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Semantic Kernel Agents - Getting Started

This project contains a step by step guide to get started with Semantic Kernel Agents in Python.

PyPI:

  • For the use of Chat Completion agents, the minimum allowed Semantic Kernel pypi version is 1.3.0.
  • For the use of OpenAI Assistant agents, the minimum allowed Semantic Kernel pypi version is 1.4.0.
  • For the use of Agent Group Chat, the minimum allowed Semantic kernel pypi version is 1.6.0.
  • For the use of Streaming OpenAI Assistant agents, the minimum allowed Semantic Kernel pypi version is 1.11.0
  • For the use of OpenAI Responses agents, the minimum allowed Semantic Kernel pypi version is 1.27.0.

Source

Examples

The getting started with agents examples include:

Chat Completion

Example Description
step1_chat_completion_agent_simple How to create and use a simple chat completion agent.
step2_chat_completion_agent_thread_management How to create and use a chat completion with a thread.
step3_chat_completion_agent_with_kernel How to create and use a a chat completion agent with the AI service created on the kernel.
step4_chat_completion_agent_plugin_simple How to create a simple chat completion agent and specify plugins via the constructor with a kernel.
step5_chat_completion_agent_plugin_with_kernel How to create and use a chat completion agent by registering plugins on the kernel.
step6_chat_completion_agent_group_chat How to create a conversation between agents.
step7_kernel_function_strategies How to utilize a KernelFunction as a chat strategy.
step8_chat_completion_agent_json_result How to have an agent produce JSON.
step9_chat_completion_agent_logging How to enable logging for agents.
step10_chat_completion_agent_structured_outputs How to use have a chat completion agent use structured outputs

Azure AI Agent

Example Description
step1_azure_ai_agent How to create an Azure AI Agent and invoke a Semantic Kernel plugin.
step2_azure_ai_agent_plugin How to create an Azure AI Agent with plugins.
step3_azure_ai_agent_group_chat How to create an agent group chat with Azure AI Agents.
step4_azure_ai_agent_code_interpreter How to use the code-interpreter tool for an Azure AI agent.
step5_azure_ai_agent_file_search How to use the file-search tool for an Azure AI agent.
step6_azure_ai_agent_openapi How to use the Open API tool for an Azure AI agent.

Note: For details on configuring an Azure AI Agent, please see here.

OpenAI Assistant Agent

Example Description
step1_assistant How to create and use an OpenAI Assistant agent.
step2_assistant_plugins How to create and use an OpenAI Assistant agent with plugins.
step3_assistant_vision How to provide an image as input to an OpenAI Assistant agent.
step4_assistant_tool_code_interpreter How to use the code-interpreter tool for an OpenAI Assistant agent.
step5_assistant_tool_file_search How to use the file-search tool for an OpenAI Assistant agent.

OpenAI Responses Agent

Example Description
step1_responses_agent How to create and use an OpenAI Responses agent in the most simple way.
step2_responses_agent_thread_management How to create and use a ResponsesAgentThread agent to maintain conversation context.
step3_responses_agent_plugins How to create and use an OpenAI Responses agent with plugins.
step4_responses_agent_web_search How to use the web search preview tool with an OpenAI Responses agent.
step5_responses_agent_file_search How to use the file-search tool with an OpenAI Responses agent.
step6_responses_agent_vision How to provide an image as input to an OpenAI Responses agent.
step7_responses_agent_structured_outputs How to use have an OpenAI Responses agent use structured outputs.

Configuring the Kernel

Similar to the Semantic Kernel Python concept samples, it is necessary to configure the secrets and keys used by the kernel. See the follow "Configuring the Kernel" guide for more information.

Running Concept Samples

Concept samples can be run in an IDE or via the command line. After setting up the required api key for your AI connector, the samples run without any extra command line arguments.