diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 0000000..e78de87 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,28 @@ +--- +name: Bug report +about: Report a bug +title: '' +labels: bug +assignees: '' + +--- + +### Please read this first + +- **Have you read the docs?**[Agents SDK docs](https://openai.github.io/openai-agents-python/) +- **Have you searched for related issues?** Others may have faced similar issues. + +### Describe the bug +A clear and concise description of what the bug is. + +### Debug information +- Agents SDK version: (e.g. `v0.0.3`) +- Python version (e.g. Python 3.10) + +### Repro steps + +Ideally provide a minimal python script that can be run to reproduce the bug. + + +### Expected behavior +A clear and concise description of what you expected to happen. diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 0000000..edd7681 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,16 @@ +--- +name: Feature request +about: Suggest an idea for this project +title: '' +labels: enhancement +assignees: '' + +--- + +### Please read this first + +- **Have you read the docs?**[Agents SDK docs](https://openai.github.io/openai-agents-python/) +- **Have you searched for related issues?** Others may have had similar requesrs + +### Describe the feature +What is the feature you're requesting? How would it work? Please provide examples and details if possible. diff --git a/.github/ISSUE_TEMPLATE/question.md b/.github/ISSUE_TEMPLATE/question.md new file mode 100644 index 0000000..cb4a05d --- /dev/null +++ b/.github/ISSUE_TEMPLATE/question.md @@ -0,0 +1,16 @@ +--- +name: Question +about: Questions about the SDK +title: '' +labels: question +assignees: '' + +--- + +### Please read this first + +- **Have you read the docs?**[Agents SDK docs](https://openai.github.io/openai-agents-python/) +- **Have you searched for related issues?** Others may have had similar requesrs + +### Question +Describe your question. Provide details if available. diff --git a/.github/workflows/issues.yml b/.github/workflows/issues.yml new file mode 100644 index 0000000..fd8f5c1 --- /dev/null +++ b/.github/workflows/issues.yml @@ -0,0 +1,23 @@ +name: Close inactive issues +on: + schedule: + - cron: "30 1 * * *" + +jobs: + close-issues: + runs-on: ubuntu-latest + permissions: + issues: write + pull-requests: write + steps: + - uses: actions/stale@v9 + with: + days-before-issue-stale: 7 + days-before-issue-close: 3 + stale-issue-label: "stale" + stale-issue-message: "This issue is stale because it has been open for 7 days with no activity." + close-issue-message: "This issue was closed because it has been inactive for 3 days since being marked as stale." + days-before-pr-stale: -1 + days-before-pr-close: -1 + any-of-labels: 'question,needs-more-info' + repo-token: ${{ secrets.GITHUB_TOKEN }} diff --git a/README.md b/README.md index 4837009..90fea50 100644 --- a/README.md +++ b/README.md @@ -116,9 +116,9 @@ When you call `Runner.run()`, we run a loop until we get a final output. 1. We call the LLM, using the model and settings on the agent, and the message history. 2. The LLM returns a response, which may include tool calls. -3. If the response has a final output (see below for the more on this), we return it and end the loop. +3. If the response has a final output (see below for more on this), we return it and end the loop. 4. If the response has a handoff, we set the agent to the new agent and go back to step 1. -5. We process the tool calls (if any) and append the tool responses messsages. Then we go to step 1. +5. We process the tool calls (if any) and append the tool responses messages. Then we go to step 1. There is a `max_turns` parameter that you can use to limit the number of times the loop executes. diff --git a/docs/config.md b/docs/config.md index 198d7b7..3cf8373 100644 --- a/docs/config.md +++ b/docs/config.md @@ -10,14 +10,14 @@ from agents import set_default_openai_key set_default_openai_key("sk-...") ``` -Alternatively, you can also configure an OpenAI client to be used. By default, the SDK creates an `AsyncOpenAI` instance, using the API key from the environment variable or the default key set above. You can chnage this by using the [set_default_openai_client()][agents.set_default_openai_client] function. +Alternatively, you can also configure an OpenAI client to be used. By default, the SDK creates an `AsyncOpenAI` instance, using the API key from the environment variable or the default key set above. You can change this by using the [set_default_openai_client()][agents.set_default_openai_client] function. ```python