Merge branch 'main' of github.com:openai/openai-agents-python into alex/inline-snapshot

Author: alexmojaki

Makefile

@@ -18,6 +18,14 @@ mypy:
 tests: 
 	uv run pytest 
 
+.PHONY: snapshots-fix
+snapshots-fix: 
+	uv run pytest --inline-snapshot=fix 
+
+.PHONY: snapshots-create 
+snapshots-create: 
+	uv run pytest --inline-snapshot=create 
+
 .PHONY: old_version_tests
 old_version_tests: 
 	UV_PROJECT_ENVIRONMENT=.venv_39 uv run --python 3.9 -m pytest

README.md

@@ -142,7 +142,7 @@ The Agents SDK is designed to be highly flexible, allowing you to model a wide r
 
 ## Tracing
 
-The Agents SDK automatically traces your agent runs, making it easy to track and debug the behavior of your agents. Tracing is extensible by design, supporting custom spans and a wide variety of external destinations, including [Logfire](https://logfire.pydantic.dev/docs/integrations/llms/openai/#openai-agents), [AgentOps](https://docs.agentops.ai/v1/integrations/agentssdk), [Braintrust](https://braintrust.dev/docs/guides/traces/integrations#openai-agents-sdk), [Scorecard](https://docs.scorecard.io/docs/documentation/features/tracing#openai-agents-sdk-integration), and [Keywords AI](https://docs.keywordsai.co/integration/development-frameworks/openai-agent). For more details about how to customize or disable tracing, see [Tracing](http://openai.github.io/openai-agents-python/tracing).
+The Agents SDK automatically traces your agent runs, making it easy to track and debug the behavior of your agents. Tracing is extensible by design, supporting custom spans and a wide variety of external destinations, including [Logfire](https://logfire.pydantic.dev/docs/integrations/llms/openai/#openai-agents), [AgentOps](https://docs.agentops.ai/v1/integrations/agentssdk), [Braintrust](https://braintrust.dev/docs/guides/traces/integrations#openai-agents-sdk), [Scorecard](https://docs.scorecard.io/docs/documentation/features/tracing#openai-agents-sdk-integration), and [Keywords AI](https://docs.keywordsai.co/integration/development-frameworks/openai-agent). For more details about how to customize or disable tracing, see [Tracing](http://openai.github.io/openai-agents-python/tracing), which also includes a larger list of [external tracing processors](http://openai.github.io/openai-agents-python/tracing/#external-tracing-processors-list).
 
 ## Development (only needed if you need to edit the SDK/examples)
 

docs/tracing.md

@@ -9,6 +9,8 @@ The Agents SDK includes built-in tracing, collecting a comprehensive record of e
     1. You can globally disable tracing by setting the env var `OPENAI_AGENTS_DISABLE_TRACING=1`
     2. You can disable tracing for a single run by setting [`agents.run.RunConfig.tracing_disabled`][] to `True`
 
+***For organizations operating under a Zero Data Retention (ZDR) policy using OpenAI's APIs, tracing is unavailable.***
+
 ## Traces and spans
 
 -   **Traces** represent a single end-to-end operation of a "workflow". They're composed of Spans. Traces have the following properties:
@@ -88,10 +90,12 @@ To customize this default setup, to send traces to alternative or additional bac
 1. [`add_trace_processor()`][agents.tracing.add_trace_processor] lets you add an **additional** trace processor that will receive traces and spans as they are ready. This lets you do your own processing in addition to sending traces to OpenAI's backend.
 2. [`set_trace_processors()`][agents.tracing.set_trace_processors] lets you **replace** the default processors with your own trace processors. This means traces will not be sent to the OpenAI backend unless you include a `TracingProcessor` that does so.
 
