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python/samples/getting_started_with_agents/openai_assistant/README.md

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+## OpenAI Assistant Agents
+
+The following getting started samples show how to use OpenAI Assistant agents with Semantic Kernel.
+
+## Assistants API Overview
+
+The Assistants API is a robust solution from OpenAI that empowers developers to integrate powerful, purpose-built AI assistants into their applications. It streamlines the development process by handling conversation histories, managing threads, and providing seamless access to advanced tools.
+
+### Key Features
+
+- **Purpose-Built AI Assistants:**  
+  Assistants are specialized AIs that leverage OpenAI’s models to interact with users, access files, maintain persistent threads, and call additional tools. This enables highly tailored and effective user interactions.
+
+- **Simplified Conversation Management:**  
+  The concept of a **thread** -- a dedicated conversation session between an assistant and a user -- ensures that message history is managed automatically. Threads optimize the conversation context by storing and truncating messages as needed.
+
+- **Integrated Tool Access:**  
+  The API provides built-in tools such as:
+  - **Code Interpreter:** Allows the assistant to execute code, enhancing its ability to solve complex tasks.
+  - **File Search:** Implements best practices for retrieving data from uploaded files, including advanced chunking and embedding techniques.
+
+- **Enhanced Function Calling:**  
+  With improved support for third-party tool integration, the Assistants API enables assistants to extend their capabilities beyond native functions.
+
+For more detailed technical information, refer to the [Assistants API](https://platform.openai.com/docs/assistants/overview).
+
+### Semantic Kernel OpenAI Assistant Agents
+
+OpenAI Assistant Agents are created in the following way:
+
+```python
+from semantic_kernel.agents import OpenAIAssistantAgent
+from semantic_kernel.connectors.ai.open_ai import OpenAISettings
+
+# Create the client using OpenAI resources and configuration
+client = OpenAIAssistantAgent.create_client()
+
+# Create the assistant definition
+definition = await client.beta.assistants.create(
+    model=OpenAISettings().chat_model_id,
+    instructions="<instructions>",
+    name="<name>",
+)
+
+# Define the Semantic Kernel OpenAI Assistant Agent
+agent = OpenAIAssistantAgent(
+    client=client,
+    definition=definition,
+)
+
+# Define a thread to hold the conversation's context
+# If a thread is not created initially it will be created
+# and returned as part of the first response
+thread = None
+
+# Get the agent response
+response = await agent.get_response(messages="Why is the sky blue?", thread=thread)
+thread = response.thread
+
+# or use the agent.invoke(...) method
+async for response in agent.invoke(messages="Why is the sky blue?", thread=thread):
+    print(f"# {response.role}: {response.content}")
+    thread = response.thread
+```
+
+### Semantic Kernel Azure Assistant Agents
+
+Azure Assistant Agents are currently in preview and require a `-preview` API version (minimum version: `2024-05-01-preview`). As new features are introduced, API versions will be updated accordingly. For the latest versioning details, please refer to the [Azure OpenAI API preview lifecycle](https://learn.microsoft.com/azure/ai-services/openai/api-version-deprecation).
+
+To specify the correct API version, set the following environment variable (for example, in your `.env` file):
+
+```bash
+AZURE_OPENAI_API_VERSION="2025-01-01-preview"
+```
+
+Alternatively, you can pass the `api_version` parameter when creating an `AzureAssistantAgent`:
+
+```python
+from semantic_kernel.agents import AzureAssistantAgent
+
+# Create the client using Azure OpenAI resources and configuration
+client = AzureAssistantAgent.create_client()
+
+# Create the assistant definition
+definition = await client.beta.assistants.create(
+    model=model,
+    instructions="<instructions>",
+    name="<name>",
+)
+
+# Define the Semantic Kernel Azure OpenAI Assistant Agent
+agent = AzureAssistantAgent(
+    client=client,
+    definition=definition,
+)
+
+# Define a thread to hold the conversation's context
+# If a thread is not created initially it will be created
+# and returned as part of the first response
+thread = None
+
+# Get the agent response
+response = await agent.get_response(messages="Why is the sky blue?", thread=thread)
+thread = response.thread
+
+# or use the agent.invoke(...) method
+async for response in agent.invoke(messages="Why is the sky blue?", thread=thread):
+    print(f"# {response.role}: {response.content}")
+    thread = response.thread
+```