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+# Getting Started with AG-UI (Python)
+
+The AG-UI (Agent UI) protocol provides a standardized way for client applications to interact with AI agents over HTTP. This tutorial demonstrates how to build both server and client applications using the AG-UI protocol with Python.
+
+## Quick Start - Client Examples
+
+If you want to quickly try out the AG-UI client, we provide three ready-to-use examples:
+
+### Basic Interactive Client (`client.py`)
+
+A simple command-line chat client that demonstrates:
+- Streaming responses in real-time
+- Automatic thread management for conversation continuity
+- Direct `AGUIChatClient` usage (caller manages message history)
+
+**Run:**
+```bash
+python client.py
+```
+
+**Note:** This example sends only the current message to the server. The server is responsible for maintaining conversation history using the thread_id.
+
+### Advanced Features Client (`client_advanced.py`)
+
+Demonstrates advanced capabilities:
+- Tool/function calling
+- Both streaming and non-streaming responses
+- Multi-turn conversations
+- Error handling patterns
+
+**Run:**
+```bash
+python client_advanced.py
+```
+
+**Note:** This example shows direct `AGUIChatClient` usage. Tool execution and conversation continuity depend on server-side configuration and capabilities.
+
+### Agent Integration (`client_with_agent.py`)
+
+Best practice example using `Agent` wrapper with **AgentThread**
+- **AgentThread** maintains conversation state
+- Client-side conversation history management via `thread.message_store`
+- **Hybrid tool execution**: client-side + server-side tools simultaneously
+- Full conversation history sent on each request
+- Tool calling with conversation context
+
+**To demonstrate hybrid tools:**
+
+1. **Start server with server-side tool** (Terminal 1):
+   ```bash
+   # Server has get_time_zone tool
+   python server.py
+   ```
+
+2. **Run client with client-side tool** (Terminal 2):
+   ```bash
+   # Client has get_weather tool
+   python client_with_agent.py
+   ```
+
+All examples require a running AG-UI server (see Step 1 below for setup).
+
+## Understanding AG-UI Architecture
+
+### Thread Management
+
+The AG-UI protocol supports two approaches to conversation history:
+
+1. **Server-Managed Threads** (client.py, client_advanced.py)
+   - Client sends only the current message + thread_id
+   - Server maintains full conversation history
+   - Requires server to support stateful thread storage
+   - Lighter network payload
+
+2. **Client-Managed History** (client_with_agent.py)
+   - Client maintains full conversation history locally
+   - Full message history sent with each request
+   - Works with any AG-UI server (stateful or stateless)
+
+The `Agent` wrapper (used in client_with_agent.py) collects messages from local storage and sends the full history to `AGUIChatClient`, which then forwards everything to the server.
+
+### Tool/Function Calling
+
+The AG-UI protocol supports **hybrid tool execution** - both client-side AND server-side tools can coexist in the same conversation.
+
+**The Hybrid Pattern** (client_with_agent.py):
+```
+Client defines:           Server defines:
+- get_weather()          - get_current_time()
+- read_sensors()         - get_server_forecast()
+
+User: "What's the weather in SF and what time is it?"
+    ↓
+Agent sends: full history + tool definitions for get_weather, read_sensors
+    ↓
+Server LLM decides: "I need get_weather('SF') and get_current_time()"
+    ↓
+Server executes get_current_time() → "2025-11-11 14:30:00 UTC"
+Server sends function call request → get_weather('SF')
+    ↓
+Agent intercepts get_weather call → executes locally
+    ↓
+Client sends result → "Sunny, 72°F"
+    ↓
+Server combines both results → "It's sunny and 72°F in SF, and the current time is 2:30 PM UTC"
+    ↓
+Client receives final response
+```
+
+**How it works:**
+
+1. **Client-Side Tools** (`client_with_agent.py`):
+   - Tools defined in Agent's `tools` parameter execute locally
+   - Tool metadata (name, description, schema) sent to server for planning
+   - When server requests client tool → client intercepts → executes locally → sends result
+
+2. **Server-Side Tools**:
+   - Defined in server agent's configuration
+   - Server executes directly without client involvement
+   - Results included in server's response
+
+3. **Hybrid Pattern (Both Together)**:
+   - Server LLM sees ALL tool definitions (client + server)
+   - Decides which to use based on task
+   - Server tools execute server-side
+   - Client tools execute client-side
+
+**Direct AGUIChatClient Usage** (client_advanced.py):
+Even without Agent wrapper, client-side tools work:
+- Tools passed in ChatOptions execute locally
+- Server can also have its own tools
+- Hybrid execution works automatically
+
+### Interrupts and Resume Entries
+
+Human-in-the-loop approvals and workflow input requests pause by emitting a terminal `RUN_FINISHED` event whose
+`outcome.type` is `"interrupt"`. Generic AG-UI clients should read prompts from `RUN_FINISHED.outcome.interrupts`
+and resume the same `threadId` with a canonical `resume` array of `ResumeEntry` values.
