check for trace end.

For consistency with your other defensive checks, you should validate that trace.trace_id exists in both dicts before accessing them to avoid potential KeyError.

     def on_trace_end(self, trace: tracing.Trace) -> None:
         """Called when a trace ends."""
         tid = trace.trace_id
 
         if tid not in self._trace_data:
             return
 
         if tid not in self._trace_calls:
             return
 
+        # Get the trace data and mark as ended
         trace_data = self._trace_data[tid]
         self._ended_traces.add(tid)

225-228: Potential None checks missing for token metrics.

The code retrieves token counts using dict.get() but doesn't provide default values. If these values don't exist in span.data_usage.usage, the metrics will contain None values.

         metrics={
-                "tokens": span.data_usage.usage.get("total_tokens"),
-                "prompt_tokens": span.data_usage.usage.get("prompt_tokens"),
-                "completion_tokens": span.data_usage.usage.get("completion_tokens"),
+                "tokens": span.data_usage.usage.get("total_tokens", 0),
+                "prompt_tokens": span.data_usage.usage.get("prompt_tokens", 0),
+                "completion_tokens": span.data_usage.usage.get("completion_tokens", 0),
         },

391-409: Consider standardizing error output format.

The error handling uses two different error sources: span.error and log_data["error"], but doesn't standardize the error format. This could lead to inconsistent error representations in the output.

         # Add error if present
+        error = None
         if span.error:
-            output["error"] = span.error
+            error = span.error
         elif log_data["error"]:
-            output["error"] = log_data["error"]
+            error = log_data["error"]
+            
+        if error:
+            # Standardize error format (e.g., convert to dict if it's not already)
+            output["error"] = error if isinstance(error, dict) else {"message": str(error)}
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  • weave/integrations/openai_agents/openai_agents.py (1 hunks)
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`**/*.py`: Focus on pythonic code patterns. Check for proper...

**/*.py: Focus on pythonic code patterns.
Check for proper exception handling.
Verify type hints usage where applicable.
Look for potential performance improvements.
Don't comment on formatting if black/isort is configured.
Check for proper dependency injection patterns.
Verify proper async handling if applicable.

  • weave/integrations/openai_agents/openai_agents.py
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🔇 Additional comments (7)
weave/integrations/openai_agents/openai_agents.py (7)

43-49: Good type hinting with TypedDict for structured data.

The use of TypedDict for WeaveDataDict provides clear typing for the expected structure of log data throughout the module, which enhances code readability and type safety.

51-62: Constructor doesn't match docstring parameters.

The class docstring mentions a parent_call parameter, but it's not included in the __init__ method signature. This creates a disconnection between the documented and actual behavior.

Add the parent_call parameter to the constructor to match the docstring:

-    def __init__(self) -> None:
+    def __init__(self, parent_call: call_context.Call | None = None) -> None:
         self._trace_data: dict[str, dict[str, Any]] = {}
         self._trace_calls: dict[str, call_context.Call] = {}
         self._span_calls: dict[str, call_context.Call] = {}
         self._ended_traces: set[str] = set()
         self._span_parents: dict[str, str] = {}
+        self._parent_call = parent_call

71-91: Missing use of potential parent_call parameter in trace start.

If a parent_call parameter is meant to be used (as suggested by the docstring), it should be used in the on_trace_start method instead of always using the current call context.

Update the on_trace_start method to use the parent_call parameter:

     def on_trace_start(self, trace: tracing.Trace) -> None:
         """Called when a trace starts."""
         # Set up basic trace data
         self._trace_data[trace.trace_id] = {
             "name": trace.name,
             "type": "task",
             "metrics": {},
             "metadata": {},
         }

         # Create a call for this trace
         wc = require_weave_client()
         trace_call = wc.create_call(
             op="openai_agent_trace",
             inputs={"name": trace.name},
-            parent=call_context.get_current_call(),
+            parent=self._parent_call or call_context.get_current_call(),
             attributes={"type": "task", "agent_trace_id": trace.trace_id},
             display_name=trace.name,
         )
         self._trace_calls[trace.trace_id] = trace_call

296-313: Good defensive coding with multiple fallback mechanisms.

The _get_parent_call method provides robust fallback logic by first checking for a parent span, then falling back to the trace call. This makes the code resilient to different span hierarchies.

356-388: Good delayed call creation for Response spans.

The code intelligently defers call creation for Response spans until on_span_end to include input data. This avoids having to update calls later and keeps the code cleaner.

411-432: Good error handling for unfinished calls.

The code correctly handles unfinished calls by checking trace_call.ended_at before trying to finish the call again. This prevents potential errors from trying to finish already-finished calls.

481-506: Good singleton pattern implementation for the patcher.

The get_openai_agents_patcher function correctly implements a singleton pattern, ensuring that only one instance of the patcher is created. This prevents resource duplication and potential conflicts.

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Actionable comments posted: 0

🧹 Nitpick comments (4)
noxfile.py (1)

53-53: Add tests covering the new "openai_agents" shard.

The new shard parameter ("openai_agents") likely needs relevant tests to ensure coverage of the OpenAI Agents integration. Consider adding or verifying such tests under tests/integrations/openai_agents.

weave/integrations/openai_agents/openai_agents.py (3)

70-76: Suggestion to make dictionary management thread-safe if needed.

self._trace_data, self._trace_calls, self._span_calls, etc., may be accessed from multiple threads if your application spawns concurrency with agent tracing. If concurrency arises, consider guarding these data structures with locks or concurrency-safe data structures.

467-483: Add logging for patch failures.

The attempt_patch method catches all exceptions but does not log them. Logging such exceptions improves debuggability:

 except Exception as e:
     self.processor = None
+    import logging
+    logging.warning(f"Failed to patch OpenAI Agents: {e}", exc_info=True)
     return False
 else:
     return True

507-515: Encourage verifying patcher usage with additional tests.

The global _openai_agents_patcher management is straightforward; consider creating a small test suite to confirm get_openai_agents_patcher returns the expected patcher and that multiple calls do not overwrite an existing instance.

