getting-started.md

title	Getting Started with AG-UI
description	Step-by-step tutorial to build your first AG-UI server and client with Agent Framework
zone_pivot_groups	programming-languages
author	moonbox3
ms.topic	tutorial
ms.author	evmattso
ms.date	04/01/2026
ms.service	agent-framework

Getting Started with AG-UI

This tutorial demonstrates how to build both server and client applications using the AG-UI protocol with .NET or Python and Agent Framework. You'll learn how to create an AG-UI server that hosts an AI agent and a client that connects to it for interactive conversations.

What You'll Build

By the end of this tutorial, you'll have:

• An AG-UI server hosting an AI agent accessible via HTTP
• A client application that connects to the server and streams responses
• Understanding of how the AG-UI protocol works with Agent Framework

::: zone pivot="programming-language-csharp"

Prerequisites

Before you begin, ensure you have the following:

• .NET 8.0 or later
• Azure OpenAI service endpoint and deployment configured
• Azure CLI installed and authenticated
• 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 DefaultAzureCredential for authentication. Make sure you're authenticated with Azure (e.g., via az login). For more information, see the Azure Identity documentation.

Warning

The AG-UI protocol is still under development and subject to change. We will keep these samples updated as the protocol evolves.

Step 1: Creating an AG-UI Server

The AG-UI server hosts your AI agent and exposes it via HTTP endpoints using ASP.NET Core.

Note

The server project requires the Microsoft.NET.Sdk.Web SDK. If you're creating a new project from scratch, use dotnet new web or ensure your .csproj file uses <Project Sdk="Microsoft.NET.Sdk.Web"> instead of Microsoft.NET.Sdk.

Install Required Packages

Install the necessary packages for the server:

dotnet add package Microsoft.Agents.AI.Hosting.AGUI.AspNetCore --prerelease
dotnet add package Azure.AI.Projects --prerelease
dotnet add package Azure.Identity
dotnet add package Microsoft.Agents.AI.Foundry --prerelease

Note

The Microsoft.Agents.AI.Foundry package is required for the AsAIAgent() extension method that creates an Agent Framework agent from an AIProjectClient.

Server Code

Create a file named Program.cs:

// Copyright (c) Microsoft. All rights reserved.

using Azure.AI.Projects;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.Hosting.AGUI.AspNetCore;

WebApplicationBuilder builder = WebApplication.CreateBuilder(args);
builder.Services.AddHttpClient().AddLogging();
builder.Services.AddAGUI();

WebApplication app = builder.Build();

string endpoint = builder.Configuration["AZURE_OPENAI_ENDPOINT"]
    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
string deploymentName = builder.Configuration["AZURE_OPENAI_DEPLOYMENT_NAME"]
    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");

// Create the AI agent
AIAgent agent = new AIProjectClient(
        new Uri(endpoint),
        new DefaultAzureCredential())
    .AsAIAgent(
        model: deploymentName,
        name: "AGUIAssistant",
        instructions: "You are a helpful assistant.");

// Map the AG-UI agent endpoint
app.MapAGUI("/", agent);

await app.RunAsync();

Warning

DefaultAzureCredential is convenient for development but requires careful consideration in production. In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid latency issues, unintended credential probing, and potential security risks from fallback mechanisms.

Key Concepts

• AddAGUI: Registers AG-UI services with the dependency injection container
• MapAGUI: Extension method that registers the AG-UI endpoint with automatic request/response handling and SSE streaming
• AsAIAgent: Creates an Agent Framework agent from an AIProjectClient with a specified model and instructions
• ASP.NET Core Integration: Uses ASP.NET Core's native async support for streaming responses
• Instructions: The agent is created with default instructions, which can be overridden by client messages
• Configuration: AIProjectClient with DefaultAzureCredential provides secure authentication

Configure and Run the Server

Set the required environment variables:

export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"

Run the server:

dotnet run --urls http://localhost:8888

The server will start listening on http://localhost:8888.

Note

Keep this server running while you set up and run the client in Step 2. Both the server and client need to run simultaneously for the complete system to work.

Step 2: Creating an AG-UI Client

The AG-UI client connects to the remote server and displays streaming responses.

