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 Agent
from agent_framework.openai import OpenAIChatCompletionClient
from agent_framework.ag_ui import add_agent_framework_fastapi_endpoint

# Create your agent
agent = Agent(
    name="my_agent",
    instructions="You are a helpful assistant.",
    client=OpenAIChatCompletionClient(
        azure_endpoint="https://your-resource.openai.azure.com/",
        model="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

from fastapi import FastAPI
from agent_framework import WorkflowBuilder, WorkflowContext, executor
from agent_framework.ag_ui import add_agent_framework_fastapi_endpoint

@executor(id="start")
async def start(message: str, ctx: WorkflowContext) -> None:
    await ctx.yield_output(f"Workflow received: {message}")

workflow = WorkflowBuilder(start_executor=start).build()

app = FastAPI()
add_agent_framework_fastapi_endpoint(app, workflow, "/")

Use workflow_factory when your workflow keeps runtime state (for example pending request_info interrupts) and must be isolated per AG-UI thread:

from fastapi import FastAPI
from agent_framework import Workflow, WorkflowBuilder
from agent_framework.ag_ui import AgentFrameworkWorkflow, add_agent_framework_fastapi_endpoint

def build_workflow_for_thread(thread_id: str) -> Workflow:
    # Build a fresh workflow instance for each thread id.
    return WorkflowBuilder(start_executor=...).build()

app = FastAPI()
thread_scoped_workflow = AgentFrameworkWorkflow(
    workflow_factory=build_workflow_for_thread,
    name="my_workflow",
)
add_agent_framework_fastapi_endpoint(app, thread_scoped_workflow, "/")

import asyncio
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_response("Hello!", stream=True):
            for content in update.contents:
                if content.type == "text" and content.text:
                    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 Agent for client-side history management
• Canonical interrupt/resume passthrough (availableInterrupts and resume)

Use state_update when a backend tool needs to send different payloads to the model, the UI, and shared state. The text value remains the LLM-bound tool result, tool_result becomes the AG-UI ToolCallResultEvent.content for frontend rendering, and state is merged into durable shared state.

from agent_framework import Content, tool
from agent_framework.ag_ui import state_update

@tool
async def get_weather(city: str) -> Content:
    data = await fetch_weather(city)
    return state_update(
        text=f"{city}: {data['temp']}°C and {data['conditions']}",
        tool_result={
            "component": "weather-card",
            "city": city,
            "temperature": data["temp"],
            "conditions": data["conditions"],
            "humidity": data["humidity"],
        },
        state={"weather": {"city": city, **data}},
    )

• 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

Agent Framework AG-UI uses the canonical AG-UI interrupt protocol. Paused agent approval and workflow request_info runs finish with RUN_FINISHED.outcome.type == "interrupt" and a non-empty RUN_FINISHED.outcome.interrupts array. Agent Framework does not define a separate interrupt model; use ag_ui.core.Interrupt and ag_ui.core.ResumeEntry when constructing typed request data in Python.

Tool approval interrupts use reason: "tool_call" and include toolCallId when the pause is bound to a tool call. Workflow request_info interrupts use reason: "input_required". Framework-specific details needed for resume validation live in each interrupt's metadata, while generic clients can render the human-readable message and responseSchema.

Interrupted terminal event shape:

{
  "type": "RUN_FINISHED",
  "outcome": {
    "type": "interrupt",
    "interrupts": [
      {
        "id": "approval_1",
        "reason": "tool_call",
        "message": "Approve tool call get_weather?",
        "toolCallId": "tool_call_1",
        "responseSchema": {
          "type": "object",
          "properties": {
            "accepted": { "type": "boolean" },
            "arguments": { "type": "object" }
          },
          "required": ["accepted"]
        },
        "metadata": {
          "agent_framework": {
            "type": "function_approval_request",
            "function_call": {
              "call_id": "tool_call_1",
              "name": "get_weather",
              "arguments": {
                "city": "Seattle"
              }
            }
          }
        }
      }
    ]
  }
}

Resume the paused thread with a canonical resume array. Each entry addresses exactly one open interrupt by interruptId; status is resolved or cancelled; resolved entries carry the approval or workflow response payload.