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📒 Files selected for processing (3)
  • noxfile.py (1 hunks)
  • pyproject.toml (1 hunks)
  • weave/integrations/openai_agents/openai_agents.py (1 hunks)
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📓 Path-based instructions (1)
`**/*.py`: Focus on pythonic code patterns. Check for proper...

**/*.py: Focus on pythonic code patterns.
Check for proper exception handling.
Verify type hints usage where applicable.
Look for potential performance improvements.
Don't comment on formatting if black/isort is configured.
Check for proper dependency injection patterns.
Verify proper async handling if applicable.

  • noxfile.py
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🔇 Additional comments (2)
pyproject.toml (1)

111-111:

❓ Verification inconclusive

Consider verifying the openai-agents>=0.0.4 dependency.

It’s beneficial to confirm that version 0.0.4 or above remains stable, well-maintained, and compatible with the rest of your dependencies.

Please run a security and compatibility check on the openai-agents package. For example:

🏁 Script executed:

#!/bin/bash
# Check reasons behind version pin `>=0.0.4`.
# 1) Confirm whether openai-agents requires a higher pinned version or has known vulnerabilities.

echo "Checking PyPI data for openai-agents..."
curl -s https://pypi.org/pypi/openai-agents/json | jq '.info.version, .releases | keys'

Length of output: 233

Action Required: Manually Verify the openai-agents>=0.0.4 Dependency
Our automated check encountered a JSON parsing issue—specifically, the command attempted to treat the version string ("0.0.4") as an object when extracting keys. This prevented us from reliably extracting the full release information to assess vulnerability status and ensure compatibility.

Recommendations:

  • Separate Queries: Instead of querying both properties in one go, run two separate commands, for example:
    • curl -s https://pypi.org/pypi/openai-agents/json | jq '.info.version'
    • curl -s https://pypi.org/pypi/openai-agents/json | jq '.releases | keys'
  • Manual Review: Confirm that version 0.0.4 (or later) is stable, well-maintained, and does not introduce any vulnerabilities that might affect the rest of your dependencies.

Please verify the above manually and update the dependency requirements if a higher version is necessary.

weave/integrations/openai_agents/openai_agents.py (1)

1-6: Good introductory docstring and structured overview.

This comprehensive docstring clearly explains the purpose of the OpenAI Agents integration. This is a best practice for maintainability.

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Actionable comments posted: 1

♻️ Duplicate comments (3)
weave/integrations/openai_agents/openai_agents.py (3)

76-96: ⚠️ Potential issue

Parent call handling missing in on_trace_start.

If a parent_call parameter is added to the constructor (as suggested by the docstring), it should be used in the on_trace_start method instead of always using the current call context.

def on_trace_start(self, trace: tracing.Trace) -> None:
    """Called when a trace starts."""
    # Set up basic trace data
    self._trace_data[trace.trace_id] = {
        "name": trace.name,
        "type": "task",
        "metrics": {},
        "metadata": {},
    }

    # Create a call for this trace
    wc = require_weave_client()
    trace_call = wc.create_call(
        op="openai_agent_trace",
        inputs={"name": trace.name},
-       parent=call_context.get_current_call(),
+       parent=self._parent_call or call_context.get_current_call(),
        attributes={"type": "task", "agent_trace_id": trace.trace_id},
        display_name=trace.name,
    )
    self._trace_calls[trace.trace_id] = trace_call

471-481: 🛠️ Refactor suggestion

Exception handling without logging.

The attempt_patch method catches all exceptions but doesn't log them, making it difficult to diagnose integration issues.

try:
    from agents.tracing import add_trace_processor

    self.processor = WeaveTracingProcessor()
    add_trace_processor(self.processor)
    self.patched = True
except Exception as e:
+    import logging
+    logging.warning(f"Failed to patch OpenAI Agents: {e}", exc_info=True)
    self.processor = None
    return False
else:
    return True

483-485: 🛠️ Refactor suggestion

Incomplete undo_patch implementation.

The undo_patch method doesn't actually remove the processor and doesn't flush pending data. While the comment indicates that OpenAI Agents doesn't support de-registering processors yet, it would be better to at least flush any pending data.

def undo_patch(self) -> bool:
    # OpenAI Agents doesn't have a way to de-register a processor yet...
+    # TODO: Implement proper processor removal when OpenAI Agents SDK supports it
+    if self.processor:
+        # Force flush to ensure all pending data is written
+        self.processor.force_flush()
+        self.patched = False
    return True
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**/*.py: Focus on pythonic code patterns.
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🔇 Additional comments (10)
weave/integrations/openai_agents/openai_agents.py (10)

1-6: Well-structured module docstring.

The docstring clearly explains the purpose of the module - to provide a Weave integration for OpenAI Agents through a TracingProcessor implementation.

21-26: Good import handling with graceful fallback.

The code correctly handles the case when OpenAI Agents package is not installed by creating a placeholder class. This makes the module importable even without the optional dependency.

29-46: Well-implemented helper functions for span metadata extraction.

The helper functions _call_type and _call_name are well designed to extract the appropriate information from OpenAI Agent spans. The robust handling of different span types ensures proper naming even when explicit names aren't available.

48-54: TypedDict provides good structure for log data.

The WeaveDataDict TypedDict provides a clear contract for the format of logged data, which helps maintain consistency across different span types.

97-118: Good early return pattern in on_trace_end.

The function uses appropriate early returns when the trace data or call isn't found, which prevents null reference exceptions and keeps the code clean.

119-311: Well-structured span data extraction methods.

The specialized methods for extracting log data from different span types (_agent_log_data, _response_log_data, etc.) maintain consistency while accommodating different data structures. The pattern of having a specialized method for each span type and a dispatcher method (_log_data) makes the code maintainable and extensible.

319-352: Thoughtful approach to span tracking.

The on_span_start method intelligently defers call creation for Response spans until more data is available, avoids creating spans without proper context, and attaches important metadata to the span call.

353-415: Comprehensive span completion logic.

The on_span_end method handles both regular spans and the specially deferred Response spans. It properly captures outputs, metrics, metadata, and errors, ensuring a complete record of the span's execution.

