* restructure: Python samples into progressive 01-05 layout - 01-get-started/: 6 numbered steps (hello agent → hosting) - 02-agents/: all agent concept samples (tools, middleware, providers, etc.) - 03-workflows/: ALL existing workflow samples preserved as-is - 04-hosting/: azure-functions, durabletask, a2a - 05-end-to-end/: demos, evaluation, hosted agents - Old files moved to _to_delete/ for review - Added AGENTS.md with structure documentation - autogen-migration/ and semantic-kernel-migration/ preserved at root * fix: switch to AzureOpenAI Foundry, fix CI failures - Switch all 01-get-started samples to AzureOpenAIResponsesClient with Azure AI Foundry project endpoint (AZURE_AI_PROJECT_ENDPOINT + AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME + AzureCliCredential) - Add _to_delete/ and 05-end-to-end/ to pyrightconfig.samples.json excludes - Fix test paths in packages/ that referenced old getting_started/ dirs: durabletask conftest + streaming test, azurefunctions conftest, devui conftest + capture_messages + openai_sdk_integration - Fix workflow_as_agent_human_in_the_loop.py import (sibling import) - Update hosting READMEs and tool comment paths - Replace root README.md with new structure overview - Update AGENTS.md to document Azure OpenAI Foundry as default provider * cleanup: remove _to_delete folder, copy resource files to active dirs All files in _to_delete/ were either: - Exact duplicates of files in the new structure (240 files) - Same file with only comment path updates (100 files) - One import-fix diff (workflow_as_agent_human_in_the_loop.py) - One superseded minimal_sample.py Resource files (sample.pdf, countries.json, employees.pdf, weather.json) copied to 02-agents/sample_assets/ and 02-agents/resources/ since active samples reference them. * fix: address PR review comments, centralize resources, remove root duplicates - Fix type annotation in 04_memory.py (string union -> proper types) - Fix old sample paths in observability files - Fix grammar/spelling in observability samples - Move sample_assets/ and resources/ to shared/ folder - Remove 8 duplicate observability files from 02-agents root - Update resource path references in multimodal_input and provider samples * fix: update broken links from old getting_started paths to new structure - Update relative paths in READMEs: getting_started/ → 01-get-started/, 02-agents/, 03-workflows/, 04-hosting/, 05-end-to-end/ - Fix absolute GitHub URLs in package READMEs - Fix broken link in ollama package README * fix: convert absolute GitHub URLs to relative paths for link checker Absolute URLs to python/samples/ on main branch 404 until PR merges. Converted to relative paths that linkspector can verify locally. * fix: update link for handoff sample moved to orchestrations/ * fix: update chatkit-integration README path from demos/ to 05-end-to-end/ * fix: update broken links in orchestrations README to match flat directory structure
8.9 KiB
ChatKit Integration Sample with Weather Agent and Image Analysis
This sample demonstrates how to integrate Microsoft Agent Framework with OpenAI ChatKit. It provides a complete implementation of a weather assistant with interactive widget visualization, image analysis, and file upload support.
Features:
- Weather information with interactive widgets
- Image analysis using vision models
- Current time queries
- File upload with attachment storage
- Chat interface with streaming responses
- City selector widget with one-click weather
Architecture
graph TB
subgraph Frontend["React Frontend (ChatKit UI)"]
UI[ChatKit Components]
Upload[File Upload]
end
subgraph Backend["FastAPI Server"]
FastAPI[FastAPI Endpoints]
subgraph ChatKit["WeatherChatKitServer"]
Respond[respond method]
Action[action method]
end
subgraph Stores["Data & Storage Layer"]
SQLite[SQLiteStore<br/>Store Protocol]
AttStore[FileBasedAttachmentStore<br/>AttachmentStore Protocol]
DB[(SQLite DB<br/>chatkit_demo.db)]
Files[/uploads directory/]
end
subgraph Integration["Agent Framework Integration"]
Converter[ThreadItemConverter]
Streamer[stream_agent_response]
Agent[Agent]
end
Widgets[Widget Rendering<br/>render_weather_widget<br/>render_city_selector_widget]
end
subgraph Azure["Azure AI"]
Foundry[GPT-5<br/>with Vision]
end
UI -->|HTTP POST /chatkit| FastAPI
Upload -->|HTTP POST /upload/id| FastAPI
FastAPI --> ChatKit
ChatKit -->|save/load threads| SQLite
ChatKit -->|save/load attachments| AttStore
ChatKit -->|convert messages| Converter
SQLite -.->|persist| DB
AttStore -.->|save files| Files
AttStore -.->|save metadata| SQLite
Converter -->|Message array| Agent
Agent -->|AgentResponseUpdate| Streamer
Streamer -->|ThreadStreamEvent| ChatKit
ChatKit --> Widgets
Widgets -->|WidgetItem| ChatKit
Agent <-->|Chat Completions API| Foundry
ChatKit -->|ThreadStreamEvent| FastAPI
FastAPI -->|SSE Stream| UI
style ChatKit fill:#e1f5ff
style Stores fill:#fff4e1
style Integration fill:#f0e1ff
style Azure fill:#e1ffe1
Server Implementation
The sample implements a ChatKit server using the ChatKitServer base class from the chatkit package:
Core Components:
-
WeatherChatKitServer: Custom ChatKit server implementation that:- Extends
ChatKitServer[dict[str, Any]] - Uses Agent Framework's
Agentwith Azure OpenAI - Converts ChatKit messages to Agent Framework format using