from openai import AsyncOpenAI from agents import set_default_openai_client custom_client = AsyncOpenAI(base_url="...", api_key="...") -set_default_openai_client(client) +set_default_openai_client(custom_client) ``` Finally, you can also customize the OpenAI API that is used. By default, we use the OpenAI Responses API. You can override this to use the Chat Completions API by using the [set_default_openai_api()][agents.set_default_openai_api] function. diff --git a/docs/guardrails.md b/docs/guardrails.md index 2b7369c..caf3277 100644 --- a/docs/guardrails.md +++ b/docs/guardrails.md @@ -21,7 +21,7 @@ Input guardrails run in 3 steps: ## Output guardrails -Output guardrailas run in 3 steps: +Output guardrails run in 3 steps: 1. First, the guardrail receives the same input passed to the agent. 2. Next, the guardrail function runs to produce a [`GuardrailFunctionOutput`][agents.guardrail.GuardrailFunctionOutput], which is then wrapped in an [`OutputGuardrailResult`][agents.guardrail.OutputGuardrailResult] @@ -33,7 +33,7 @@ Output guardrailas run in 3 steps: ## Tripwires -If the input or output fails the guardrail, the Guardrail can signal this with a tripwire. As soon as we see a guardail that has triggered the tripwires, we immediately raise a `{Input,Output}GuardrailTripwireTriggered` exception and halt the Agent execution. +If the input or output fails the guardrail, the Guardrail can signal this with a tripwire. As soon as we see a guardrail that has triggered the tripwires, we immediately raise a `{Input,Output}GuardrailTripwireTriggered` exception and halt the Agent execution. ## Implementing a guardrail diff --git a/docs/index.md b/docs/index.md index ba757c1..8aef657 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,12 +1,12 @@ # OpenAI Agents SDK -The [OpenAI Agents SDK](https://github.com/openai/openai-agents-python) enables you to build agentic AI apps in a lightweight, easy to use package with very few abstractions. It's a production-ready upgrade of our previous experimentation for agents, [Swarm](https://github.com/openai/swarm/tree/main). The Agents SDK has a very small set of primitives: +The [OpenAI Agents SDK](https://github.com/openai/openai-agents-python) enables you to build agentic AI apps in a lightweight, easy-to-use package with very few abstractions. It's a production-ready upgrade of our previous experimentation for agents, [Swarm](https://github.com/openai/swarm/tree/main). The Agents SDK has a very small set of primitives: - **Agents**, which are LLMs equipped with instructions and tools - **Handoffs**, which allow agents to delegate to other agents for specific tasks - **Guardrails**, which enable the inputs to agents to be validated -In combination with Python, these primitives are powerful enough to express complex relationships between tools and agents, and allow you to build real world applications without a steep learning curve. In addition, the SDK comes with built-in **tracing** that lets you visualize and debug your agentic flows, as well as evaluate them and even fine-tune models for your application. +In combination with Python, these primitives are powerful enough to express complex relationships between tools and agents, and allow you to build real-world applications without a steep learning curve. In addition, the SDK comes with built-in **tracing** that lets you visualize and debug your agentic flows, as well as evaluate them and even fine-tune models for your application. ## Why use the Agents SDK diff --git a/docs/models.md b/docs/models.md index 7d2ff1f..7ad515b 100644 --- a/docs/models.md +++ b/docs/models.md @@ -1,6 +1,6 @@ # Models -The Agents SDK comes with out of the box support for OpenAI models in two flavors: +The Agents SDK comes with out-of-the-box support for OpenAI models in two flavors: - **Recommended**: the [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel], which calls OpenAI APIs using the new [Responses API](https://platform.openai.com/docs/api-reference/responses). - The [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel], which calls OpenAI APIs using the [Chat Completions API](https://platform.openai.com/docs/api-reference/chat). @@ -15,7 +15,7 @@ Within a single workflow, you may want to use different models for each agent. F !!!note - While our SDK supports both the [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel] and the[`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel] shapes, we recommend using a single model shape for each workflow because the two shapes support a different set of features and