416-451: Robust cleanup handling with status tracking.

The shutdown and force_flush methods ensure that all spans are properly tracked even when execution is interrupted. The implementation gracefully handles different termination scenarios with appropriate status values.

488-513: Singleton pattern implementation for the patcher.

The get_openai_agents_patcher function correctly implements a singleton pattern, ensuring that only one patcher instance is created. It also properly handles disabled settings by returning a NoOpPatcher.

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Actionable comments posted: 0

♻️ Duplicate comments (2)
weave/integrations/openai_agents/openai_agents.py (2)

477-479: 🛠️ Refactor suggestion

Incomplete undo_patch implementation.

The undo_patch method should at least flush any pending data to ensure it's written, even if deregistration isn't supported yet.

    def undo_patch(self) -> bool:
        # OpenAI Agents doesn't have a way to de-register a processor yet...
+        if self.processor:
+            # Force flush to ensure all pending data is written
+            self.processor.force_flush()
+            self.patched = False
        return True

460-476: 🛠️ Refactor suggestion

Missing error logging in attempt_patch method.

The attempt_patch method catches all exceptions but doesn't log them, making it difficult to diagnose integration issues.

        try:
            from agents.tracing import add_trace_processor

            self.processor = WeaveTracingProcessor()
            add_trace_processor(self.processor)
            self.patched = True
        except Exception as e:
+            import logging
+            logging.warning(f"Failed to patch OpenAI Agents: {e}", exc_info=True)
            self.processor = None
            return False
        else:
            return True
🧹 Nitpick comments (2)
weave/integrations/openai_agents/openai_agents.py (2)

18-18: Global variable should use proper typing.

The global variable should explicitly specify its type with None as the initial value.

-_openai_agents_patcher: OpenAIAgentsPatcher | None = None
+_openai_agents_patcher: OpenAIAgentsPatcher | None = None

65-70: Constructor could use type annotation for self.

The constructor properly initializes the internal state with appropriate data structures, but missing the type annotation for self parameter.

-def __init__(self) -> None:
+def __init__(self: "WeaveTracingProcessor") -> None:
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`**/*.py`: Focus on pythonic code patterns. Check for proper...

**/*.py: Focus on pythonic code patterns.
Check for proper exception handling.
Verify type hints usage where applicable.
Look for potential performance improvements.
Don't comment on formatting if black/isort is configured.
Check for proper dependency injection patterns.
Verify proper async handling if applicable.

  • weave/integrations/openai_agents/openai_agents.py
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🔇 Additional comments (24)
weave/integrations/openai_agents/openai_agents.py (24)

1-6: Clean and informative module docstring.

The docstring clearly explains the purpose of the module and its functionality, which is good for maintainability.

8-17: Import structure looks good.

The imports are organized logically and include proper type hints. The use of from __future__ import annotations enables forward references in type annotations.

21-26: Good defensive import handling.

The code properly handles the case when the agents package isn't installed by providing a no-op stub class. This helps prevent runtime errors while still maintaining type hints.

29-46: Helper functions are well-designed.

The helper functions _call_type and _call_name cleanly encapsulate the logic for determining the appropriate call type and name for a given span. The use of descriptive docstrings is a plus.

48-54: Clear type definition.

The WeaveDataDict TypedDict provides a clear structure for the data being collected and processed, making the code more maintainable and self-documenting.

56-63: Good class documentation.

The docstring for WeaveTracingProcessor clearly explains its purpose and behavior. The pyright: ignore[reportGeneralTypeIssues] comment is appropriate given the potential import issues.

93-112: Proper trace end handling.

The on_trace_end method properly checks if trace data and calls exist before proceeding, which prevents potential errors. The method also correctly finishes the trace call with appropriate output.

113-128: Agent log data extraction looks good.

The _agent_log_data method properly extracts and structures data from an agent span, ensuring that it follows the WeaveDataDict format.

129-171: Comprehensive response data handling.

The _response_log_data method thoroughly extracts and organizes data from response spans, separating inputs, outputs, metadata, and metrics appropriately. The error field is correctly initialized as None.

173-183: Function log data extraction is correct.

The _function_log_data method properly extracts input and output data from function spans.

185-198: Handoff log data extraction is correct.

The _handoff_log_data method properly extracts agent handoff information.

200-210: Guardrail log data extraction is correct.

The _guardrail_log_data method properly extracts the triggered state for guardrails.

212-229: Generation log data extraction is comprehensive.

The _generation_log_data method properly extracts model information and token usage metrics.

231-268: Custom log data handling is flexible.

The _custom_log_data method intelligently maps custom data to the appropriate fields and handles remaining fields as metadata. This provides good flexibility for custom span types.

270-294: Log data dispatcher is well-structured.

The _log_data method correctly dispatches to the appropriate handler based on span type, with a safe fallback for unknown span types.

295-312: Parent call resolution is thorough.

The _get_parent_call method has a good hierarchical approach to finding the parent call, checking both parent spans and the trace root.

313-346: Span start handling looks good.

The on_span_start method correctly checks for required parent calls and traces before creating a span call. The special handling for Response spans is appropriate since they may need data available only at span end.

347-409: Span end handling is comprehensive.

The on_span_end method handles the deferred creation of Response span calls and properly finalizes all spans with the appropriate output data. It also correctly handles error reporting from both the span itself and the log data.

410-437: Good cleanup for unfinished calls.

The _finish_unfinished_calls helper method ensures that all unfinished calls are properly closed, which is important for preventing resource leaks.

438-444: Proper shutdown and flush handling.

The shutdown and force_flush methods ensure that all traces and spans are properly finalized when the processor is stopped or flushed.

447-453: Clear patcher class documentation.

The docstring for OpenAIAgentsPatcher clearly explains its purpose and how it differs from other patchers.

455-459: Patcher constructor is properly implemented.

The constructor correctly initializes the patcher with settings and appropriate default values.

482-507: Singleton pattern implemented correctly.

The get_openai_agents_patcher function properly implements a singleton pattern to ensure that only one patcher instance is created. It also correctly handles the case when integration is disabled.

