Merge branch 'openai:main' into freeznet/allow-mcp-customize-handler

Author: freeznet

.github/workflows/issues.yml

@@ -15,13 +15,14 @@ jobs:
           days-before-issue-stale: 7
           days-before-issue-close: 3
           stale-issue-label: "stale"
+          exempt-issue-labels: "skip-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."
           any-of-issue-labels: 'question,needs-more-info'
           days-before-pr-stale: 10
           days-before-pr-close: 7
           stale-pr-label: "stale"
-          exempt-issue-labels: "skip-stale"
+          exempt-pr-labels: "skip-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/update-docs.yml

@@ -5,13 +5,20 @@ name: "Update Translated Docs"
 # 2. When you add a new page to the left-hand menu, **make sure to manually update mkdocs.yml** to include the new item.
 # 3. If you switch to a different LLM (for example, from o3 to a newer model), be sure to conduct thorough testing before making the switch.
 
+# To add more languages, you will update the following:
+# 1.  Add '!docs/{lang}/**' to `on.push.paths` in this file
+# 2. Update mkdocs.yml to have the new language
+# 3. Update docs/scripts/translate_docs.py to have the new language
+
 on:
   push:
     branches:
       - main
     paths:
       - 'docs/**'
       - mkdocs.yml
+      - '!docs/ja/**'
+      - '!docs/ko/**'
 
 permissions:
   contents: write

.gitignore

@@ -144,3 +144,6 @@ cython_debug/
 # PyPI configuration file
 .pypirc
 .aider*
+
+# Redis database files
+dump.rdb

README.md

@@ -31,6 +31,8 @@ pip install openai-agents
 
 For voice support, install with the optional `voice` group: `pip install 'openai-agents[voice]'`.
 
+For Redis session support, install with the optional `redis` group: `pip install 'openai-agents[redis]'`.
+
 ### uv
 
 If you're familiar with [uv](https://docs.astral.sh/uv/), using the tool would be even similar:
@@ -42,6 +44,8 @@ uv add openai-agents
 
 For voice support, install with the optional `voice` group: `uv add 'openai-agents[voice]'`.
 
+For Redis session support, install with the optional `redis` group: `uv add 'openai-agents[redis]'`.
+
 ## Hello world example
 
 ```python
@@ -211,8 +215,13 @@ print(result.final_output)  # "Approximately 39 million"
 ```python
 from agents import Agent, Runner, SQLiteSession
 
-# Custom SQLite database file
+# SQLite - file-based or in-memory database
 session = SQLiteSession("user_123", "conversations.db")
+
+# Redis - for scalable, distributed deployments
+# from agents.extensions.memory import RedisSession
+# session = RedisSession.from_url("user_123", url="redis://localhost:6379/0")
+
 agent = Agent(name="Assistant")
 