-External trace processors include:
+## External tracing processors list
 
+-   [Arize-Phoenix](https://docs.arize.com/phoenix/tracing/integrations-tracing/openai-agents-sdk)
 -   [Braintrust](https://braintrust.dev/docs/guides/traces/integrations#openai-agents-sdk)
 -   [Pydantic Logfire](https://logfire.pydantic.dev/docs/integrations/llms/openai/#openai-agents)
 -   [AgentOps](https://docs.agentops.ai/v1/integrations/agentssdk)
--   [Scorecard](https://docs.scorecard.io/docs/documentation/features/tracing#openai-agents-sdk-integration))
+-   [Scorecard](https://docs.scorecard.io/docs/documentation/features/tracing#openai-agents-sdk-integration)
 -   [Keywords AI](https://docs.keywordsai.co/integration/development-frameworks/openai-agent)
+-   [LangSmith](https://docs.smith.langchain.com/observability/how_to_guides/trace_with_openai_agents_sdk)

examples/agent_patterns/input_guardrails.py

@@ -30,8 +30,8 @@
 
 ### 1. An agent-based guardrail that is triggered if the user is asking to do math homework
 class MathHomeworkOutput(BaseModel):
-    is_math_homework: bool
     reasoning: str
+    is_math_homework: bool
 
 
 guardrail_agent = Agent(

examples/agent_patterns/llm_as_a_judge.py

@@ -23,8 +23,8 @@
 
 @dataclass
 class EvaluationFeedback:
-    score: Literal["pass", "needs_improvement", "fail"]
     feedback: str
+    score: Literal["pass", "needs_improvement", "fail"]
 
 
 evaluator = Agent[None](

examples/basic/agent_lifecycle_example.py

@@ -74,7 +74,7 @@ class FinalResult(BaseModel):
 
 start_agent = Agent(
     name="Start Agent",
-    instructions="Generate a random number. If it's even, stop. If it's odd, hand off to the multipler agent.",
+    instructions="Generate a random number. If it's even, stop. If it's odd, hand off to the multiply agent.",
     tools=[random_number],
     output_type=FinalResult,
     handoffs=[multiply_agent],

examples/basic/hello_world_jupyter.py

@@ -3,7 +3,7 @@
 agent = Agent(name="Assistant", instructions="You are a helpful assistant")
 
 # Intended for Jupyter notebooks where there's an existing event loop
-result = await Runner.run(agent, "Write a haiku about recursion in programming.") # type: ignore[top-level-await]  # noqa: F704
+result = await Runner.run(agent, "Write a haiku about recursion in programming.")  # type: ignore[top-level-await]  # noqa: F704
 print(result.final_output)
 
 # Code within code loops,

examples/handoffs/message_filter.py

@@ -60,9 +60,9 @@ async def main():
 
         print("Step 1 done")
 
-        # 2. Ask it to square a number
+        # 2. Ask it to generate a number
         result = await Runner.run(
-            second_agent,
+            first_agent,
             input=result.to_input_list()
             + [{"content": "Can you generate a random number between 0 and 100?", "role": "user"}],
         )

examples/handoffs/message_filter_streaming.py

@@ -60,9 +60,9 @@ async def main():
 
         print("Step 1 done")
 
-        # 2. Ask it to square a number
+        # 2. Ask it to generate a number
         result = await Runner.run(
-            second_agent,
+            first_agent,
             input=result.to_input_list()
             + [{"content": "Can you generate a random number between 0 and 100?", "role": "user"}],
         )

pyproject.toml

@@ -47,7 +47,7 @@ dev = [
     "mkdocstrings[python]>=0.28.0",
     "coverage>=7.6.12",
     "playwright==1.50.0",
-    "inline-snapshot>=0.20.5",
+    "inline-snapshot>=0.20.7",
 ]
 [tool.uv.workspace]
 members = ["agents"]
@@ -118,3 +118,6 @@ filterwarnings = [
 markers = [
     "allow_call_model_methods: mark test as allowing calls to real model implementations",
 ]
+
+[tool.inline-snapshot]
+format-command="ruff format --stdin-filename {filename}"