72-92:

❓ Verification inconclusive

Consider adding parent_call parameter to constructor for on_trace_start.

The on_trace_start method initializes trace data and creates a call, but it always uses the current call context. Consider adding a parent_call parameter to the constructor and using it here when available.

🌐 Web query:

Does openai agents tracing.TracingProcessor expect a standard __init__ signature?

💡 Result:

The TracingProcessor class in the OpenAI Agents SDK does not enforce a standard __init__ signature for subclasses, as it's designed as an abstract interface rather than a concrete implementation. Key observations:

  1. Base class definition
    The TracingProcessor ABC (source) only requires implementation of these abstract methods:

    @abstractmethod
def on_trace_start(self, trace: Trace) -> None
@abstractmethod 
def on_trace_end(self, trace: Trace) -> None
@abstractmethod
def on_span_start(self, span: Span[Any]) -> None
@abstractmethod
def on_span_end(self, span: Span[Any]) -> None
@abstractmethod
def shutdown(self) -> None

    No __init__ method is defined in the base class[3].

  2. Implementation flexibility
    Concrete implementations like BatchTraceProcessor show custom initialization parameters:

    def __init__(self, 
             exporter: TracingExporter,
             max_queue_size: int = 8192,
             max_batch_size: int = 128,
             schedule_delay: float = 5.0,
             export_trigger_ratio: float = 0.7):

    This demonstrates subclass-specific initialization needs[1].

  3. Type checking considerations
    As shown in Python typing discussions[2][5], there's no enforced signature validation for __init__ methods in subclass hierarchies when using ABCs. Developers must manually ensure compatibility when overriding initialization.

  4. SDK extension patterns
    The official documentation shows processor implementations with varying initialization patterns, including:

    • API key configuration via set_api_key() method[1]
    • Batch processing parameters in BatchTraceProcessor[1]
    • Multi-processor handling in SynchronousMultiTracingProcessor[4]

This design allows implementers to create custom tracing processors with initialization parameters specific to their needs (e.g., different exporters, queue configurations, or authentication methods) while maintaining compatibility with the SDK's tracing infrastructure[1][3][6].

Citations:

Enhancement Suggestion: Optional parent_call Parameter in Constructor
The current implementation of on_trace_start always sources the parent call via call_context.get_current_call(). Given that the OpenAI Agents SDK’s TracingProcessor interface doesn’t enforce a standard __init__ signature, you have the flexibility to extend your processor’s constructor. Consider:

  • Adding an optional parent_call parameter to your class constructor.
  • Utilizing this parameter in on_trace_start to use the provided parent call (if available) instead of unconditionally calling call_context.get_current_call(). This would allow greater flexibility for users who may wish to supply a customized call context.

This change is optional and aligns with the SDK’s design patterns, which permit custom initialization parameters.

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Actionable comments posted: 2

🧹 Nitpick comments (6)
tests/integrations/openai_agents/cassettes/openai_agents_test/test_openai_agents_quickstart_homework.yaml (1)

1-448: The test cassette may expose implementation details of the guardrail mechanism.

The YAML cassette for the homework test contains multiple interactions showing the full flow of the guardrail and agent handoff logic. This is good for testing but includes internal implementation details like:

  1. The exact format of function calls and responses (lines 3-7)
  2. The JSON schema structure for the HomeworkOutput model (lines 100-102)
  3. Detailed traces with span IDs and timestamps (lines 292-297)

While not a security risk, consider if this level of detail is necessary for the test or if a more focused recording would be sufficient.

tests/integrations/openai_agents/openai_agents_test.py (5)

49-50: Clean up commented skip marker.

The commented-out skip marker should either be removed or uncommented with an updated reason if still needed.

-# @pytest.mark.skip(reason="Not clear why VCR fails for this test")

57-60: Add docstring to HomeworkOutput model.

The HomeworkOutput model should have a docstring explaining its purpose and fields for better code documentation.

class HomeworkOutput(BaseModel):
+    """
+    Pydantic model for guardrail output that determines if a user question is homework-related.
+    
+    Attributes:
+        is_homework: Boolean indicating if the query is homework-related
+        reasoning: String explaining why the model made this determination
+    """
    is_homework: bool
    reasoning: str

79-86: Add type hints to the guardrail function parameters.

The homework_guardrail function is missing proper type hints for the parameters, which should be added for consistency with the rest of the codebase.

-async def homework_guardrail(ctx, agent, input_data):
+async def homework_guardrail(ctx: agents.Context, agent: Agent, input_data: str) -> GuardrailFunctionOutput:
    result = await Runner.run(guardrail_agent, input_data, context=ctx.context)
    final_output = result.final_output_as(HomeworkOutput)
    return GuardrailFunctionOutput(
        output_info=final_output,
        tripwire_triggered=not final_output.is_homework,
    )

55-257: Consider refactoring long test function into smaller, more focused test cases.

The test_openai_agents_quickstart_homework function is quite long (200+ lines) and tests multiple aspects of the agent behavior. Consider breaking it down into more focused test cases to improve readability and maintainability.

You could split this into:

  1. A test for basic guardrail behavior
  2. A test for agent handoffs
  3. A test for tripwire triggering

For example:

@pytest.mark.asyncio
async def test_openai_agents_guardrail_check(client: WeaveClient) -> None:
    # Setup guardrail and agent...
    # Test just the guardrail behavior

@pytest.mark.asyncio
async def test_openai_agents_handoff_flow(client: WeaveClient) -> None:
    # Setup agents with handoffs...
    # Test the handoff flow

@pytest.mark.asyncio
async def test_openai_agents_guardrail_tripwire(client: WeaveClient) -> None:
    # Setup agents with tripwire...
    # Test the tripwire triggering

96-100: Consider improving error handling in the test.

The test expects a InputGuardrailTripwireTriggered exception but doesn't validate any details about the exception itself. Consider capturing the exception and verifying its properties.