Important

Before running the client, ensure the AG-UI server from Step 1 is running at http://localhost:8888.

Install Required Packages

Install the AG-UI client library:

dotnet add package Microsoft.Agents.AI.AGUI --prerelease
dotnet add package Microsoft.Agents.AI --prerelease

Note

The Microsoft.Agents.AI package provides the AsAIAgent() extension method.

Client Code

Create a file named Program.cs:

// Copyright (c) Microsoft. All rights reserved.

using Microsoft.Agents.AI;
using Microsoft.Agents.AI.AGUI;
using Microsoft.Extensions.AI;

string serverUrl = Environment.GetEnvironmentVariable("AGUI_SERVER_URL") ?? "http://localhost:8888";

Console.WriteLine($"Connecting to AG-UI server at: {serverUrl}\n");

// Create the AG-UI client agent
using HttpClient httpClient = new()
{
    Timeout = TimeSpan.FromSeconds(60)
};

AGUIChatClient chatClient = new(httpClient, serverUrl);

AIAgent agent = chatClient.AsAIAgent(
    name: "agui-client",
    description: "AG-UI Client Agent");

AgentSession session = await agent.CreateSessionAsync();
List<ChatMessage> messages =
[
    new(ChatRole.System, "You are a helpful assistant.")
];

try
{
    while (true)
    {
        // Get user input
        Console.Write("\nUser (:q or quit to exit): ");
        string? message = Console.ReadLine();

        if (string.IsNullOrWhiteSpace(message))
        {
            Console.WriteLine("Request cannot be empty.");
            continue;
        }

        if (message is ":q" or "quit")
        {
            break;
        }

        messages.Add(new ChatMessage(ChatRole.User, message));

        // Stream the response
        bool isFirstUpdate = true;
        string? threadId = null;

        await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(messages, session))
        {
            ChatResponseUpdate chatUpdate = update.AsChatResponseUpdate();

            // First update indicates run started
            if (isFirstUpdate)
            {
                threadId = chatUpdate.ConversationId;
                Console.ForegroundColor = ConsoleColor.Yellow;
                Console.WriteLine($"\n[Run Started - Thread: {chatUpdate.ConversationId}, Run: {chatUpdate.ResponseId}]");
                Console.ResetColor();
                isFirstUpdate = false;
            }

            // Display streaming text content
            foreach (AIContent content in update.Contents)
            {
                if (content is TextContent textContent)
                {
                    Console.ForegroundColor = ConsoleColor.Cyan;
                    Console.Write(textContent.Text);
                    Console.ResetColor();
                }
                else if (content is ErrorContent errorContent)
                {
                    Console.ForegroundColor = ConsoleColor.Red;
                    Console.WriteLine($"\n[Error: {errorContent.Message}]");
                    Console.ResetColor();
                }
            }
        }

        Console.ForegroundColor = ConsoleColor.Green;
        Console.WriteLine($"\n[Run Finished - Thread: {threadId}]");
        Console.ResetColor();
    }
}
catch (Exception ex)
{
    Console.WriteLine($"\nAn error occurred: {ex.Message}");
}

Key Concepts

• Server-Sent Events (SSE): The protocol uses SSE for streaming responses
• AGUIChatClient: Client class that connects to AG-UI servers and implements IChatClient
• AsAIAgent: Extension method on AGUIChatClient to create an agent from the client
• RunStreamingAsync: Streams responses as AgentResponseUpdate objects
• AsChatResponseUpdate: Extension method to access chat-specific properties like ConversationId and ResponseId
• Session Management: The AgentSession maintains conversation context across requests
• Content Types: Responses include TextContent for messages and ErrorContent for errors

Configure and Run the Client

Optionally set a custom server URL:

export AGUI_SERVER_URL="http://localhost:8888"

Run the client in a separate terminal (ensure the server from Step 1 is running):

dotnet run

Step 3: Testing the Complete System

With both the server and client running, you can now test the complete system.

Expected Output

$ dotnet run
Connecting to AG-UI server at: http://localhost:8888

User (:q or quit to exit): What is 2 + 2?