tools. If your workflow requires mixing and matching model shapes, make sure that all the features you're using are available on both. + While our SDK supports both the [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel] and the [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel] shapes, we recommend using a single model shape for each workflow because the two shapes support a different set of features and tools. If your workflow requires mixing and matching model shapes, make sure that all the features you're using are available on both. ```python from agents import Agent, Runner, AsyncOpenAI, OpenAIChatCompletionsModel @@ -48,7 +48,7 @@ async def main(): print(result.final_output) ``` -1. Sets the the name of an OpenAI model directly. +1. Sets the name of an OpenAI model directly. 2. Provides a [`Model`][agents.models.interface.Model] implementation. ## Using other LLM providers diff --git a/docs/multi_agent.md b/docs/multi_agent.md index c118249..aa1b6bc 100644 --- a/docs/multi_agent.md +++ b/docs/multi_agent.md @@ -27,11 +27,11 @@ This pattern is great when the task is open-ended and you want to rely on the in ## Orchestrating via code -While orchestrating via LLM is powerful, orchestrating via LLM makes tasks more deterministic and predictable, in terms of speed, cost and performance. Common patterns here are: +While orchestrating via LLM is powerful, orchestrating via code makes tasks more deterministic and predictable, in terms of speed, cost and performance. Common patterns here are: - Using [structured outputs](https://platform.openai.com/docs/guides/structured-outputs) to generate well formed data that you can inspect with your code. For example, you might ask an agent to classify the task into a few categories, and then pick the next agent based on the category. - Chaining multiple agents by transforming the output of one into the input of the next. You can decompose a task like writing a blog post into a series of steps - do research, write an outline, write the blog post, critique it, and then improve it. - Running the agent that performs the task in a `while` loop with an agent that evaluates and provides feedback, until the evaluator says the output passes certain criteria. - Running multiple agents in parallel, e.g. via Python primitives like `asyncio.gather`. This is useful for speed when you have multiple tasks that don't depend on each other. -We have a number of examples in [`examples/agent_patterns`](https://github.com/openai/openai-agents-python/examples/agent_patterns). +We have a number of examples in [`examples/agent_patterns`](https://github.com/openai/openai-agents-python/tree/main/examples/agent_patterns). diff --git a/docs/quickstart.md b/docs/quickstart.md index 19051f4..f8eca5c 100644 --- a/docs/quickstart.md +++ b/docs/quickstart.md @@ -166,6 +166,9 @@ triage_agent = Agent( ) async def main(): + result = await Runner.run(triage_agent, "who was the first president of the united states?") + print(result.final_output) + result = await Runner.run(triage_agent, "what is life") print(result.final_output) diff --git a/docs/results.md b/docs/results.md index d1864fa..52408d4 100644 --- a/docs/results.md +++ b/docs/results.md @@ -32,7 +32,7 @@ The [`new_items`][agents.result.RunResultBase.new_items] property contains the n - [`MessageOutputItem`][agents.items.MessageOutputItem] indicates a message from the LLM. The raw item is the message generated. - [`HandoffCallItem`][agents.items.HandoffCallItem] indicates that the LLM called the handoff tool. The raw item is the tool call item from the LLM. -- [`HandoffOutputItem`][agents.items.HandoffOutputItem] indicates that a handoff occured. The raw item is the tool response to the handoff tool call. You can also access the source/target agents from the item. +- [`HandoffOutputItem`][agents.items.HandoffOutputItem] indicates that a handoff occurred. The raw item is the tool response to the handoff tool call. You can also access the source/target agents from the item. - [`ToolCallItem`][agents.items.ToolCallItem] indicates that the LLM invoked a tool. - [`ToolCallOutputItem`][agents.items.ToolCallOutputItem] indicates that a tool was called. The raw item is the tool response. You can also access the tool output from the item. - [`ReasoningItem`][agents.items.ReasoningItem] indicates a reasoning item from the LLM. The raw item is the reasoning generated. diff --git a/docs/tracing.md b/docs/tracing.md index fbf2ae4..da0d536 100644 --- a/docs/tracing.md +++ b/docs/tracing.md @@ -16,7 +16,7 @@ The Agents SDK includes built-in tracing, collecting