Files
agent-framework/python/samples/getting_started/devui
T
Victor Dibia 94eae24082 Python: DevUI: Add OpenAI Responses API proxy support + HIL for Workflows (#1737)
* DevUI: Add OpenAI Responses API proxy support with enhanced UI features

This commit adds support for proxying requests to OpenAI's Responses API,
allowing DevUI to route conversations to OpenAI models when configured to enable testing.

Backend changes:
- Add OpenAI proxy executor with conversation routing logic
- Enhance event mapper to support OpenAI Responses API format
- Extend server endpoints to handle OpenAI proxy mode
- Update models with OpenAI-specific response types
- Remove emojis from logging and CLI output for cleaner text

Frontend changes:
- Add settings modal with OpenAI proxy configuration UI
- Enhance agent and workflow views with improved state management
- Add new UI components (separator, switch) for settings
- Update debug panel with better event filtering
- Improve message renderers for OpenAI content types
- Update types and API client for OpenAI integration

* update ui, settings modal and workflow input form, add register cleanup hooks.

* add workflow HIL support, user mode, other fixes

* feat(devui): add human-in-the-loop (HIL) support with dynamic response schemas

Implement  HIL workflow support allowing workflows to pause for user input
with dynamically generated JSON schemas based on response handler type hints.

Key Features:
- Automatic response schema extraction from @response_handler decorators
- Dynamic form generation in UI based on Pydantic/dataclass response types
- Checkpoint-based conversation storage for HIL requests/responses
- Resume workflow execution after user provides HIL response

Backend Changes:
- Add extract_response_type_from_executor() to introspect response handlers
- Enrich RequestInfoEvent with response_schema via _enrich_request_info_event_with_response_schema()
- Map RequestInfoEvent to response.input.requested OpenAI event format
- Store HIL responses in conversation history and restore checkpoints

Frontend Changes:
- Add HILInputModal component with SchemaFormRenderer for dynamic forms
- Support Pydantic BaseModel and dataclass response types
- Render enum fields as dropdowns, strings as text/textarea, numbers, booleans, arrays, objects
- Display original request context alongside response form

Testing:
- Add  tests for checkpoint storage (test_checkpoints.py)
- Add schema generation tests for all input types (test_schema_generation.py)
- Validate end-to-end HIL flow with spam workflow sample

This enables workflows to seamlessly pause execution and request structured user input
with type-safe, validated forms generated automatically from response type annotations.

* improve HIL support, improve workflow execution view

* ui updates

* ui updates

* improve HIL for workflows, add auth and view modes

* update workflow

* security improvements , ui fixes

* fix mypy error

* update loading spinner in ui

---------

Co-authored-by: Mark Wallace <127216156+markwallace-microsoft@users.noreply.github.com>
94eae24082 · 2025-11-07 23:28:32 +00:00
History
..

DevUI Samples

This folder contains sample agents and workflows designed to work with the Agent Framework DevUI - a lightweight web interface for running and testing agents interactively.

What is DevUI?

DevUI is a sample application that provides:

  • A web interface for testing agents and workflows
  • OpenAI-compatible API endpoints
  • Directory-based entity discovery
  • In-memory entity registration
  • Sample entity gallery

Note

: DevUI is a sample app for development and testing. For production use, build your own custom interface using the Agent Framework SDK.

Quick Start

Option 1: In-Memory Mode (Simplest)

Run a single sample directly. This demonstrates how to wrap agents and workflows programmatically without needing a directory structure:

cd python/samples/getting_started/devui
python in_memory_mode.py

This opens your browser at http://localhost:8090 with pre-configured agents and a basic workflow.

Option 2: Directory Discovery

Launch DevUI to discover all samples in this folder:

cd python/samples/getting_started/devui
devui

This starts the server at http://localhost:8080 with all agents and workflows available.

Sample Structure

Each agent/workflow follows a strict structure required by DevUI's discovery system:

agent_name/
├── __init__.py      # Must export: agent = ChatAgent(...)
├── agent.py         # Agent implementation
└── .env.example     # Example environment variables

Available Samples

Agents

Sample Description Features Required Environment Variables
weather_agent_azure/ Weather agent using Azure OpenAI with API key authentication Azure OpenAI integration, function calling, mock weather tools AZURE_OPENAI_API_KEY, AZURE_OPENAI_CHAT_DEPLOYMENT_NAME, AZURE_OPENAI_ENDPOINT
foundry_agent/ Weather agent using Azure AI Agent (Foundry) with Azure CLI authentication (run az login first) Azure AI Agent integration, Azure CLI authentication, mock weather tools AZURE_AI_PROJECT_ENDPOINT, FOUNDRY_MODEL_DEPLOYMENT_NAME

Workflows

Sample Description Features Required Environment Variables
workflow_agents/ Content review workflow with agents as executors Agents as workflow nodes, conditional routing based on structured outputs, quality-based paths (Writer → Reviewer → Editor/Publisher) AZURE_OPENAI_API_KEY, AZURE_OPENAI_CHAT_DEPLOYMENT_NAME, AZURE_OPENAI_ENDPOINT
spam_workflow/ 5-step email spam detection workflow with branching logic Sequential execution, conditional branching (spam vs. legitimate), multiple executors, mock spam detection None - uses mock data
fanout_workflow/ Advanced data processing workflow with parallel execution Fan-out/fan-in patterns, complex state management, multi-stage processing (validation → transformation → quality assurance) None - uses mock data

Standalone Examples

Sample Description Features
in_memory_mode.py Demonstrates programmatic entity registration without directory structure In-memory agent and workflow registration, multiple entities served from a single file, includes basic workflow, simplest way to get started

Environment Variables

Each sample that requires API keys includes a .env.example file. To use:

  1. Copy .env.example to .env in the same directory
  2. Fill in your actual API keys
  3. DevUI automatically loads .env files from entity directories

Alternatively, set environment variables globally:

export OPENAI_API_KEY="your-key-here"
export OPENAI_CHAT_MODEL_ID="gpt-4o"

Using DevUI with Your Own Agents

To make your agent discoverable by DevUI:

  1. Create a folder for your agent
  2. Add an __init__.py that exports agent or workflow
  3. (Optional) Add a .env file for environment variables

Example:

# my_agent/__init__.py
from agent_framework import ChatAgent
from agent_framework.openai import OpenAIChatClient

agent = ChatAgent(
    name="MyAgent",
    description="My custom agent",
    chat_client=OpenAIChatClient(),
    # ... your configuration
)

Then run:

devui /path/to/my/agents/folder

API Usage

DevUI exposes OpenAI-compatible endpoints:

curl -X POST http://localhost:8080/v1/responses \
  -H "Content-Type: application/json" \
  -d '{
    "model": "agent-framework",
    "input": "What is the weather in Seattle?",
    "extra_body": {"entity_id": "agent_directory_weather-agent_<uuid>"}
  }'

List available entities:

curl http://localhost:8080/v1/entities

Learn More

Troubleshooting

Missing API keys: Check your .env files or environment variables.

Import errors: Make sure you've installed the devui package:

pip install agent-framework-devui --pre

Port conflicts: DevUI uses ports 8080 (directory mode) and 8090 (in-memory mode) by default. Close other services or specify a different port:

devui --port 8888