AG-UI protocol integration for Agent Framework, enabling seamless integration with AG-UI's web interface and streaming protocol.

pip install agent-framework-ag-ui

from fastapi import FastAPI
from agent_framework import ChatAgent
from agent_framework.azure import AzureOpenAIChatClient
from agent_framework.ag_ui import add_agent_framework_fastapi_endpoint

# Create your agent
agent = ChatAgent(
    name="my_agent",
    instructions="You are a helpful assistant.",
    chat_client=AzureOpenAIChatClient(
        endpoint="https://your-resource.openai.azure.com/",
        deployment_name="gpt-4o-mini",
        api_key="your-api-key",
    ),
)

# Create FastAPI app and add AG-UI endpoint
app = FastAPI()
add_agent_framework_fastapi_endpoint(app, agent, "/")

# Run with: uvicorn main:app --reload

import asyncio
from agent_framework import TextContent
from agent_framework.ag_ui import AGUIChatClient

async def main():
    async with AGUIChatClient(endpoint="http://localhost:8000/") as client:
        # Stream responses
        async for update in client.get_streaming_response("Hello!"):
            for content in update.contents:
                if isinstance(content, TextContent):
                    print(content.text, end="", flush=True)
        print()

asyncio.run(main())

The AGUIChatClient supports:

• Streaming and non-streaming responses
• Hybrid tool execution (client-side + server-side tools)
• Automatic thread management for conversation continuity
• Integration with ChatAgent for client-side history management

• Getting Started Tutorial - Step-by-step guide to building AG-UI servers and clients
  • Server setup with FastAPI
  • Client examples using AGUIChatClient
  • Hybrid tool execution (client-side + server-side)
  • Thread management and conversation continuity
• Examples - Complete examples for AG-UI features

This integration supports all 7 AG-UI features:

1. Agentic Chat: Basic streaming chat with tool calling support
2. Backend Tool Rendering: Tools executed on backend with results streamed to client
3. Human in the Loop: Function approval requests for user confirmation before tool execution
4. Agentic Generative UI: Async tools for long-running operations with progress updates
5. Tool-based Generative UI: Custom UI components rendered on frontend based on tool calls
6. Shared State: Bidirectional state sync between client and server
7. Predictive State Updates: Stream tool arguments as optimistic state updates during execution

The package uses a clean, orchestrator-based architecture:

• AgentFrameworkAgent: Lightweight wrapper that delegates to orchestrators
• Orchestrators: Handle different execution flows (default, human-in-the-loop, etc.)
• Confirmation Strategies: Domain-specific confirmation messages (extensible)
• AgentFrameworkEventBridge: Converts Agent Framework events to AG-UI events
• Message Adapters: Bidirectional conversion between AG-UI and Agent Framework message formats
• FastAPI Endpoint: Streaming HTTP endpoint with Server-Sent Events (SSE)

1. New to AG-UI? Start with the Getting Started Tutorial
2. Want to see examples? Check out the Examples for AG-UI features

MIT