a comprehensive record of e - `trace_id`: A unique ID for the trace. Automatically generated if you don't pass one. Must have the format `trace_<32_alphanumeric>`. - `group_id`: Optional group ID, to link multiple traces from the same conversation. For example, you might use a chat thread ID. - `disabled`: If True, the trace will not be recorded. - - `metadata`: Optiona metadata for the trace. + - `metadata`: Optional metadata for the trace. - **Spans** represent operations that have a start and end time. Spans have: - `started_at` and `ended_at` timestamps. - `trace_id`, to represent the trace they belong to diff --git a/examples/research_bot/README.md b/examples/research_bot/README.md index 4060983..49fb357 100644 --- a/examples/research_bot/README.md +++ b/examples/research_bot/README.md @@ -21,5 +21,5 @@ If you're building your own research bot, some ideas to add to this are: 1. Retrieval: Add support for fetching relevant information from a vector store. You could use the File Search tool for this. 2. Image and file upload: Allow users to attach PDFs or other files, as baseline context for the research. -3. More planning and thinking: Models often produce better results given more time to think. Improve the planning process to come up with a better plan, and add an evaluation step so that the model can choose to improve it's results, search for more stuff, etc. +3. More planning and thinking: Models often produce better results given more time to think. Improve the planning process to come up with a better plan, and add an evaluation step so that the model can choose to improve its results, search for more stuff, etc. 4. Code execution: Allow running code, which is useful for data analysis. diff --git a/examples/tools/computer_use.py b/examples/tools/computer_use.py index ae33955..832255e 100644 --- a/examples/tools/computer_use.py +++ b/examples/tools/computer_use.py @@ -1,6 +1,5 @@ import asyncio import base64 -import logging from typing import Literal, Union from playwright.async_api import Browser, Page, Playwright, async_playwright @@ -16,8 +15,10 @@ from agents import ( trace, ) -logging.getLogger("openai.agents").setLevel(logging.DEBUG) -logging.getLogger("openai.agents").addHandler(logging.StreamHandler()) +# Uncomment to see very verbose logs +# import logging +# logging.getLogger("openai.agents").setLevel(logging.DEBUG) +# logging.getLogger("openai.agents").addHandler(logging.StreamHandler()) async def main(): diff --git a/pyproject.toml b/pyproject.toml index 17265e7..0dec7a5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "openai-agents" -version = "0.0.2" +version = "0.0.3" description = "OpenAI Agents SDK" readme = "README.md" requires-python = ">=3.9" @@ -9,7 +9,7 @@ authors = [ { name = "OpenAI", email = "support@openai.com" }, ] dependencies = [ - "openai>=1.66.0", + "openai>=1.66.2", "pydantic>=2.10, <3", "griffe>=1.5.6, <2", "typing-extensions>=4.12.2, <5", diff --git a/src/agents/_run_impl.py b/src/agents/_run_impl.py index 112819c..2c84950 100644 --- a/src/agents/_run_impl.py +++ b/src/agents/_run_impl.py @@ -23,7 +23,7 @@ from openai.types.responses.response_computer_tool_call import ( ActionWait, ) from openai.types.responses.response_input_param import ComputerCallOutput -from openai.types.responses.response_output_item import Reasoning +from openai.types.responses.response_reasoning_item import ResponseReasoningItem from . import _utils from .agent import Agent @@ -167,7 +167,7 @@ class RunImpl: agent: Agent[TContext], # The original input to the Runner original_input: str | list[TResponseInputItem], - # Eveything generated by Runner since the original input, but before the current step + # Everything generated by Runner since the original input, but before the current step pre_step_items: list[RunItem], new_response: ModelResponse, processed_response: ProcessedResponse, @@ -288,7 +288,7 @@ class RunImpl: items.append(ToolCallItem(raw_item=output, agent=agent)) elif isinstance(output, ResponseFunctionWebSearch): items.append(ToolCallItem(raw_item=output, agent=agent)) - elif isinstance(output, Reasoning): + elif isinstance(output, ResponseReasoningItem): items.append(ReasoningItem(raw_item=output, agent=agent)) elif isinstance(output, ResponseComputerToolCall): items.append(ToolCallItem(raw_item=output, agent=agent)) diff --git a/src/agents/items.py b/src/agents/items.py index bbaf49d..ffbeba0 100644 --- a/src/agents/items.py +++ b/src/agents/items.py @@ -19,7 +19,7 @@ from openai.types.responses import ( ResponseStreamEvent, ) from openai.types.responses.response_input_item_param