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…into feat/draw_graph
Add callout for ZDR intracing
Add callout for ZDR intracing
These classes print a really long, unreadable output by default. This trims it to the main useful info (i.e. output, last agent, overview of run). --- [//]: # (BEGIN SAPLING FOOTER) * __->__ openai#196 * openai#195
Moves utils files into a separate directory so we don't end up with a massive utils file --- [//]: # (BEGIN SAPLING FOOTER) * openai#196 * __->__ openai#195
Update the README to point to the list of tracing processors. The docs will contain all the external processors, so the readme doesn't get too long.
# Summary This adds the missing TracingProcessor export to __init__.py. # Behavior When trying to add a custom tracing processor, the TracingProcessor importing fails with not found error when trying the example usage proposed in issue openai#164 Specifically this line throws the error: `add_trace_processor(MyTracingProcessor("output"))` # Expected Behavior Inspecting the init file, simply the import/export was missing. Adding these made the example code work for me # Test plan Local dev of example code in openai#164 # Issue number openai#164 # Checks None
I think we need to put reasoning-related outputs first to encourage CoT reasoning.
LangChain employee/maintainer, happy to make edits to our docs/integrations if needed.
## Changes - In this handoff example, we need to use `first_agent` with `random_number_tool`, instead of `second_agent` without that tool, since the user is asking to generate a random number. - fixed typo in comments
Argument name is not description but handoff_description
`assert len(spans) == 12` is a very weak assertion. This PR asserts the exported traces and spans more precisely in a readable tree format. And when the format of an exported trace/span changes (e.g. a new key is added to every span), you can use `pytest --inline-snapshot=fix` to update all relevant tests automatically. See https://15r10nk.github.io/inline-snapshot/latest/ for more info.
Clarifying that the handoff abstraction is implemented via Responses tool calling
…into feat/draw_graph
Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>
New release, to incorporate openai#519
Was not running on my stacked PRs.
Small refactor for rest of stack. --- [//]: # (BEGIN SAPLING FOOTER) * openai#524 * openai#523 * __->__ openai#522
Small refactor. --- [//]: # (BEGIN SAPLING FOOTER) * openai#524 * __->__ openai#523
Easy linking back to the repo, plus some social proof (stars/forks etc).
litellm is a library that abstracts away details/differences for a lot of model providers. Adding an extension, so that any provider can easily be integrated. --- [//]: # (BEGIN SAPLING FOOTER) * openai#532 * __->__ openai#524
…i#533) This pull request introduces the following changes: 1. **Exclude translated pages from search**: I explored ways to make the search plugin work with the i18n plugin, but it would require extensive custom JavaScript hacks. So for now, I’m holding off on this work. 2. **Switch from GPT-4.1 to o3 for even better translation quality**: While 4.1 performs well, o3 shows even greater quality for this task, and there’s no reason to avoid using it.
See openai#528, some folks are having issues because their output types are not strict-compatible. My approach was: 1. Create `AgentOutputSchemaBase`, which represents the base methods for an output type - the json schema + validation 2. Make the existing `AgentOutputSchema` subclass `AgentOutputSchemaBase` 3. Allow users to pass a `AgentOutputSchemaBase` to `Agent(output_type=...)`
Closes openai#435 and closes openai#538. Unit tests.
Only the file name is needed since graphviz's `render()` automatically adds the file extension. Also, unnecessary .gv (.dot) files are output, so the `cleanup=True` option has been modified to prevent them from being saved. Here is a similar modification, but in a different content. - openai/openai-agents-python#451
## Summary This replaces the default model provider with a `MultiProvider`, which has the logic: - if the model name starts with `openai/` or doesn't contain "/", use OpenAI - if the model name starts with `litellm/`, use LiteLLM to use the appropriate model provider. It's also extensible, so users can create their own mappings. I also imagine that if we natively supported Anthropic/Gemini etc, we can add it to MultiProvider to make it work. The goal is that it should be really easy to use any model provider. Today if you pass `model="gpt-4.1"`, it works great. But `model="claude-sonnet-3.7"` doesn't. If we can make it that easy, it's a win for devx. I'm not entirely sure if this is a good idea - is it too magical? Is the API too reliant on litellm? Comments welcome. ## Test plan For now, the example. Will add unit tests if we agree its worth mergin. --------- Co-authored-by: Steven Heidel <steven@heidel.ca>
Fix for openai#574 @rm-openai I'm not sure how to add a test within the repo but I have pasted a test script below that seems to work ```python import asyncio from openai.types.responses import ResponseTextDeltaEvent from agents import Agent, Runner async def main(): agent = Agent( name="Joker", instructions="You are a helpful assistant.", ) result = Runner.run_streamed(agent, input="Please tell me 5 jokes.") num_visible_event = 0 async for event in result.stream_events(): if event.type == "raw_response_event" and isinstance(event.data, ResponseTextDeltaEvent): print(event.data.delta, end="", flush=True) num_visible_event += 1 print(num_visible_event) if num_visible_event == 3: result.cancel() if __name__ == "__main__": asyncio.run(main()) ````
Now that `ModelSettings` has `Reasoning`, a non-primitive object, `dataclasses.as_dict()` wont work. It will raise an error when you try to serialize (e.g. for tracing). This ensures the object is actually serializable.
Per https://modelcontextprotocol.io/specification/draft/basic/lifecycle#timeouts "Implementations SHOULD establish timeouts for all sent requests, to prevent hung connections and resource exhaustion. When the request has not received a success or error response within the timeout period, the sender SHOULD issue a cancellation notification for that request and stop waiting for a response. SDKs and other middleware SHOULD allow these timeouts to be configured on a per-request basis." I picked 5 seconds since that's the default for SSE
In response to issue openai#587 , I implemented a solution to first check if `refusal` and `usage` attributes exist in the `delta` object. I added a unit test similar to `test_openai_chatcompletions_stream.py`. Let me know if I should change something. --------- Co-authored-by: Rohan Mehta <rm@openai.com>
When using the voice agent in typed code, it is suboptimal and error
prone to type the TTS voice variables in your code independently.
With this commit we are making the type exportable so that developers
can just use that and be future-proof.
Example of usage in code:
```
DEFAULT_TTS_VOICE: TTSModelSettings.TTSVoice = "alloy"
...
tts_voice: TTSModelSettings.TTSVoice = DEFAULT_TTS_VOICE
...
output = await VoicePipeline(
workflow=workflow,
config=VoicePipelineConfig(
tts_settings=TTSModelSettings(
buffer_size=512,
transform_data=transform_data,
voice=tts_voice,
instructions=tts_instructions,
))
).run(audio_input)
```
---------
Co-authored-by: Rohan Mehta <rm@openai.com>
Hi Team! This PR adds FutureAGI to the tracing documentation as one of the automatic tracing processors for OpenAI agents SDK. 
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