 # Different session IDs maintain separate conversation histories

docs/agents.md

@@ -22,7 +22,7 @@ def get_weather(city: str) -> str:
 agent = Agent(
     name="Haiku agent",
     instructions="Always respond in haiku form",
-    model="o3-mini",
+    model="gpt-5-nano",
     tools=[get_weather],
 )
 ```
@@ -163,7 +163,7 @@ By using the `clone()` method on an agent, you can duplicate an Agent, and optio
 pirate_agent = Agent(
     name="Pirate",
     instructions="Write like a pirate",
-    model="o3-mini",
+    model="gpt-4.1",
 )
 
 robot_agent = pirate_agent.clone(

docs/examples.md

@@ -2,44 +2,88 @@
 
 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](https://github.com/openai/openai-agents-python/tree/main/examples/agent_patterns):**
-  Examples in this category illustrate common agent design patterns, such as
+-   **[agent_patterns](https://github.com/openai/openai-agents-python/tree/main/examples/agent_patterns):**
+    Examples in this category illustrate common agent design patterns, such as
+
+    -   Deterministic workflows
+    -   Agents as tools
+    -   Parallel agent execution
+    -   Conditional tool usage
+    -   Input/output guardrails
+    -   LLM as a judge
+    -   Routing
+    -   Streaming guardrails
+
+-   **[basic](https://github.com/openai/openai-agents-python/tree/main/examples/basic):**
+    These examples showcase foundational capabilities of the SDK, such as
+
+    -   Hello world examples (Default model, GPT-5, open-weight model)
+    -   Agent lifecycle management
+    -   Dynamic system prompts
+    -   Streaming outputs (text, items, function call args)
+    -   Prompt templates
+    -   File handling (local and remote, images and PDFs)
+    -   Usage tracking
+    -   Non-strict output types
+    -   Previous response ID usage
+
+-   **[customer_service](https://github.com/openai/openai-agents-python/tree/main/examples/customer_service):**
+    Example customer service system for an airline.
+
+-   **[financial_research_agent](https://github.com/openai/openai-agents-python/tree/main/examples/financial_research_agent):**
+    A financial research agent that demonstrates structured research workflows with agents and tools for financial data analysis.
+
+-   **[handoffs](https://github.com/openai/openai-agents-python/tree/main/examples/handoffs):**
+    See practical examples of agent handoffs with message filtering.
+
+-   **[hosted_mcp](https://github.com/openai/openai-agents-python/tree/main/examples/hosted_mcp):**
+    Examples demonstrating how to use hosted MCP (Model Context Protocol) connectors and approvals.
+
+-   **[mcp](https://github.com/openai/openai-agents-python/tree/main/examples/mcp):**
+    Learn how to build agents with MCP (Model Context Protocol), including:
 
-    - Deterministic workflows
-    - Agents as tools
-    - Parallel agent execution
+    -   Filesystem examples
+    -   Git examples
+    -   MCP prompt server examples
+    -   SSE (Server-Sent Events) examples
+    -   Streamable HTTP examples
 
-- **[basic](https://github.com/openai/openai-agents-python/tree/main/examples/basic):**
-  These examples showcase foundational capabilities of the SDK, such as
+-   **[memory](https://github.com/openai/openai-agents-python/tree/main/examples/memory):**
+    Examples of different memory implementations for agents, including:
 
-    - Dynamic system prompts
-    - Streaming outputs
-    - Lifecycle events
+    -   SQLite session storage
+    -   Advanced SQLite session storage
+    -   Redis session storage
+    -   SQLAlchemy session storage
+    -   Encrypted session storage
+    -   OpenAI session storage
 
-- **[tool examples](https://github.com/openai/openai-agents-python/tree/main/examples/tools):**
-  Learn how to implement OAI hosted tools such as web search and file search,
-   and integrate them into your agents.
+-   **[model_providers](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers):**
+    Explore how to use non-OpenAI models with the SDK, including custom providers and LiteLLM integration.
 
-- **[model providers](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers):**
-  Explore how to use non-OpenAI models with the SDK.
+-   **[realtime](https://github.com/openai/openai-agents-python/tree/main/examples/realtime):**
+    Examples showing how to build real-time experiences using the SDK, including:
 
-- **[handoffs](https://github.com/openai/openai-agents-python/tree/main/examples/handoffs):**
-  See practical examples of agent handoffs.
+    -   Web applications
+    -   Command-line interfaces
+    -   Twilio integration
 
-- **[mcp](https://github.com/openai/openai-agents-python/tree/main/examples/mcp):**
-  Learn how to build agents with MCP.
+-   **[reasoning_content](https://github.com/openai/openai-agents-python/tree/main/examples/reasoning_content):**
+    Examples demonstrating how to work with reasoning content and structured outputs.
 
-- **[customer_service](https://github.com/openai/openai-agents-python/tree/main/examples/customer_service)** and **[research_bot](https://github.com/openai/openai-agents-python/tree/main/examples/research_bot):**
-  Two more built-out examples that illustrate real-world applications
+-   **[research_bot](https://github.com/openai/openai-agents-python/tree/main/examples/research_bot):**
+    Simple deep research clone that demonstrates complex multi-agent research workflows.
 
-    - **customer_service**: Example customer service system for an airline.
-    - **research_bot**: Simple deep research clone.
+-   **[tools](https://github.com/openai/openai-agents-python/tree/main/examples/tools):**
+    Learn how to implement OAI hosted tools such as:
 
-- **[voice](https://github.com/openai/openai-agents-python/tree/main/examples/voice):**
-  See examples of voice agents, using our TTS and STT models.
+    -   Web search and web search with filters
+    -   File search
+    -   Code interpreter
+    -   Computer use
+    -   Image generation
 
-- **[realtime](https://github.com/openai/openai-agents-python/tree/main/examples/realtime):**
-  Examples showing how to build realtime experiences using the SDK.
+-   **[voice](https://github.com/openai/openai-agents-python/tree/main/examples/voice):**
+    See examples of voice agents, using our TTS and STT models, including streamed voice examples.

docs/guardrails.md

@@ -151,4 +151,4 @@ async def main():
 1. This is the actual agent's output type.
 2. This is the guardrail's output type.
 3. This is the guardrail function that receives the agent's output, and returns the result.
-4. This is the actual agent that defines the workflow.
+4. This is the actual agent that defines the workflow.