[Run Started - Thread: thread_abc123, Run: run_xyz789]
2 + 2 equals 4.
[Run Finished - Thread: thread_abc123]

User (:q or quit to exit): Tell me a fun fact about space

[Run Started - Thread: thread_abc123, Run: run_def456]
Here's a fun fact: A day on Venus is longer than its year! Venus takes
about 243 Earth days to rotate once on its axis, but only about 225 Earth
days to orbit the Sun.
[Run Finished - Thread: thread_abc123]

User (:q or quit to exit): :q

Color-Coded Output

The client displays different content types with distinct colors:

• Yellow: Run started notifications
• Cyan: Agent text responses (streamed in real-time)
• Green: Run completion notifications
• Red: Error messages

How It Works

Server-Side Flow

1. Client sends HTTP POST request with messages
2. ASP.NET Core endpoint receives the request via MapAGUI
3. Agent processes the messages using Agent Framework
4. Responses are converted to AG-UI events
5. Events are streamed back as Server-Sent Events (SSE)
6. Connection closes when the run completes

Client-Side Flow

1. AGUIChatClient sends HTTP POST request to server endpoint
2. Server responds with SSE stream
3. Client parses incoming events into AgentResponseUpdate objects
4. Each update is displayed based on its content type
5. ConversationId is captured for conversation continuity
6. Stream completes when run finishes

Protocol Details

The AG-UI protocol uses:

• HTTP POST for sending requests
• Server-Sent Events (SSE) for streaming responses
• JSON for event serialization
• Thread IDs (as ConversationId) for maintaining conversation context
• Run IDs (as ResponseId) for tracking individual executions

Next Steps

Now that you understand the basics of AG-UI, you can:

• Add Backend Tools: Create custom function tools for your domain

Additional Resources

• AG-UI Overview
• Agent Framework Documentation
• AG-UI Protocol Specification

::: zone-end

::: zone pivot="programming-language-python"

Prerequisites

Before you begin, ensure you have the following:

• Python 3.10 or later
• Azure OpenAI service endpoint and deployment configured
• Azure CLI installed and authenticated
• 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 Foundry.

Note

These samples use DefaultAzureCredential for authentication. Make sure you're authenticated with Azure (e.g., via az login). For more information, see the Azure Identity documentation.

Warning

The AG-UI protocol is still under development and subject to change. We will keep these samples updated as the protocol evolves.

Step 1: Creating an AG-UI Server

The AG-UI server hosts your AI agent and exposes it via HTTP endpoints using FastAPI.

Install Required Packages

Install the necessary packages for the server:

pip install agent-framework-ag-ui --pre

Or using uv:

uv pip install agent-framework-ag-ui --prerelease=allow

This will automatically install agent-framework-core, fastapi, and uvicorn as dependencies.

Server Code

Create a file named server.py:

"""AG-UI server example."""

import os

from agent_framework import Agent
from agent_framework.openai import OpenAIChatCompletionClient
from agent_framework_ag_ui import add_agent_framework_fastapi_endpoint
from azure.identity import AzureCliCredential
from fastapi import FastAPI

# Read required configuration
endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT")
deployment_name = os.environ.get("AZURE_OPENAI_CHAT_COMPLETION_MODEL")

if not endpoint:
    raise ValueError("AZURE_OPENAI_ENDPOINT environment variable is required")
if not deployment_name:
    raise ValueError("AZURE_OPENAI_CHAT_COMPLETION_MODEL environment variable is required")

chat_client = OpenAIChatCompletionClient(
    model=deployment_name,
    azure_endpoint=endpoint,
    api_version=os.getenv("AZURE_OPENAI_API_VERSION"),
    credential=AzureCliCredential(),
)

# Create the AI agent
agent = Agent(
    name="AGUIAssistant",
    instructions="You are a helpful assistant.",
    client=chat_client,
)

# Create FastAPI app
app = FastAPI(title="AG-UI Server")

# Register the AG-UI endpoint
add_agent_framework_fastapi_endpoint(app, agent, "/")

if __name__ == "__main__":
    import uvicorn

    uvicorn.run(app, host="127.0.0.1", port=8888)