+
+```json
+{
+  "threadId": "thread-1",
+  "messages": [],
+  "resume": [
+    {
+      "interruptId": "approval_1",
+      "status": "resolved",
+      "payload": {
+        "approved": true
+      }
+    }
+  ]
+}
+```
+
+`Interrupt` and `ResumeEntry` are AG-UI protocol models from `ag_ui.core`; Agent Framework does not define a
+separate interrupt model. New interrupted runs use `RUN_FINISHED.outcome.interrupts`, not a stable top-level
+`RUN_FINISHED.interrupt` field.
+
+## What is AG-UI?
+
+AG-UI is a protocol that enables:
+- **Remote agent hosting**: Host AI agents as web services that can be accessed by multiple clients
+- **Streaming responses**: Real-time streaming of agent responses using Server-Sent Events (SSE)
+- **Standardized communication**: Consistent message format for agent interactions
+- **Thread management**: Maintain conversation context across multiple requests
+- **Advanced features**: Human-in-the-loop, state management, tool rendering
+
+## 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 (for DefaultAzureCredential)
+- 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 Azure AI Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/deploy-models-openai).
+
+**Note**: These samples use `DefaultAzureCredential` for authentication. Make sure you're authenticated with Azure (e.g., via `az login`, or environment variables). For more information, see the [Azure Identity documentation](https://learn.microsoft.com/python/api/azure-identity/azure.identity.defaultazurecredential).
+
+> **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
+
+```bash
+pip install agent-framework-ag-ui
+```
+
+Or using uv:
+
+```bash
+uv pip install agent-framework-ag-ui
+```
+
+### Server Code
+
+Create a file named `server.py`:
+
+```python
+# Copyright (c) Microsoft. All rights reserved.
+
+"""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 fastapi import FastAPI
+
+# Read required configuration
+endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT")
+model = os.environ.get("AZURE_OPENAI_MODEL")
+api_key = os.environ.get("AZURE_OPENAI_API_KEY")
+
+if not endpoint:
+    raise ValueError("AZURE_OPENAI_ENDPOINT environment variable is required")
+if not model:
+    raise ValueError("AZURE_OPENAI_MODEL environment variable is required")
+if not api_key:
+    raise ValueError("AZURE_OPENAI_API_KEY environment variable is required")
+
+# Create the AI agent
+agent = Agent(
+    name="AGUIAssistant",
+    instructions="You are a helpful assistant.",
+    client=OpenAIChatCompletionClient(
+        azure_endpoint=endpoint,
+        model=model,
+        api_key=api_key,
+    ),
+)
+
+# 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=5100)
+```
+
+### Key Concepts
+
+- **`add_agent_framework_fastapi_endpoint`**: Registers the AG-UI endpoint with automatic request/response handling and SSE streaming
+- **`Agent`**: The 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` can read from environment variables (`AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_MODEL`, `AZURE_OPENAI_API_KEY`) or accept parameters directly
+
+**Alternative (simpler)**: Use environment variables only:
+
+```python
+# No need to read environment variables manually
+agent = Agent(
+    name="AGUIAssistant",
+    instructions="You are a helpful assistant.",
+    client=OpenAIChatCompletionClient(),  # Reads from environment automatically
+)
+```
+
+### Configure and Run the Server
+
+Set the required environment variables:
+
+```bash
+export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
+export AZURE_OPENAI_MODEL="gpt-4o-mini"
+# Optional: Set API key if not using DefaultAzureCredential
+# export AZURE_OPENAI_API_KEY="your-api-key"
+```
+
+Run the server:
+
+```bash
+python server.py
+```
+
+Or using uvicorn directly:
+
+```bash
+uvicorn server:app --host 127.0.0.1 --port 5100
+```
+
+The server will start listening on `http://127.0.0.1:5100`.
+
+## Step 2: Creating an AG-UI Client
+
+The AG-UI client connects to the remote server and displays streaming responses. The `AGUIChatClient` is a built-in implementation that integrates with the Agent Framework's standard chat interface.
+
+### Install Required Packages
+
+The `AGUIChatClient` is included in the `agent-framework-ag-ui` package (already installed if you installed the server packages).
