Merge branch 'main' of https://github.com/openai/openai-agents-python into feat/draw_graph

Author: MartinEBravo

.github/workflows/issues.yml

@@ -17,7 +17,10 @@ jobs:
           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'
+          any-of-issue-labels: 'question,needs-more-info'
+          days-before-pr-stale: 10
+          days-before-pr-close: 7
+          stale-pr-label: "stale"
+          stale-pr-message: "This PR is stale because it has been open for 10 days with no activity."
+          close-pr-message: "This PR was closed because it has been inactive for 7 days since being marked as stale."
           repo-token: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/tests.yml

@@ -8,6 +8,9 @@ on:
     branches:
       - main
 
+env:
+  UV_FROZEN: "1"
+
 jobs:
   lint:
     runs-on: ubuntu-latest
@@ -50,8 +53,8 @@ jobs:
           enable-cache: true
       - name: Install dependencies
         run: make sync
-      - name: Run tests
-        run: make tests
+      - name: Run tests with coverage
+        run: make coverage
 
   build-docs:
     runs-on: ubuntu-latest

.gitignore

@@ -135,7 +135,7 @@ dmypy.json
 cython_debug/
 
 # PyCharm
-#.idea/
+.idea/
 
 # Ruff stuff:
 .ruff_cache/

Makefile

@@ -5,6 +5,7 @@ sync:
 .PHONY: format
 format: 
 	uv run ruff format
+	uv run ruff check --fix
 
 .PHONY: lint
 lint: 
@@ -18,6 +19,13 @@ mypy:
 tests: 
 	uv run pytest 
 
+.PHONY: coverage
+coverage:
+	
+	uv run coverage run -m pytest
+	uv run coverage xml -o coverage.xml
+	uv run coverage report -m --fail-under=95
+
 .PHONY: snapshots-fix
 snapshots-fix: 
 	uv run pytest --inline-snapshot=fix 
@@ -29,7 +37,6 @@ snapshots-create:
 .PHONY: old_version_tests
 old_version_tests: 
 	UV_PROJECT_ENVIRONMENT=.venv_39 uv run --python 3.9 -m pytest
-	UV_PROJECT_ENVIRONMENT=.venv_39 uv run --python 3.9 -m mypy .
 
 .PHONY: build-docs
 build-docs:
@@ -42,4 +49,6 @@ serve-docs:
 .PHONY: deploy-docs
 deploy-docs:
 	uv run mkdocs gh-deploy --force --verbose
+
+	
 

README.md

@@ -30,6 +30,8 @@ source env/bin/activate
 pip install openai-agents
 ```
 
+For voice support, install with the optional `voice` group: `pip install 'openai-agents[voice]'`.
+
 ## Hello world example
 
 ```python

docs/agents.md

@@ -130,3 +130,23 @@ robot_agent = pirate_agent.clone(
     instructions="Write like a robot",
 )
 ```
+
+## Forcing tool use
+
+Supplying a list of tools doesn't always mean the LLM will use a tool. You can force tool use by setting [`ModelSettings.tool_choice`][agents.model_settings.ModelSettings.tool_choice]. Valid values are:
+
+1. `auto`, which allows the LLM to decide whether or not to use a tool.
+2. `required`, which requires the LLM to use a tool (but it can intelligently decide which tool).
+3. `none`, which requires the LLM to _not_ use a tool.
+4. Setting a specific string e.g. `my_tool`, which requires the LLM to use that specific tool.
+
+!!! note
+
+    To prevent infinite loops, the framework automatically resets `tool_choice` to "auto" after a tool call in the following scenarios:
+    
+    1. When `tool_choice` is set to a specific function name (any string that's not "auto", "required", or "none")
+    2. When `tool_choice` is set to "required" AND there is only one tool available
+    
+    This targeted reset mechanism allows the model to decide whether to make additional tool calls in subsequent turns while avoiding infinite loops in these specific cases.
+    
+    If you want the Agent to completely stop after a tool call (rather than continuing with auto mode), you can set [`Agent.tool_use_behavior="stop_on_first_tool"`] which will directly use the tool output as the final response without further LLM processing.

docs/context.md

@@ -41,14 +41,14 @@ async def fetch_user_age(wrapper: RunContextWrapper[UserInfo]) -> str:  # (2)!
     return f"User {wrapper.context.name} is 47 years old"
 
 async def main():
-    user_info = UserInfo(name="John", uid=123)  # (3)!
+    user_info = UserInfo(name="John", uid=123)
 
-    agent = Agent[UserInfo](  # (4)!
+    agent = Agent[UserInfo](  # (3)!
         name="Assistant",
         tools=[fetch_user_age],
     )
 
-    result = await Runner.run(
+    result = await Runner.run(  # (4)!
         starting_agent=agent,
         input="What is the age of the user?",
         context=user_info,

docs/examples.md

@@ -0,0 +1,36 @@
+# Examples
+
+Check out a variety of sample implementations of the SDK in the examples section of the [repo](https://github.com/openai/openai-agents-python/tree/main/examples). The examples are organized into several categories that demonstrate different patterns and capabilities.
+
+
+## Categories
+
+- **agent_patterns:**
+  Examples in this category illustrate common agent design patterns, such as
+  
+    - Deterministic workflows
+    - Agents as tools
+    - Parallel agent execution
+
+- **basic:**
+  These examples showcase foundational capabilities of the SDK, such as
+  
+    - Dynamic system prompts
+    - Streaming outputs
+    - Lifecycle events
+
+- **tool examples:**
+  Learn how to implement OAI hosted tools such as web search and file search,
+   and integrate them into your agents.
+
+- **model providers:**
+  Explore how to use non-OpenAI models with the SDK.
+
+- **handoffs:**
+  See practical examples of agent handoffs.
+
+- **customer_service** and **research_bot:**
+  Two more built-out examples that illustrate real-world applications
+  
+    - **customer_service**: Example customer service system for an airline.
+    - **research_bot**: Simple deep research clone.

docs/guardrails.md

@@ -29,7 +29,7 @@ Output guardrails run in 3 steps:
 
 !!! Note
 
-    Output guardrails are intended to run on the final agent input, so an agent's guardrails only run if the agent is the *last* agent. Similar to the input guardrails, we do this because guardrails tend to be related to the actual Agent - you'd run different guardrails for different agents, so colocating the code is useful for readability.
+    Output guardrails are intended to run on the final agent output, so an agent's guardrails only run if the agent is the *last* agent. Similar to the input guardrails, we do this because guardrails tend to be related to the actual Agent - you'd run different guardrails for different agents, so colocating the code is useful for readability.
 
 ## Tripwires
 
@@ -111,8 +111,8 @@ class MessageOutput(BaseModel): # (1)!
     response: str
 
 class MathOutput(BaseModel): # (2)!
-    is_math: bool
     reasoning: str
+    is_math: bool
 
 guardrail_agent = Agent(
     name="Guardrail check",

docs/ref/voice/events.md

@@ -0,0 +1,3 @@
+# `Events`
+
+::: agents.voice.events