agent-framework/dotnet/samples/02-agents/Agents

Getting started with agents

The getting started with agents samples demonstrate the fundamental concepts and functionalities of single agents and can be used with any agent type.

While the functionality can be used with any agent type, these samples use Azure OpenAI as the AI provider and use ChatCompletion as the type of service.

For other samples that demonstrate how to create and configure each type of agent that come with the agent framework, see the How to create an agent for each provider samples.

Getting started with agents prerequisites

Before you begin, ensure you have the following prerequisites:

• .NET 10 SDK or later
• Azure OpenAI service endpoint and deployment configured
• Azure CLI installed and authenticated (for Azure credential authentication)
• User has the Cognitive Services OpenAI Contributor role for the Azure OpenAI resource.

Note: These samples use Azure OpenAI models. For more information, see how to deploy Azure OpenAI models with Microsoft Foundry.

Note: These samples use Azure CLI credentials for authentication. Make sure you're logged in with az login and have access to the Azure OpenAI resource and have the Cognitive Services OpenAI Contributor role. For more information, see the Azure CLI documentation.

Samples

Sample	Description
Using OpenAPI function tools with a simple agent	This sample demonstrates how to create function tools from an OpenAPI spec and use them with a simple agent (note that this sample is in the Semantic Kernel repository)
Using function tools with approvals	This sample demonstrates how to use function tools where approvals require human in the loop approvals before execution
Structured output with a simple agent	This sample demonstrates how to use structured output with a simple agent
Persisted conversations with a simple agent	This sample demonstrates how to persist conversations and reload them later. This is useful for cases where an agent is hosted in a stateless service
3rd party chat history storage with a simple agent	This sample demonstrates how to store chat history in a 3rd party storage solution
Observability with a simple agent	This sample demonstrates how to add telemetry to a simple agent
Dependency injection with a simple agent	This sample demonstrates how to add and resolve an agent with a dependency injection container
Exposing a simple agent as MCP tool	This sample demonstrates how to expose an agent as an MCP tool
Using images with a simple agent	This sample demonstrates how to use image multi-modality with an AI agent
Exposing a simple agent as a function tool	This sample demonstrates how to expose an agent as a function tool
Background responses with tools and persistence	This sample demonstrates advanced background response scenarios including function calling during background operations and state persistence
Using middleware with an agent	This sample demonstrates how to use middleware with an agent
Using plugins with an agent	This sample demonstrates how to use plugins with an agent
Reducing chat history size	This sample demonstrates how to reduce the chat history to constrain its size, where chat history is maintained locally
Background responses	This sample demonstrates how to use background responses for long-running operations with polling and resumption support
Deep research with an agent	This sample demonstrates how to use the Deep Research Tool to perform comprehensive research on complex topics
Declarative agent	This sample demonstrates how to declaratively define an agent.
Providing additional AI Context to an agent using multiple AIContextProviders	This sample demonstrates how to inject additional AI context into a ChatClientAgent using multiple custom AIContextProvider components that are attached to the agent.
Using compaction pipeline with an agent	This sample demonstrates how to use a compaction pipeline to efficiently limit the size of the conversation history for an agent.
In-function-loop checkpointing	This sample demonstrates how to persist chat history after each service call during a tool-calling loop, enabling crash recovery and mid-run observability.

Running the samples from the console

To run the samples, navigate to the desired sample directory, e.g.

cd Agent_Step01_UsingFunctionToolsWithApprovals

Set the following environment variables:

$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/" # Replace with your Azure OpenAI resource endpoint
$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini

If the variables are not set, you will be prompted for the values when running the samples.

Execute the following command to build the sample:

dotnet build

Execute the following command to run the sample:

dotnet run --no-build

Or just build and run in one step:

dotnet run

Running the samples from Visual Studio

Open the solution in Visual Studio and set the desired sample project as the startup project. Then, run the project using the built-in debugger or by pressing F5.

You will be prompted for any required environment variables if they are not already set.