ThreadItemConverter - Streams responses back to ChatKit using
stream_agent_response - Creates and streams interactive widgets after agent responses
- Extends
-
SQLiteStore: Data persistence layer that:- Implements the
Store[dict[str, Any]]protocol from ChatKit - Persists threads, messages, and attachment metadata in SQLite
- Provides thread management and item history
- Stores attachment metadata for the upload lifecycle
- Implements the
-
FileBasedAttachmentStore: File storage implementation that:- Implements the
AttachmentStore[dict[str, Any]]protocol from ChatKit - Stores uploaded files on the local filesystem (in
./uploadsdirectory) - Generates upload URLs for two-phase file upload
- Saves attachment metadata to the data store for upload tracking
- Provides preview URLs for images
- Implements the
Key Integration Points:
# Converting ChatKit messages to Agent Framework
converter = ThreadItemConverter(
attachment_data_fetcher=self._fetch_attachment_data
)
agent_messages = await converter.to_agent_input(user_message_item)
# Running agent and streaming back to ChatKit
async for event in stream_agent_response(
self.weather_agent.run(agent_messages, stream=True),
thread_id=thread.id,
):
yield event
# Streaming widgets
widget = render_weather_widget(weather_data)
async for event in stream_widget(thread_id=thread.id, widget=widget):
yield event
Installation and Setup
Prerequisites
- Python 3.10+
- Node.js 18.18+ and npm 9+
- Azure OpenAI service configured
- Azure CLI for authentication (
az login)
Network Requirements
Important: This sample uses the OpenAI ChatKit frontend, which requires internet connectivity to OpenAI services.
The frontend makes outbound requests to:
cdn.platform.openai.com- ChatKit UI library (required)chatgpt.com- Configuration endpointapi-js.mixpanel.com- Telemetry
This sample is not suitable for air-gapped or network-restricted environments. The ChatKit frontend library cannot be self-hosted. See Limitations for details.
Domain Key Configuration
For local development, the sample uses a default domain key (domain_pk_localhost_dev).
For production deployment:
-
Register your domain at platform.openai.com
-
Create a
.envfile in thefrontenddirectory:VITE_CHATKIT_API_DOMAIN_KEY=your_domain_key_here
Backend Setup
- Install Python packages:
cd python/samples/05-end-to-end/chatkit-integration
pip install agent-framework-chatkit fastapi uvicorn azure-identity
- Configure Azure OpenAI:
export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
export AZURE_OPENAI_API_VERSION="2024-06-01"
export AZURE_OPENAI_CHAT_DEPLOYMENT_NAME="gpt-4o"
- Authenticate with Azure:
az login
Frontend Setup
Install the Node.js dependencies:
cd frontend
npm install
How to Run
Start the Backend Server
From the chatkit-integration directory:
python app.py
Or with auto-reload for development:
uvicorn app:app --host 127.0.0.1 --port 8001 --reload
The backend will start on http://localhost:8001
Start the Frontend Development Server
In a new terminal, from the frontend directory:
npm run dev
The frontend will start on http://localhost:5171
Access the Application
Open your browser and navigate to:
http://localhost:5171
You can now:
- Ask about weather in any location (weather widgets display automatically)
- Upload images for analysis using the attachment button
- Get the current time
- Ask to see available cities and click city buttons for instant weather
Project Structure
chatkit-integration/
├── app.py # FastAPI backend with ChatKitServer implementation
├── store.py # SQLiteStore implementation
├── attachment_store.py # FileBasedAttachmentStore implementation
├── weather_widget.py # Widget rendering functions
├── chatkit_demo.db # SQLite database (auto-created)
├── uploads/ # Uploaded files directory (auto-created)
└── frontend/
├── package.json
├── vite.config.ts
├── index.html
└── src/
├── main.tsx
└── App.tsx # ChatKit UI integration
Configuration
You can customize the application by editing constants at the top of app.py:
# Server configuration
SERVER_HOST = "127.0.0.1" # Bind to localhost only for security (local dev)
SERVER_PORT = 8001
SERVER_BASE_URL = f"http://localhost:{SERVER_PORT}"
# Database configuration
DATABASE_PATH = "chatkit_demo.db"
# File storage configuration
UPLOADS_DIRECTORY = "./uploads"
# User context
DEFAULT_USER_ID = "demo_user"
Sample Conversations
Try these example queries:
- "What's the weather like in Tokyo?"
- "Show me available cities" (displays interactive city selector)
- "What's the current time?"
- Upload an image and ask "What do you see in this image?"
Limitations
Air-Gapped / Regulated Environments
The ChatKit frontend (chatkit.js) is loaded from OpenAI's CDN and cannot be self-hosted. This means:
- Not suitable for air-gapped environments where
*.openai.comis blocked - Not suitable for regulated environments that prohibit external telemetry
- Requires domain registration with OpenAI for production use
What you CAN self-host:
- The Python backend (FastAPI server,
ChatKitServer, stores) - The
agent-framework-chatkitintegration layer - Your LLM infrastructure (Azure OpenAI, local models, etc.)
What you CANNOT self-host:
- The ChatKit frontend UI library
For more details, see:
- openai/chatkit-js#57 - Self-hosting feature request
- openai/chatkit-js#76 - Domain key requirements