+
+```bash
+pip install agent-framework-ag-ui
+```
+
+### Client Code
+
+Create a file named `client.py`:
+
+```python
+# Copyright (c) Microsoft. All rights reserved.
+
+"""AG-UI client example using AGUIChatClient."""
+
+import asyncio
+import os
+
+from agent_framework.ag_ui import AGUIChatClient
+
+
+async def main():
+    """Main client loop demonstrating AGUIChatClient usage."""
+    # Get server URL from environment or use default
+    server_url = os.environ.get("AGUI_SERVER_URL", "http://127.0.0.1:5100/")
+    print(f"Connecting to AG-UI server at: {server_url}\n")
+
+    # Create client with context manager for automatic cleanup
+    async with AGUIChatClient(endpoint=server_url) as client:
+        thread_id: str | None = None
+
+        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
+
+                # Send message and stream the response
+                print("\nAssistant: ", end="", flush=True)
+
+                # Use metadata to maintain conversation continuity
+                metadata = {"thread_id": thread_id} if thread_id else None
+
+                async for update in client.get_response(message, metadata=metadata, stream=True):
+                    # Extract thread ID from first update
+                    if not thread_id and update.additional_properties:
+                        thread_id = update.additional_properties.get("thread_id")
+                        if thread_id:
+                            print(f"\n[Thread: {thread_id}]")
+                            print("Assistant: ", end="", flush=True)
+
+                    # Stream text content as it arrives
+                    for content in update.contents:
+                        if content.type == "text" and content.text:
+                            print(content.text, end="", flush=True)
+
+                print()  # New line after response
+
+        except KeyboardInterrupt:
+            print("\n\nExiting...")
+        except Exception as e:
+            print(f"\nAn error occurred: {e}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+### Key Concepts
+
+- **`AGUIChatClient`**: Built-in client that implements the Agent Framework's `BaseChatClient` interface
+- **Automatic Event Handling**: The client automatically converts AG-UI events to Agent Framework types
+- **Thread Management**: Pass `thread_id` in metadata to maintain conversation context across requests
+- **Streaming Responses**: Use `get_response(..., stream=True)` for real-time streaming or `get_response(..., stream=False)` for non-streaming
+- **Context Manager**: Use `async with` for automatic cleanup of HTTP connections
+- **Standard Interface**: Works with all Agent Framework patterns (Agent, tools, etc.)
+- **Hybrid Tool Execution**: Supports both client-side and server-side tools executing together in the same conversation
+
+### Configure and Run the Client
+
+Optionally set a custom server URL:
+
+```bash
+export AGUI_SERVER_URL="http://127.0.0.1:5100/"
+```
+
+Run the client (in a separate terminal):
+
+```bash
+python client.py
+```
+
+## Step 3: Testing the Complete System
+
+### Expected Output
+
+```
+$ python client.py
+Connecting to AG-UI server at: http://127.0.0.1:5100/
+
+User (:q or quit to exit): What is the capital of France?
+
+[Thread: abc123]
+Assistant: The capital of France is Paris. It is known for its rich history, culture,
+and iconic landmarks such as the Eiffel Tower and the Louvre Museum.
+
+User (:q or quit to exit): Tell me a fun fact about space
+```
+
+## Troubleshooting
+
+### Connection Refused
+
+Ensure the server is running before starting the client:
+
+```bash
+# Terminal 1
+python server.py
+
+# Terminal 2 (after server starts)
+python client.py
+```
+
+### Authentication Errors
+
+Make sure you're authenticated with Azure:
+
+```bash
+az login
+```
+
+Verify you have the correct role assignment on the Azure OpenAI resource.
+
+### Streaming Not Working
+
+Check that your client timeout is sufficient:
+
+```python
+httpx.AsyncClient(timeout=60.0)  # 60 seconds should be enough
+```
+
+For long-running agents, increase the timeout accordingly.
+
+### No Events Received
+
+Ensure you're using the correct `Accept` header:
+
+```python
+headers={"Accept": "text/event-stream"}
+```
+
+And parsing SSE format correctly (lines starting with `data: `).
+
+### 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
+
+### Event Type Mismatches
+
+Remember that event types are UPPERCASE with underscores (`RUN_STARTED`, not `run_started`) and field names are camelCase (`threadId`, not `thread_id`).
+
+### Import Errors
+
+Make sure all packages are installed:
+
+```bash
+pip install agent-framework-ag-ui agent-framework-core fastapi uvicorn httpx
+```
+
+Or check your virtual environment is activated:
+
+```bash
+source venv/bin/activate  # Linux/macOS
+venv\Scripts\activate     # Windows
+```