{
  "threadId": "thread-1",
  "messages": [],
  "resume": [
    {
      "interruptId": "approval_1",
      "status": "resolved",
      "payload": {
        "approved": true
      }
    }
  ]
}

This is a clean release-candidate breaking change before 1.0.0: new interrupted runs use RUN_FINISHED.outcome.interrupts and do not emit a stable top-level RUN_FINISHED.interrupt field. Normal non-interrupted runs continue to finish with valid RUN_FINISHED terminal events.

The Python package is currently in release candidate stage and is targeting the released 1.0.0 API surface. The preferred application import path is agent_framework.ag_ui; direct package imports from agent_framework_ag_ui are also supported.

Review focus: whether these names are the right stable contract for Python users, and whether the protocol interrupt fields below match AG-UI's expected pause/resume shape.

Interrupt support is protocol data rather than a separate Agent Framework Python class. Requests accept canonical availableInterrupts/available_interrupts and resume values; AGUIChatClient and AGUIHttpService.post_run(...) forward those fields with AG-UI wire aliases; agent approval and workflow request_info pauses emit RUN_FINISHED.outcome.interrupts; AGUIEventConverter preserves canonical interrupt outcome metadata on the final ChatResponseUpdate; and thread snapshot hydration replays the canonical interrupt outcome when a scoped snapshot stores an unresolved pause.

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

Additional compatibility and draft support:

• Native Workflow endpoint registration via add_agent_framework_fastapi_endpoint(...)
• Workflow-to-AG-UI event mapping (run/step/activity/tool/custom events)
• Custom event compatibility for inbound CUSTOM, CUSTOM_EVENT, and custom_event
• Pragmatic multimodal input parsing for both legacy (binary) and draft media-part shapes
• Canonical interrupt/resume handling (availableInterrupts, resume, and RUN_FINISHED.outcome.interrupts)

The AG-UI endpoint does not enforce authentication by default. For production deployments, you should add authentication using FastAPI's dependency injection system via the dependencies parameter.

import os
from fastapi import Depends, FastAPI, HTTPException, Security
from fastapi.security import APIKeyHeader
from agent_framework import Agent
from agent_framework.ag_ui import add_agent_framework_fastapi_endpoint

# Configure API key authentication
API_KEY_HEADER = APIKeyHeader(name="X-API-Key", auto_error=False)
EXPECTED_API_KEY = os.environ.get("AG_UI_API_KEY")

async def verify_api_key(api_key: str | None = Security(API_KEY_HEADER)) -> None:
    """Verify the API key provided in the request header."""
    if not api_key or api_key != EXPECTED_API_KEY:
        raise HTTPException(status_code=401, detail="Invalid or missing API key")

# Create agent and app
agent = Agent(name="my_agent", instructions="...", client=...)
app = FastAPI()

# Register endpoint WITH authentication
add_agent_framework_fastapi_endpoint(
    app,
    agent,
    "/",
    dependencies=[Depends(verify_api_key)],  # Authentication enforced here
)

The dependencies parameter accepts any FastAPI dependency, enabling integration with:

• OAuth 2.0 / OpenID Connect - Use fastapi.security.OAuth2PasswordBearer
• JWT Tokens - Validate tokens with libraries like python-jose
• Azure AD / Entra ID - Use azure-identity for Microsoft identity platform
• Rate Limiting - Add request throttling dependencies
• Custom Authentication - Implement your organization's auth requirements

For a complete authentication example, see getting_started/server.py.

AG-UI Thread Snapshot persistence is opt-in and disabled by default. Existing endpoints keep their current behavior unless you provide a snapshot_store.

Thread snapshots let an AG-UI frontend recover replayable UI state after a refresh. When snapshot persistence is enabled, the endpoint stores the latest replayable snapshot for an AG-UI Thread within an application-defined Snapshot Scope. A Hydrate Request is an AG-UI request with a known threadId, messages: [], and no resume payload. Hydration replays the stored Shared State, message snapshot, and canonical interrupt outcome when available, then finishes without invoking the wrapped agent or workflow.