Key Concepts

• add_agent_framework_fastapi_endpoint: Registers the AG-UI endpoint with automatic request/response handling and SSE streaming
• Agent: The Agent Framework agent that will handle incoming requests
• FastAPI Integration: Uses FastAPI's native async support for streaming responses
• Instructions: The agent is created with default instructions, which can be overridden by client messages
• Configuration: OpenAIChatCompletionClient accepts explicit Azure routing inputs such as model, azure_endpoint, api_version, and credential, and can also read from environment variables

Configure and Run the Server

Set the required environment variables:

export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
export AZURE_OPENAI_CHAT_COMPLETION_MODEL="gpt-4o-mini"

Run the server:

python server.py

Or using uvicorn directly:

uvicorn server:app --host 127.0.0.1 --port 8888

The server will start listening on http://127.0.0.1:8888.

Step 2: Creating an AG-UI Client

The AG-UI client connects to the remote server and displays streaming responses.

Install Required Packages

The AG-UI package is already installed, which includes the AGUIChatClient:

# Already installed with agent-framework-ag-ui
pip install agent-framework-ag-ui --pre

Client Code

Create a file named client.py:

"""AG-UI client example."""

import asyncio
import os

from agent_framework import Agent
from agent_framework_ag_ui import AGUIChatClient


async def main():
    """Main client loop."""
    # Get server URL from environment or use default
    server_url = os.environ.get("AGUI_SERVER_URL", "http://127.0.0.1:8888/")
    print(f"Connecting to AG-UI server at: {server_url}\n")

    # Create AG-UI chat client
    chat_client = AGUIChatClient(server_url=server_url)

    # Create agent with the chat client
    agent = Agent(
        name="ClientAgent",
        client=chat_client,
        instructions="You are a helpful assistant.",
    )

    # Get a thread for conversation continuity
    thread = agent.create_session()

    try:
        while True:
            # Get user input
            message = input("\nUser (:q or quit to exit): ")
            if not message.strip():
                print("Request cannot be empty.")
                continue

            if message.lower() in (":q", "quit"):
                break

            # Stream the agent response
            print("\nAssistant: ", end="", flush=True)
            async for update in agent.run(message, session=thread, stream=True):
                # Print text content as it streams
                if update.text:
                    print(f"\033[96m{update.text}\033[0m", end="", flush=True)

            print("\n")

    except KeyboardInterrupt:
        print("\n\nExiting...")
    except Exception as e:
        print(f"\n\033[91mAn error occurred: {e}\033[0m")


if __name__ == "__main__":
    asyncio.run(main())

Key Concepts

• Server-Sent Events (SSE): The protocol uses SSE format (data: {json}\n\n)
• Event Types: Different events provide metadata and content (UPPERCASE with underscores):
  • RUN_STARTED: Agent has started processing
  • TEXT_MESSAGE_START: Start of a text message from the agent
  • TEXT_MESSAGE_CONTENT: Incremental text streamed from the agent (with delta field)
  • TEXT_MESSAGE_END: End of a text message
  • RUN_FINISHED: Successful completion
  • RUN_ERROR: Error information
• Field Naming: Event fields use camelCase (e.g., threadId, runId, messageId)
• Thread Management: The threadId maintains conversation context across requests
• Client-Side Instructions: System messages are sent from the client

Configure and Run the Client

Optionally set a custom server URL:

export AGUI_SERVER_URL="http://127.0.0.1:8888/"

Run the client (in a separate terminal):

python client.py

Step 3: Testing the Complete System

With both the server and client running, you can now test the complete system.

Expected Output

$ python client.py
Connecting to AG-UI server at: http://127.0.0.1:8888/

User (:q or quit to exit): What is 2 + 2?

