Running Agents

The base Agent abstraction exposes various options for running the agent. Callers can choose to supply zero, one or many input messages. Callers can also choose between streaming and non-streaming. Let's dig into the different usage scenarios.

Streaming and non-streaming

The Microsoft Agent Framework supports both streaming and non-streaming methods for running an agent.

For non-streaming, use the RunAsync method.

Console.WriteLine(await agent.RunAsync("What is the weather like in Amsterdam?"));

For streaming, use the RunStreamingAsync method.

await foreach (var update in agent.RunStreamingAsync("What is the weather like in Amsterdam?"))
{
    Console.Write(update);
}

For non-streaming, use the run method.

result = await agent.run("What is the weather like in Amsterdam?")
print(result.text)

For streaming, use the run_stream method.

async for update in agent.run_stream("What is the weather like in Amsterdam?"):
    if update.text:
        print(update.text, end="", flush=True)

Agent run options

The base agent abstraction does allow passing an options object for each agent run, however the ability to customize a run at the abstraction level is quite limited. Agents can vary significantly and therefore there aren't really common customization options.

For cases where the caller knows the type of the agent they are working with, it is possible to pass type specific options to allow customizing the run.

For example, here the agent is a ChatClientAgent and it is possible to pass a ChatClientAgentRunOptions object that inherits from AgentRunOptions. This allows the caller to provide custom ChatOptions that are merged with any agent level options before being passed to the IChatClient that the ChatClientAgent is built on.

var chatOptions = new ChatOptions() { Tools = [AIFunctionFactory.Create(GetWeather)] };
Console.WriteLine(await agent.RunAsync("What is the weather like in Amsterdam?", options: new ChatClientAgentRunOptions(chatOptions)));

Python agents support passing keyword arguments to customize each run. The specific options available depend on the agent type, but ChatAgent supports many chat client parameters that can be passed to both run and run_stream methods.

Common options for ChatAgent include:

max_tokens: Maximum number of tokens to generate
temperature: Controls randomness in response generation
model: Override the model for this specific run
tools: Add additional tools for this run only
response_format: Specify the response format (e.g., structured output)

# Run with custom options
result = await agent.run(
    "What is the weather like in Amsterdam?",
    temperature=0.3,
    max_tokens=150,
    model="gpt-4o"
)

# Streaming with custom options
async for update in agent.run_stream(
    "Tell me a detailed weather forecast",
    temperature=0.7,
    tools=[additional_weather_tool]
):
    if update.text:
        print(update.text, end="", flush=True)

When both agent-level defaults and run-level options are provided, the run-level options take precedence.

Response types

Both streaming and non-streaming responses from agents contain all content produced by the agent. Content may include data that is not the result (i.e. the answer to the user question) from the agent. Examples of other data returned include function tool calls, results from function tool calls, reasoning text, status updates, and many more.

Since not all content returned is the result, it's important to look for specific content types when trying to isolate the result from the other content.

To extract the text result from a response, all TextContent items from all ChatMessages items need to be aggregated. To simplify this, we provide a Text property on all response types that aggregates all TextContent.

For the non-streaming case, everything is returned in one AgentRunResponse object. AgentRunResponse allows access to the produced messages via the Messages property.

var response = await agent.RunAsync("What is the weather like in Amsterdam?");
Console.WriteLine(response.Text);
Console.WriteLine(response.Messages.Count);

For the streaming case, AgentRunResponseUpdate objects are streamed as they are produced. Each update may contain a part of the result from the agent, and also various other content items. Similar to the non-streaming case, it is possible to use the Text property to get the portion of the result contained in the update, and drill into the detail via the Contents property.

await foreach (var update in agent.RunStreamingAsync("What is the weather like in Amsterdam?"))
{
    Console.WriteLine(update.Text);
    Console.WriteLine(update.Contents.Count);
}

For the non-streaming case, everything is returned in one AgentRunResponse object. AgentRunResponse allows access to the produced messages via the messages property.

To extract the text result from a response, all TextContent items from all ChatMessage items need to be aggregated. To simplify this, we provide a text property on all response types that aggregates all TextContent.

response = await agent.run("What is the weather like in Amsterdam?")
print(response.text)
print(len(response.messages))

# Access individual messages
for message in response.messages:
    print(f"Role: {message.role}, Text: {message.text}")

For the streaming case, AgentRunResponseUpdate objects are streamed as they are produced. Each update may contain a part of the result from the agent, and also various other content items. Similar to the non-streaming case, it is possible to use the text property to get the portion of the result contained in the update, and drill into the detail via the contents property.

async for update in agent.run_stream("What is the weather like in Amsterdam?"):
    print(f"Update text: {update.text}")
    print(f"Content count: {len(update.contents)}")

    # Access individual content items
    for content in update.contents:
        if hasattr(content, 'text'):
            print(f"Content: {content.text}")

Message types

Input and output from agents are represented as messages. Messages are subdivided into content items.

The Microsoft Agent Framework uses the message and content types provided by the Microsoft.Extensions.AI abstractions. Messages are represented by the ChatMessage class and all content classes inherit from the base AIContent class.

Various AIContent subclasses exist that are used to represent different types of content. Some are provided as part of the base Microsoft.Extensions.AI abstractions, but providers can also add their own types, where needed.

Here are some popular types from Microsoft.Extensions.AI:

Type	Description
TextContent	Textual content that can be both input, e.g. from a user or developer, and output from the agent. Typically contains the text result from an agent.
DataContent	Binary content that can be both input and output. Can be used to pass image, audio or video data to and from the agent (where supported).
UriContent	A url that typically points at hosted content such as an image, audio or video.
FunctionCallContent	A request by an inference service to invoke a function tool.
FunctionResultContent	The result of a function tool invocation.

The Python Agent Framework uses message and content types from the agent_framework package. Messages are represented by the ChatMessage class and all content classes inherit from the base BaseContent class.

Various BaseContent subclasses exist that are used to represent different types of content:

Type	Description
`TextContent`	Textual content that can be both input and output from the agent. Typically contains the text result from an agent.
`DataContent`	Binary content represented as a data URI (e.g., base64-encoded images). Can be used to pass binary data to and from the agent.
`UriContent`	A URI that points to hosted content such as an image, audio file, or document.
`FunctionCallContent`	A request by an AI service to invoke a function tool.
`FunctionResultContent`	The result of a function tool invocation.
`ErrorContent`	Error information when processing fails.
`UsageContent`	Token usage and billing information from the AI service.

Here's how to work with different content types:

from agent_framework import ChatMessage, TextContent, DataContent, UriContent

# Create a text message
text_message = ChatMessage(role="user", text="Hello!")

# Create a message with multiple content types
image_data = b"..."  # your image bytes
mixed_message = ChatMessage(
    role="user",
    contents=[
        TextContent("Analyze this image:"),
        DataContent(data=image_data, media_type="image/png"),
    ]
)

# Access content from responses
response = await agent.run("Describe the image")
for message in response.messages:
    for content in message.contents:
        if isinstance(content, TextContent):
            print(f"Text: {content.text}")
        elif isinstance(content, DataContent):
            print(f"Data URI: {content.uri}")
        elif isinstance(content, UriContent):
            print(f"External URI: {content.uri}")

Next steps

Agent Tools

Feedback

Was this page helpful?

Last updated on 2025-10-02