-    with pytest.raises(agents.exceptions.InputGuardrailTripwireTriggered):
-        result = await Runner.run(triage_agent, "what is life")
+    with pytest.raises(agents.exceptions.InputGuardrailTripwireTriggered) as excinfo:
+        result = await Runner.run(triage_agent, "what is life")
+    
+    # Verify exception details
+    assert "homework_guardrail" in str(excinfo.value)  # Verify the exception message mentions the guardrail
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  • noxfile.py (2 hunks)
  • tests/integrations/openai_agents/cassettes/openai_agents_test/test_openai_agents_quickstart.yaml (1 hunks)
  • tests/integrations/openai_agents/cassettes/openai_agents_test/test_openai_agents_quickstart_homework.yaml (1 hunks)
  • tests/integrations/openai_agents/openai_agents_test.py (1 hunks)
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`**/*.{yaml,yml,json,tf}`: Check for security best practices. Verify environment-specific configurations. Look for hardcoded credentials or sensitive data. Ensure proper resource l...

**/*.{yaml,yml,json,tf}: Check for security best practices.
Verify environment-specific configurations.
Look for hardcoded credentials or sensitive data.
Ensure proper resource limits and requests.
Verify proper versioning of dependencies.
Check for infrastructure best practices.

  • tests/integrations/openai_agents/cassettes/openai_agents_test/test_openai_agents_quickstart.yaml
  • tests/integrations/openai_agents/cassettes/openai_agents_test/test_openai_agents_quickstart_homework.yaml
`**/*.py`: Focus on pythonic code patterns. Check for proper exception handling. Verify type hints usage where applicable. Look for potential performance improvements. Don't commen...

**/*.py: Focus on pythonic code patterns.
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Verify proper async handling if applicable.

  • tests/integrations/openai_agents/openai_agents_test.py
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💡 Verification agent

🧩 Analysis chain

VCR cassette contains sensitive information about the request structure.

The YAML cassette for the test_openai_agents_quickstart test contains detailed information about the OpenAI API interaction. While this is necessary for testing, there are some considerations:

  1. The file contains user-agent strings and package version information (line 19, 29)
  2. The file shows runtime version information (line 37)
  3. The file includes organization identifiers (line 82)

Consider adding more header filtering in the VCR configuration to avoid leaking potentially sensitive information.

🏁 Script executed:

#!/bin/bash
# Check if the VCR configuration in the test file has appropriate header filtering

grep -A5 "mark.vcr" tests/integrations/openai_agents/openai_agents_test.py

Length of output: 535

Sensitive API Request Information Exposed in Test Cassette

The VCR cassette for test_openai_agents_quickstart still contains sensitive details recorded from the actual API request. While it's expected to capture some request data for testing, the current VCR configuration only filters the "authorization" header. This leaves other sensitive headers—such as the user-agent string, package version (lines 19 and 29), runtime version (line 37), and organization identifiers (line 82)—unfiltered and exposed in the cassette.

Actionable Recommendations:

  • Extend Header Filtering: Update the VCR configuration (in tests/integrations/openai_agents/openai_agents_test.py) to include additional headers, such as:
    • "user-agent"
    • "x-stainless-package-version"
    • "x-stainless-runtime"
    • Headers carrying organization identifiers (or any other sensitive metadata)
  • Double-check Captured Data: Ensure that the cassette does not inadvertently capture other sensitive request details that are not required for test reproducibility.

@andrewtruong andrewtruong marked this pull request as ready for review March 18, 2025 04:52
@andrewtruong andrewtruong requested a review from a team as a code owner March 18, 2025 04:52
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Actionable comments posted: 0

🧹 Nitpick comments (3)
tests/integrations/openai_agents/openai_agents_test.py (3)

9-9: Address or clarify the TODO comment with concrete details.

This TODO comment about the Responses API doesn't have enough context to understand what specifically needs to be updated. Either implement the needed changes, add more details about what needs to be done, or reference a tracking issue.

58-58: Clean up commented-out code.

There's a commented-out skip decorator with an explanation "Not clear why VCR fails for this test". This suggests there might be an unresolved issue with the test reliability. Either remove the comment if the issue is resolved or add a tracking issue reference.

115-266: Consider reducing assertion repetition with helper methods.

The test contains extensive, repetitive assertion blocks that follow a similar pattern. This could be refactored into helper methods to improve readability and maintainability.

+ def assert_call_basics(call, name, output_value=None, error=None):
+     assert call.inputs["name"] == name
+     assert call.output["output"] == output_value
+     assert call.output["metrics"] == {}
+     assert call.output["error"] == error
+
+ def assert_agent_metadata(call, tools=None, handoffs=None, output_type="str"):
+     tools = tools or []
+     handoffs = handoffs or []
+     assert call.output["metadata"]["tools"] == tools
+     assert call.output["metadata"]["handoffs"] == handoffs
+     assert call.output["metadata"]["output_type"] == output_type
+
+ def assert_response_basics(call, content, role="user"):
+     assert call.inputs["name"] == "Response"
+     assert call.inputs["input"][0]["content"] == content
+     assert call.inputs["input"][0]["role"] == role
+
+ def assert_response_output(val, role="assistant", type="message", status="completed"):
+     assert isinstance(val, ObjectRecord)
+     assert val.role == role
+     assert val.type == type
+     assert val.status == status

Then use these helpers to simplify the test:

# Example usage:
assert_call_basics(call1, "Triage Agent")
assert_agent_metadata(call1, handoffs=["History Tutor", "Math Tutor"])

assert_response_basics(call4, "who was the first president of the united states?")
assert_response_output(call4.output["output"][0]._val, type="function_call", name="transfer_to_history_tutor")
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🔇 Additional comments (6)
tests/integrations/openai_agents/openai_agents_test.py (6)

88-95: Good implementation of the homework guardrail function.

The async function properly uses the Runner to execute the guardrail agent and correctly transforms the result into the expected output type. Well-structured error handling through the GuardrailFunctionOutput mechanism.

108-109: Excellent explicit testing of guardrail tripwire behavior.

Good practice to explicitly test both the positive case (allowed input) and negative case (tripwire triggered) to verify the guardrail functionality is working correctly.

65-86: Well-structured agent definitions with clear responsibilities.

