wehub-resource-syncOpenAI 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.
Semantic Kernel OpenAI Assistant Agents
OpenAI Assistant Agents are created in the following way:
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.
To specify the correct API version, set the following environment variable (for example, in your .env file):
AZURE_OPENAI_API_VERSION="2025-01-01-preview"
Alternatively, you can pass the api_version parameter when creating an AzureAssistantAgent:
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