Use the built-in in-memory store for local development, demos, and tests:

from fastapi import FastAPI

from agent_framework.ag_ui import InMemoryAGUIThreadSnapshotStore, add_agent_framework_fastapi_endpoint

app = FastAPI()
agent = ...
snapshot_store = InMemoryAGUIThreadSnapshotStore(max_snapshots=500)


def resolve_snapshot_scope(request):
    # Local demo scope. Production apps should derive the scope from authenticated user or tenant context.
    del request
    return "local-demo"


add_agent_framework_fastapi_endpoint(
    app,
    agent,
    "/",
    snapshot_store=snapshot_store,
    snapshot_scope_resolver=resolve_snapshot_scope,
)

A frontend can then hydrate the latest stored snapshot for the scoped thread:

{
  "threadId": "thread-1",
  "messages": []
}

Endpoint configuration requires snapshot_scope_resolver whenever a snapshot store is configured, including when the store is already set on a pre-wrapped AgentFrameworkAgent or AgentFrameworkWorkflow. The resolver returns the application-defined Snapshot Scope used with the AG-UI Thread id as the storage key. When using AgentFrameworkWorkflow(workflow_factory=...), the same resolver also scopes the in-memory workflow cache even without a snapshot store; provide it in multi-user deployments so two users who submit the same threadId do not share a live Workflow instance.

For hosted agents, request Shared State is also available through AgentSession.state during that run, whether or not snapshot persistence is configured. Request values are untrusted per-run context: they overlay ordinary restored values, are not passed through typed session restoration, and are excluded from private Session Continuation State. Keys owned by configured context providers or reserved for approval and message-injection middleware are not copied into AgentSession.state; their server-owned values take precedence over client Shared State.

When scoped snapshots are configured, each category has one State Authority:

Session Continuation State is stored atomically in the optional AGUIThreadSnapshot.session_state field and restored through the core AgentSession typed serialization contract. It is never accepted from an AG-UI request or emitted during hydration. Deleting a scoped thread snapshot resets its replayable and private state together, and clearing a Snapshot Scope removes all such records in that scope. Missing or empty request Shared State is not a reset command. If private continuation cannot be restored or serialized, the endpoint logs the failure and continues without that continuation so stale or unsupported provider state cannot permanently block the thread or suppress RUN_FINISHED.

AG-UI Thread ids identify AG-UI Threads; they do not authorize snapshot access. Do not treat a thread id as a bearer credential or tenant boundary. Production applications must authenticate and authorize every AG-UI endpoint request and choose a Snapshot Scope that represents the app's real access boundary, such as an authenticated user, tenant, or workspace. Do not rely on untrusted client-provided fields by themselves to choose that boundary.

Tool approval resumes are validated against server-owned Approval State. The default Approval State store is process-local and bounded, and stores only approval-specific state needed to validate and continue pending approvals. It is not an authentication, tenant authorization, or distributed durability mechanism; production applications remain responsible for endpoint authentication, tenant authorization, and deployment/storage architecture that matches their availability and worker topology requirements.

Snapshot storage is treated as trusted server-side storage because private continuation is eligible for typed core restoration; applications are responsible for providing its integrity protection. Snapshots also have confidentiality impact: they may contain sensitive user text, model output, tool results, function arguments, UI payloads, Shared State, interrupt data, and private provider working state. The built-in InMemoryAGUIThreadSnapshotStore is in-memory only, process-local, bounded, latest-only, and not durable production storage. It is cleared on process restart and is not shared across workers.

No file-backed AG-UI snapshot store is provided by the package. Applications that need durable persistence should provide an app-owned implementation of the AGUIThreadSnapshotStore protocol and own storage hardening, including encryption, integrity protection, access control, retention, audit, data residency, and deletion behavior. Existing custom stores remain source-compatible because session_state is optional, but they provide Session State Continuity only when they round-trip that field unchanged with the rest of the snapshot.

The supported consistency model is one active run per (Snapshot Scope, threadId). Concurrent writes to the same scoped thread remain last-writer-wins. Applications that require stronger consistency must serialize those runs using coordination appropriate to their deployment; a process-local lock does not provide distributed consistency.

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