The code clearly defines agents with specific responsibilities - a guardrail agent to check for homework questions, and specialized tutors for different subjects. Good use of descriptive names and instructions that make the purpose of each agent clear.

96-103: Clear triage agent implementation with proper guardrail configuration.

The triage agent is well-configured with appropriate instructions, handoffs to specialized agents, and input guardrails. The structure follows best practices for agent orchestration.

1-7: Good import organization and utilization of required dependencies.

The imports are well-organized, separating external dependencies from internal Weave modules. Appropriate classes are imported from the agents SDK and Weave tracing components.

17-56: Well-structured test for basic agent functionality.

The first test provides a good baseline for verifying the basic functionality of the OpenAI Agents integration with Weave. It properly checks the structure of calls and the content of responses, with clear assertions.

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Actionable comments posted: 0

🧹 Nitpick comments (8)
tests/integrations/openai_agents/openai_agents_test.py (8)

8-8: Make the TODO comment more actionable.

The current TODO doesn't specify who should address this issue or when. Consider adding more details, such as linking to a relevant issue/ticket number or specifying the action items required.

-# TODO: Responses should be updated once we have patching for the new Responses API
+# TODO(GH-XXXX): Update response assertions once we have patching for the new Responses API

56-58: Remove or update the commented-out skip marker.

There's a commented-out skip marker with a reason for skipping. If the issue has been resolved, consider removing the comment entirely. If not, consider updating the comment to indicate the current status of the issue.

-# @pytest.mark.skip(reason="Not clear why VCR fails for this test")

86-93: Consider enhancing type hints in the homework_guardrail function.

The function lacks type annotations for its parameters, which would improve code readability and provide better IDE support.

-    async def homework_guardrail(ctx, agent, input_data):
+    async def homework_guardrail(ctx: agents.Context, agent: Agent, input_data: str) -> GuardrailFunctionOutput:

106-107: Add assertion for the error message or content.

When testing for exceptions, it's a good practice to also verify the content or message of the exception to ensure it's failing for the expected reason.

-    with pytest.raises(agents.exceptions.InputGuardrailTripwireTriggered):
+    with pytest.raises(agents.exceptions.InputGuardrailTripwireTriggered) as excinfo:
         result = await Runner.run(triage_agent, "what is life")
+    assert "guardrail triggered" in str(excinfo.value) or "tripwire" in str(excinfo.value)

109-113: Improve readability with more descriptive section comments.

The current section comment could be more descriptive about what is being tested in this block.

-    #####################
-    ### Result1 Block ###
-    #####################
+    #####################
+    ### Validating first agent run (history question) ###
+    #####################

215-219: Improve readability with more descriptive section comments.

Similar to the previous section comment, this one could be more descriptive about what is being tested.

-    #####################
-    ### Result2 Block ###
-    #####################
+    #####################
+    ### Validating second agent run (guardrail triggered) ###
+    #####################

104-108: Consider adding a comment explaining the expected behavior difference.

It would be helpful to have a comment explaining why the first query succeeds but the second one triggers the guardrail.

     result = await Runner.run(
         triage_agent, "who was the first president of the united states?"
     )
+    # This query should pass the homework guardrail check and be handled by the history tutor
     with pytest.raises(agents.exceptions.InputGuardrailTripwireTriggered):
+        # "What is life" is not homework-related, so guardrail should prevent processing
         result = await Runner.run(triage_agent, "what is life")

11-63: Consider refactoring the two tests to share common verification logic.

Both tests have similar assertion patterns for verifying calls and outputs. Consider extracting common verification logic into helper functions to reduce duplication and improve maintainability.

For example, you could create a helper function like:

def verify_agent_call(call, expected_name, expected_metadata=None):
    assert call.inputs["name"] == expected_name
    assert call.output["output"] is None
    assert call.output["metrics"] == {}
    
    if expected_metadata:
        for key, value in expected_metadata.items():
            assert call.output["metadata"][key] == value
    
    assert call.output["error"] is None

Then use it in your tests:

verify_agent_call(
    agent_call, 
    "Assistant", 
    {"tools": [], "handoffs": [], "output_type": "str"}
)
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`**/*.py`: Focus on pythonic code patterns. Check for proper exception handling. Verify type hints usage where applicable. Look for potential performance improvements. Don't commen...

**/*.py: Focus on pythonic code patterns.
Check for proper exception handling.
Verify type hints usage where applicable.
Look for potential performance improvements.
Don't comment on formatting if black/isort is configured.
Check for proper dependency injection patterns.
Verify proper async handling if applicable.

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🔇 Additional comments (2)
tests/integrations/openai_agents/openai_agents_test.py (2)

11-54: The test structure looks good and appropriately verifies the OpenAI agent integration.

The test properly verifies the agent's functionality by creating an agent, running it synchronously, and asserting the structure and content of the resulting calls. The assertions are comprehensive and cover all important aspects of the agent execution.

63-261: The test covers all aspects of the OpenAI Agents SDK integration workflow.

The test is comprehensive and thoroughly verifies the asynchronous execution of multiple agents with guardrails. It tests both successful execution and failure cases, and makes detailed assertions about the structure and content of all calls and responses.

zbirenbaum
zbirenbaum reviewed Mar 18, 2025
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weave/integrations/openai_agents/openai_agents.py
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Left a couple comments but this is a bit hard to parse. Would it be possible to separate out the handlers for Tracing and Spans into their own files since there is already an openai_agents dir so it's a bit clearer to review?

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I would prefer this too, but it's not obvious how to get the "trace" call into the "span" processor

weave/integrations/openai_agents/openai_agents.py
trace_call = wc.create_call(
op="openai_agent_trace",
inputs={"name": trace.name},
parent=call_context.get_current_call(),
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How is the call structure being resolved for a trace? They have group_id but only spans have a parent_id

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In this context, trace_call is basically a dummy call that acts like the "trace" container.