import ComputerCallOutput, FunctionCallOutput -from openai.types.responses.response_output_item import Reasoning +from openai.types.responses.response_reasoning_item import ResponseReasoningItem from pydantic import BaseModel from typing_extensions import TypeAlias @@ -136,10 +136,10 @@ class ToolCallOutputItem(RunItemBase[Union[FunctionCallOutput, ComputerCallOutpu @dataclass -class ReasoningItem(RunItemBase[Reasoning]): +class ReasoningItem(RunItemBase[ResponseReasoningItem]): """Represents a reasoning item.""" - raw_item: Reasoning + raw_item: ResponseReasoningItem """The raw reasoning item.""" type: Literal["reasoning_item"] = "reasoning_item" diff --git a/src/agents/models/openai_responses.py b/src/agents/models/openai_responses.py index a10d7b9..e060fb8 100644 --- a/src/agents/models/openai_responses.py +++ b/src/agents/models/openai_responses.py @@ -361,7 +361,7 @@ class Converter: includes = "file_search_call.results" if tool.include_search_results else None elif isinstance(tool, ComputerTool): converted_tool = { - "type": "computer-preview", + "type": "computer_use_preview", "environment": tool.computer.environment, "display_width": tool.computer.dimensions[0], "display_height": tool.computer.dimensions[1], diff --git a/src/agents/tracing/processors.py b/src/agents/tracing/processors.py index 282bc23..308adf2 100644 --- a/src/agents/tracing/processors.py +++ b/src/agents/tracing/processors.py @@ -78,9 +78,6 @@ class BackendSpanExporter(TracingExporter): logger.warning("OPENAI_API_KEY is not set, skipping trace export") return - traces: list[dict[str, Any]] = [] - spans: list[dict[str, Any]] = [] - data = [item.export() for item in items if item.export()] payload = {"data": data} @@ -100,7 +97,7 @@ class BackendSpanExporter(TracingExporter): # If the response is successful, break out of the loop if response.status_code < 300: - logger.debug(f"Exported {len(traces)} traces, {len(spans)} spans") + logger.debug(f"Exported {len(items)} items") return # If the response is a client error (4xx), we wont retry diff --git a/tests/test_items_helpers.py b/tests/test_items_helpers.py index 64e2dcd..90fe647 100644 --- a/tests/test_items_helpers.py +++ b/tests/test_items_helpers.py @@ -13,12 +13,12 @@ from openai.types.responses.response_function_tool_call import ResponseFunctionT from openai.types.responses.response_function_tool_call_param import ResponseFunctionToolCallParam from openai.types.responses.response_function_web_search import ResponseFunctionWebSearch from openai.types.responses.response_function_web_search_param import ResponseFunctionWebSearchParam -from openai.types.responses.response_input_item_param import Reasoning as ReasoningInputParam -from openai.types.responses.response_output_item import Reasoning, ReasoningContent from openai.types.responses.response_output_message import ResponseOutputMessage from openai.types.responses.response_output_message_param import ResponseOutputMessageParam from openai.types.responses.response_output_refusal import ResponseOutputRefusal from openai.types.responses.response_output_text import ResponseOutputText +from openai.types.responses.response_reasoning_item import ResponseReasoningItem, Summary +from openai.types.responses.response_reasoning_item_param import ResponseReasoningItemParam from agents import ( Agent, @@ -129,7 +129,7 @@ def test_text_message_outputs_across_list_of_runitems() -> None: item1: RunItem = MessageOutputItem(agent=Agent(name="test"), raw_item=message1) item2: RunItem = MessageOutputItem(agent=Agent(name="test"), raw_item=message2) # Create a non-message run item of a different type, e.g., a reasoning trace. - reasoning = Reasoning(id="rid", content=[], type="reasoning") + reasoning = ResponseReasoningItem(id="rid", summary=[], type="reasoning") non_message_item: RunItem = ReasoningItem(agent=Agent(name="test"), raw_item=reasoning) # Confirm only the message outputs are concatenated. assert ItemHelpers.text_message_outputs([item1, non_message_item, item2]) == "foobar" @@ -266,16 +266,18 @@ def test_to_input_items_for_computer_call_click() -> None: def test_to_input_items_for_reasoning() -> None: """A reasoning output should produce the same dict as a reasoning input item.""" - rc = ReasoningContent(text="why", type="reasoning_summary") - reasoning = Reasoning(id="rid1", content=[rc], type="reasoning") + rc = Summary(text="why", type="summary_text") + reasoning = ResponseReasoningItem(id="rid1", summary=[rc], type="reasoning") resp = ModelResponse(output=[reasoning], usage=Usage(), referenceable_id=None) input_items = resp.to_input_items() assert isinstance(input_items, list) and len(input_items) == 1 converted_dict = input_items[0] - expected: ReasoningInputParam = { + expected: ResponseReasoningItemParam = { "id": "rid1", - "content": [{"text": "why", "type": "reasoning_summary"}], + "summary": [{"text": "why", "type": "summary_text"}], "type": "reasoning", } + print(converted_dict) + print(expected) assert converted_dict == expected diff --git a/tests/test_openai_responses_converter.py b/tests/test_openai_responses_converter.py index 5820426..34cbac5 100644 --- a/tests/test_openai_responses_converter.py +++ b/tests/test_openai_responses_converter.py @@ -163,7 +163,7 @@ def test_convert_tools_basic_types_and_includes(): assert "function" in types assert "file_search" in types assert "web_search_preview" in types - assert "computer-preview" in types + assert "computer_use_preview" in types # Verify file search tool contains max_num_results and vector_store_ids file_params = next(ct for ct in converted.tools if ct["type"] == "file_search") assert file_params.get("max_num_results") == file_tool.max_num_results @@ -173,7 +173,7 @@ def test_convert_tools_basic_types_and_includes(): assert web_params.get("user_location") == web_tool.user_location assert web_params.get("search_context_size") == web_tool.search_context_size # Verify computer tool contains environment and computed dimensions - comp_params = next(ct for ct in converted.tools if ct["type"] == "computer-preview") + comp_params = next(ct for ct in converted.tools if ct["type"] == "computer_use_preview") assert comp_params.get("environment") == "mac" assert comp_params.get("display_width") == 800 assert comp_params.get("display_height") == 600 diff --git a/tests/test_run_step_processing.py b/tests/test_run_step_processing.py index 41f65c4..24f9e8e 100644 --- a/tests/test_run_step_processing.py +++ b/tests/test_run_step_processing.py @@ -7,7 +7,7 @@ from openai.types.responses import ( ResponseFunctionWebSearch, ) from openai.types.responses.response_computer_tool_call import ActionClick -from openai.types.responses.response_output_item import Reasoning, ReasoningContent +from openai.types.responses.response_reasoning_item import ResponseReasoningItem, Summary from pydantic import BaseModel from agents import ( @@ -287,8 +287,8 @@ def test_function_web_search_tool_call_parsed_correctly(): def test_reasoning_item_parsed_correctly(): # Verify that a Reasoning output item is converted into a ReasoningItem. - reasoning = Reasoning( - id="r1", type="reasoning", content=[ReasoningContent(text="why", type="reasoning_summary")] + reasoning = ResponseReasoningItem( + id="r1", type="reasoning", summary=[Summary(text="why", type="summary_text")] ) response = ModelResponse( output=[reasoning], diff --git a/uv.lock b/uv.lock index fd28b2b..c828fa3 100644 --- a/uv.lock +++ b/uv.lock @@ -797,7 +797,7 @@ wheels = [ [[package]] name = "openai" -version = "1.66.0" +version = "1.66.2" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "anyio" }, @@ -809,14 +809,14 @@ dependencies = [ { name = "tqdm" }, { name = "typing-extensions" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/84/c5/3c422ca3ccc81c063955e7c20739d7f8f37fea0af865c4a60c81e6225e14/openai-1.66.0.tar.gz", hash = "sha256:8a9e672bc6eadec60a962f0b40d7d1c09050010179c919ed65322e433e2d1025", size = 396819 } +sdist = { url = "https://files.pythonhosted.org/packages/d8/e1/b3e1fda1aa32d4f40d4de744e91de4de65c854c3e53c63342e4b5f9c5995/openai-1.66.2.tar.gz", hash = "sha256:9b3a843c25f81ee09b6469d483d9fba779d5c6ea41861180772f043481b0598d", size = 397041 } wheels = [ - { url = "https://files.pythonhosted.org/packages/d7/f1/d52960dac9519c9de64593460826a0fe2e19159389ec97ecf3e931d2e6a3/openai-1.66.0-py3-none-any.whl", hash = "sha256:43e4a3c0c066cc5809be4e6aac456a3ebc4ec1848226ef9d1340859ac130d45a", size = 566389 }, + { url = "https://files.pythonhosted.org/packages/2c/6f/3315b3583ffe3e31c55b446cb22d2a7c235e65ca191674fffae62deb3c11/openai-1.66.2-py3-none-any.whl", hash = "sha256:75194057ee6bb8b732526387b6041327a05656d976fc21c064e21c8ac6b07999", size = 567268 }, ] [[package]] name = "openai-agents" -version = "0.0.2" +version = "0.0.3" source = { editable = "." } dependencies = [ { name = "griffe" }, @@ -846,7 +846,7 @@ dev = [ [package.metadata] requires-dist = [ { name = "griffe", specifier = ">=1.5.6,<2" }, - { name = "openai", specifier = ">=1.66.0" }, + { name = "openai", specifier = ">=1.66.2" }, { name = "pydantic", specifier = ">=2.10,<3" }, { name = "requests", specifier = ">=2.0,<3" }, { name = "types-requests", specifier = ">=2.0,<3" },