[Run Started - Thread: abc123, Run: xyz789]
2 + 2 equals 4.
[Run Finished - Thread: abc123, Run: xyz789]

User (:q or quit to exit): Tell me a fun fact about space

[Run Started - Thread: abc123, Run: def456]
Here's a fun fact: A day on Venus is longer than its year! Venus takes
about 243 Earth days to rotate once on its axis, but only about 225 Earth
days to orbit the Sun.
[Run Finished - Thread: abc123, Run: def456]

User (:q or quit to exit): :q

Color-Coded Output

The client displays different content types with distinct colors:

• Yellow: Run started notifications
• Cyan: Agent text responses (streamed in real-time)
• Green: Run completion notifications
• Red: Error messages

Testing with curl (Optional)

Before running the client, you can test the server manually using curl:

curl -N http://127.0.0.1:8888/ \
  -H "Content-Type: application/json" \
  -H "Accept: text/event-stream" \
  -d '{
    "messages": [
      {"role": "user", "content": "What is 2 + 2?"}
    ]
  }'

You should see Server-Sent Events streaming back:

data: {"type":"RUN_STARTED","threadId":"...","runId":"..."}

data: {"type":"TEXT_MESSAGE_START","messageId":"...","role":"assistant"}

data: {"type":"TEXT_MESSAGE_CONTENT","messageId":"...","delta":"The"}

data: {"type":"TEXT_MESSAGE_CONTENT","messageId":"...","delta":" answer"}

...

data: {"type":"TEXT_MESSAGE_END","messageId":"..."}

data: {"type":"RUN_FINISHED","threadId":"...","runId":"..."}

How It Works

Server-Side Flow

1. Client sends HTTP POST request with messages
2. FastAPI endpoint receives the request
3. AgentFrameworkAgent wrapper orchestrates the execution
4. Agent processes the messages using Agent Framework
5. AgentFrameworkEventBridge converts agent updates to AG-UI events
6. Responses are streamed back as Server-Sent Events (SSE)
7. Connection closes when the run completes

Client-Side Flow

1. Client sends HTTP POST request to server endpoint
2. Server responds with SSE stream
3. Client parses incoming data: lines as JSON events
4. Each event is displayed based on its type
5. threadId is captured for conversation continuity
6. Stream completes when RUN_FINISHED event arrives

Protocol Details

The AG-UI protocol uses:

• HTTP POST for sending requests
• Server-Sent Events (SSE) for streaming responses
• JSON for event serialization
• Thread IDs for maintaining conversation context
• Run IDs for tracking individual executions
• Event type naming: UPPERCASE with underscores (e.g., RUN_STARTED, TEXT_MESSAGE_CONTENT)
• Field naming: camelCase (e.g., threadId, runId, messageId)

Common Patterns

Custom Server Configuration

from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware

app = FastAPI()

# Add CORS for web clients
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

add_agent_framework_fastapi_endpoint(app, agent, "/agent")

Multiple Agents

app = FastAPI()

weather_agent = Agent(name="weather", ...)
finance_agent = Agent(name="finance", ...)

add_agent_framework_fastapi_endpoint(app, weather_agent, "/weather")
add_agent_framework_fastapi_endpoint(app, finance_agent, "/finance")

Error Handling

try:
    async for event in client.send_message(message):
        if event.get("type") == "RUN_ERROR":
            error_msg = event.get("message", "Unknown error")
            print(f"Error: {error_msg}")
            # Handle error appropriately
except httpx.HTTPError as e:
    print(f"HTTP error: {e}")
except Exception as e:
    print(f"Unexpected error: {e}")

Troubleshooting

Connection Refused

Ensure the server is running before starting the client:

# Terminal 1
python server.py

# Terminal 2 (after server starts)
python client.py

Authentication Errors

Make sure you're authenticated with Azure:

az login

Verify you have the correct role assignment on the Azure OpenAI resource.

Streaming Not Working

Check that your client timeout is sufficient:

httpx.AsyncClient(timeout=60.0)  # 60 seconds should be enough

For long-running agents, increase the timeout accordingly.

Thread Context Lost

The client automatically manages thread continuity. If context is lost:

1. Check that threadId is being captured from RUN_STARTED events
2. Ensure the same client instance is used across messages
3. Verify the server is receiving the thread_id in subsequent requests

Next Steps

Now that you understand the basics of AG-UI, you can:

• Add Backend Tools: Create custom function tools for your domain

Additional Resources

• AG-UI Overview
• Agent Framework Documentation
• AG-UI Protocol Specification

::: zone-end