All call structure is otherwise modelled with child calls

weave/integrations/openai_agents/openai_agents.py
)
self._trace_calls[trace.trace_id] = trace_call

def on_trace_end(self, trace: tracing.Trace) -> None:
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not a requirement, but was thinking this could be organized a bit by having a separate processor implemented for spans and traces. Then just call self.span_processor.on_trace_end(trace: Trace)

Edit: sorry meant to type self.trace_processor.on_trace_end(trace: Trace)

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How would you get the "trace" call from the SpanProcessor?

coderabbitai[bot]
coderabbitai bot reviewed Mar 18, 2025
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Actionable comments posted: 0

🧹 Nitpick comments (4)
tests/integrations/openai_agents/openai_agents_test.py (4)

8-8: Update or remove the outdated TODO comment.

This TODO comment appears to be outdated now that the PR is implementing the patching for the Responses API. Consider removing it or updating it with more specific details if there's still work to be done.

-# TODO: Responses should be updated once we have patching for the new Responses API
+# Integration tests for OpenAI Agents SDK with Weave

85-92: Add type hints to the homework_guardrail function parameters.

For consistency with the rest of the codebase and to improve code quality, add appropriate type hints to the parameters of the homework_guardrail function.

-    async def homework_guardrail(ctx, agent, input_data):
+    async def homework_guardrail(ctx: agents.Context, agent: Agent, input_data: str) -> GuardrailFunctionOutput:

16-54: Consider adding explicit error handling for robust testing.

The test currently doesn't handle any potential exceptions that might occur during the API call. Consider adding try/except blocks or expectations for possible API errors to make the test more robust.

For example:

    try:
        result = Runner.run_sync(agent, "Write a haiku about recursion in programming.")
        # Your existing assertions
    except Exception as e:
        pytest.fail(f"Runner.run_sync failed with exception: {e}")

102-107: Add more descriptive assertion message when testing exception.

When testing that an exception is raised, include a descriptive message to make it clear what's being tested and why the exception is expected.

    result = await Runner.run(
        triage_agent, "who was the first president of the united states?"
    )
-    with pytest.raises(agents.exceptions.InputGuardrailTripwireTriggered):
+    with pytest.raises(agents.exceptions.InputGuardrailTripwireTriggered, 
+                      match="Guardrail triggered for non-homework question"):
        result = await Runner.run(triage_agent, "what is life")
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`**/*.py`: Focus on pythonic code patterns. Check for proper exception handling. Verify type hints usage where applicable. Look for potential performance improvements. Don't commen...

**/*.py: Focus on pythonic code patterns.
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Verify type hints usage where applicable.
Look for potential performance improvements.
Don't comment on formatting if black/isort is configured.
Check for proper dependency injection patterns.
Verify proper async handling if applicable.

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🔇 Additional comments (2)
tests/integrations/openai_agents/openai_agents_test.py (2)

11-54: LGTM: The test provides comprehensive assertions for agent workflow.

The test effectively creates an agent, runs it with a specific input, and thoroughly validates the structure and content of the resulting calls. The assertions cover all important aspects including inputs, outputs, metrics, and metadata.

56-260: LGTM: The complex agent test is well-structured with comprehensive validation.

This test effectively demonstrates the interactions between multiple agents, including guardrails and handoffs. The use of async/await is appropriate, and the detailed assertions thoroughly validate the expected behavior at each step of the process.

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Actionable comments posted: 4

🧹 Nitpick comments (4)
tests/integrations/openai_agents/openai_agents_test.py (4)

19-19: Consider improving test robustness with context manager.

The Runner.run_sync call should ideally be wrapped in a context manager to ensure proper cleanup, especially when dealing with external API calls.

-    result = Runner.run_sync(agent, "Write a haiku about recursion in programming.")
+    with agents.context.AgentContext() as ctx:
+        result = Runner.run_sync(agent, "Write a haiku about recursion in programming.", context=ctx)

86-93: Add type annotations to function parameters.

The homework_guardrail function is missing type annotations on its parameters, which is inconsistent with the rest of the file that uses proper type hints.

-    async def homework_guardrail(ctx, agent, input_data):
+    async def homework_guardrail(ctx: agents.context.AgentContext, agent: Agent, input_data: str) -> GuardrailFunctionOutput:

106-107: Improve exception handling test clarity and reliability.

The current approach doesn't capture or inspect the actual exception details. By capturing the exception, you can verify more details about what was triggered.

-    with pytest.raises(agents.exceptions.InputGuardrailTripwireTriggered):
-        result = await Runner.run(triage_agent, "what is life")
+    with pytest.raises(agents.exceptions.InputGuardrailTripwireTriggered) as excinfo:
+        result = await Runner.run(triage_agent, "what is life")
+    # Verify exception details
+    assert "homework" in str(excinfo.value).lower(), "Exception should mention homework guardrail"

257-260: Add assertions for error message content.

While you're verifying the structure of the error response, you're not checking anything about the actual error message content, which should ideally explain why the guardrail was triggered.

     assert val13.role == "assistant"
     assert val13.type == "message"
     assert val13.status == "completed"
+    # Verify error message content explains homework guardrail
+    error_text = val13.content[0].text if hasattr(val13.content[0], 'text') else str(val13.content[0])
+    assert "homework" in error_text.lower() or "educational" in error_text.lower(), "Error should explain homework policy"
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`**/*.py`: Focus on pythonic code patterns. Check for proper exception handling. Verify type hints usage where applicable. Look for potential performance improvements. Don't commen...

**/*.py: Focus on pythonic code patterns.
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Verify type hints usage where applicable.
Look for potential performance improvements.
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Check for proper dependency injection patterns.
Verify proper async handling if applicable.

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🔇 Additional comments (1)
tests/integrations/openai_agents/openai_agents_test.py (1)

8-8: Remove or update the TODO comment.

This TODO comment indicates that responses should be updated once patching for the new Responses API is available. Since this PR is implementing OpenAI Agents SDK integration, you should either update this comment with a more specific task or remove it if it's no longer relevant.

Is the TODO comment still accurate, or has the Responses API patching been implemented elsewhere in the PR?

tests/integrations/openai_agents/openai_agents_test.py Outdated
)


@pytest.mark.skip(reason="This test works, but the order of requests to OpenAI can be mixed up (by the Agent framework). This causes the test to fail more than reasonable in CI.")
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🛠️ Refactor suggestion

Consider implementing a more stable test approach.

The test is skipped due to the order of requests causing test failures in CI. Instead of skipping, consider making the test more robust.

You could modify the test to be less dependent on the exact order of operations:

  1. Collect all calls at the end
  2. Filter calls by name and verify their content individually rather than by index
  3. Use a VCR configuration that's more flexible with request ordering

tests/integrations/openai_agents/openai_agents_test.py
Comment on lines +184 to +187
assert val6.role == "assistant"
assert val6.type == "message"
assert val6.status == "completed"

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🛠️ Refactor suggestion

Add content assertion for History Tutor response.

You're verifying the structure of the response (role, type, status) but not checking anything about the actual content of the response, which is a key aspect to test.

     assert val6.role == "assistant"
     assert val6.type == "message"
     assert val6.status == "completed"
+    assert val6.content is not None
+    # Verify some content about Washington in the response
+    response_text = val6.content[0].text if hasattr(val6.content[0], 'text') else str(val6.content[0])
+    assert "washington" in response_text.lower(), "Response should mention Washington"
+    assert "president" in response_text.lower(), "Response should mention presidency"
📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
assert val6.role == "assistant"
assert val6.type == "message"
assert val6.status == "completed"
assert val6.role == "assistant"
assert val6.type == "message"
assert val6.status == "completed"
assert val6.content is not None
# Verify some content about Washington in the response
response_text = val6.content[0].text if hasattr(val6.content[0], 'text') else str(val6.content[0])
assert "washington" in response_text.lower(), "Response should mention Washington"
assert "president" in response_text.lower(), "Response should mention presidency"

tests/integrations/openai_agents/openai_agents_test.py
Comment on lines +104 to +105
triage_agent, "who was the first president of the united states?"
)
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🛠️ Refactor suggestion

Improve test by adding assertions for the first successful test case.

The first call to Runner.run doesn't have any assertions to verify the result. You should add assertions to validate the successful case, not just the failure case.

     result = await Runner.run(
         triage_agent, "who was the first president of the united states?"
     )
+    assert result is not None
+    assert isinstance(result.final_output, str)
+    assert "washington" in result.final_output.lower(), "Expected response about George Washington"
📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
triage_agent, "who was the first president of the united states?"
)
result = await Runner.run(
triage_agent, "who was the first president of the united states?"
)
assert result is not None
assert isinstance(result.final_output, str)
assert "washington" in result.final_output.lower(), "Expected response about George Washington"

tests/integrations/openai_agents/openai_agents_test.py
Comment on lines +46 to +53
val = response_call.output["output"][0]
assert val.role == "assistant"
assert val.type == "message"
assert val.status == "completed"
assert (
val.content[0].text
== "Code calls to itself, \nInfinite loops in silence, \nPatterns emerge clear."
)
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🛠️ Refactor suggestion

Improve assertion robustness for response text.

The current assertion strictly checks for exact text content. Since you're working with AI-generated content, this might cause flaky tests if OpenAI's responses vary slightly.

-    assert (
-        val.content[0].text
-        == "Code calls to itself,  \nInfinite loops in silence,  \nPatterns emerge clear."
-    )
+    # More flexible assertion that checks for structure and theme rather than exact wording
+    haiku_text = val.content[0].text
+    assert isinstance(haiku_text, str)
+    assert len(haiku_text.strip().split("\n")) >= 3, "Response should contain at least 3 lines for a haiku"
+    assert "recursion" in haiku_text.lower() or "recursive" in haiku_text.lower(), "Haiku should mention recursion"
📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
val = response_call.output["output"][0]
assert val.role == "assistant"
assert val.type == "message"
assert val.status == "completed"
assert (
val.content[0].text
== "Code calls to itself, \nInfinite loops in silence, \nPatterns emerge clear."
)
val = response_call.output["output"][0]
assert val.role == "assistant"
assert val.type == "message"
assert val.status == "completed"
# More flexible assertion that checks for structure and theme rather than exact wording
haiku_text = val.content[0].text
assert isinstance(haiku_text, str)
assert len(haiku_text.strip().split("\n")) >= 3, "Response should contain at least 3 lines for a haiku"
assert "recursion" in haiku_text.lower() or "recursive" in haiku_text.lower(), "Haiku should mention recursion"

zbirenbaum
zbirenbaum reviewed Mar 19, 2025
View reviewed changes
noxfile.py Outdated
@@ -92,6 +93,9 @@ def tests(session, shard):
env["MISTRAL_API_KEY"] = session.env.get("MISTRAL_API_KEY")
env["OPENAI_API_KEY"] = session.env.get("OPENAI_API_KEY")

if shard == "openai_agents":
env["OPENAI_API_KEY"] = session.env.get("OPENAI_API_KEY", "DUMMY_API_KEY")
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Maybe I'm doing something wrong but this line replaces the API key to DUMMY_API_KEY even when passed explicitly like so:
OPENAI_API_KEY=<api-key> nox -e "tests-3.13(shard='openai_agents')"

Since this makes it impossible to re-record and I can't find a way to pass environment vars to the command directly (--env sets the virtual environment not the vars) it might be an issue.

noxfile.py Outdated
@@ -92,6 +93,9 @@ def tests(session, shard):
env["MISTRAL_API_KEY"] = session.env.get("MISTRAL_API_KEY")
env["OPENAI_API_KEY"] = session.env.get("OPENAI_API_KEY")

if shard == "openai_agents":
env["OPENAI_API_KEY"] = session.env.get("OPENAI_API_KEY", "DUMMY_API_KEY")
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This never reads from the env variable even when explicitly passed which breaks re-recording

zbirenbaum
zbirenbaum approved these changes Mar 20, 2025
View reviewed changes
tests/integrations/openai_agents/test_autopatch.py Outdated
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remove empty file

@andrewtruong andrewtruong enabled auto-merge (squash) March 20, 2025 03:53
@andrewtruong andrewtruong merged commit c3d1bed into master Mar 20, 2025
129 of 131 checks passed
@andrewtruong andrewtruong deleted the andrew/oai3 branch March 20, 2025 04:11
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