agent-framework/python/packages/devui/README.md

DevUI - A Sample App for Running Agents and Workflows

A lightweight, standalone sample app interface for running entities (agents/workflows) in the Microsoft Agent Framework supporting directory-based discovery, in-memory entity registration, and sample entity gallery.

Important

DevUI is a sample app to help you get started with the Agent Framework. It is not intended for production use. For production, or for features beyond what is provided in this sample app, it is recommended that you build your own custom interface and API server using the Agent Framework SDK.

Quick Start

# Install
pip install agent-framework-devui --pre

You can also launch it programmatically

from agent_framework import ChatAgent
from agent_framework.openai import OpenAIChatClient
from agent_framework.devui import serve

def get_weather(location: str) -> str:
    """Get weather for a location."""
    return f"Weather in {location}: 72°F and sunny"

# Create your agent
agent = ChatAgent(
    name="WeatherAgent",
    chat_client=OpenAIChatClient(),
    tools=[get_weather]
)

# Launch debug UI - that's it!
serve(entities=[agent], auto_open=True)
# → Opens browser to http://localhost:8080

In addition, if you have agents/workflows defined in a specific directory structure (see below), you can launch DevUI from the cli to discover and run them.

# Launch web UI + API server
devui ./agents --port 8080
# → Web UI: http://localhost:8080
# → API: http://localhost:8080/v1/*

When DevUI starts with no discovered entities, it displays a sample entity gallery with curated examples from the Agent Framework repository to help you get started quickly.

Directory Structure

For your agents to be discovered by the DevUI, they must be organized in a directory structure like below. Each agent/workflow must have an __init__.py that exports the required variable (agent or workflow).

Note: .env files are optional but will be automatically loaded if present in the agent/workflow directory or parent entities directory. Use them to store API keys, configuration variables, and other environment-specific settings.

agents/
├── weather_agent/
│   ├── __init__.py      # Must export: agent = ChatAgent(...)
│   ├── agent.py
│   └── .env             # Optional: API keys, config vars
├── my_workflow/
│   ├── __init__.py      # Must export: workflow = WorkflowBuilder()...
│   ├── workflow.py
│   └── .env             # Optional: environment variables
└── .env                 # Optional: shared environment variables

Viewing Telemetry (Otel Traces) in DevUI

Agent Framework emits OpenTelemetry (Otel) traces for various operations. You can view these traces in DevUI by enabling tracing when starting the server.

devui ./agents --tracing framework

OpenAI-Compatible API

For convenience, DevUI provides an OpenAI Responses backend API. This means you can run the backend and also use the OpenAI client sdk to connect to it. Use agent/workflow name as the model, and set streaming to True as needed.

# Simple - use your entity name as the model
curl -X POST http://localhost:8080/v1/responses \
  -H "Content-Type: application/json" \
  -d @- << 'EOF'
{
  "model": "weather_agent",
  "input": "Hello world"
}

Or use the OpenAI Python SDK:

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8080/v1",
    api_key="not-needed"  # API key not required for local DevUI
)

response = client.responses.create(
    model="weather_agent",  # Your agent/workflow name
    input="What's the weather in Seattle?"
)

# Extract text from response
print(response.output[0].content[0].text)
# Supports streaming with stream=True

Multi-turn Conversations

Use the standard OpenAI conversation parameter for multi-turn conversations:

# Create a conversation
conversation = client.conversations.create(
    metadata={"agent_id": "weather_agent"}
)

# Use it across multiple turns
response1 = client.responses.create(
    model="weather_agent",
    input="What's the weather in Seattle?",
    conversation=conversation.id
)

response2 = client.responses.create(
    model="weather_agent",
    input="How about tomorrow?",
    conversation=conversation.id  # Continues the conversation!
)

How it works: DevUI automatically retrieves the conversation's message history from the stored thread and passes it to the agent. You don't need to manually manage message history - just provide the same conversation ID for follow-up requests.

CLI Options

devui [directory] [options]

Options:
  --port, -p      Port (default: 8080)
  --host          Host (default: 127.0.0.1)
  --headless      API only, no UI
  --config        YAML config file
  --tracing       none|framework|workflow|all
  --reload        Enable auto-reload

Key Endpoints

API Mapping

Given that DevUI offers an OpenAI Responses API, it internally maps messages and events from Agent Framework to OpenAI Responses API events (in _mapper.py). For transparency, this mapping is shown below:

Agent Framework Content	OpenAI Event/Type	Status
TextContent	response.output_text.delta	Standard
TextReasoningContent	response.reasoning.delta	Standard
FunctionCallContent (initial)	response.output_item.added	Standard
FunctionCallContent (args)	response.function_call_arguments.delta	Standard
FunctionResultContent	response.function_result.complete	DevUI
ErrorContent	response.error	Standard
UsageContent	Final Response.usage field (not streamed)	Standard
WorkflowEvent	response.workflow_event.complete	DevUI
DataContent, UriContent	response.trace.complete	DevUI

• Standard = OpenAI Responses API spec
• DevUI = Custom extensions for Agent Framework features (workflows, traces, function results)

OpenAI Responses API Compliance

DevUI follows the OpenAI Responses API specification for maximum compatibility:

Standard OpenAI Types Used:

• ResponseOutputItemAddedEvent - Output item notifications (function calls)
• Response.usage - Token usage (in final response, not streamed)
• All standard text, reasoning, and function call events

Custom DevUI Extensions:

• response.function_result.complete - Function execution results (DevUI executes functions, OpenAI doesn't)
• response.workflow_event.complete - Agent Framework workflow events
• response.trace.complete - Execution traces for debugging

These custom extensions are clearly namespaced and can be safely ignored by standard OpenAI clients.

Entity Management

• GET /v1/entities - List discovered agents/workflows
• GET /v1/entities/{entity_id}/info - Get detailed entity information
• POST /v1/entities/add - Add entity from URL (for gallery samples)
• DELETE /v1/entities/{entity_id} - Remove remote entity

Execution (OpenAI Responses API)

• POST /v1/responses - Execute agent/workflow (streaming or sync)

Conversations (OpenAI Standard)

• POST /v1/conversations - Create conversation
• GET /v1/conversations/{id} - Get conversation
• POST /v1/conversations/{id} - Update conversation metadata
• DELETE /v1/conversations/{id} - Delete conversation
• GET /v1/conversations?agent_id={id} - List conversations (DevUI extension)
• POST /v1/conversations/{id}/items - Add items to conversation
• GET /v1/conversations/{id}/items - List conversation items
• GET /v1/conversations/{id}/items/{item_id} - Get conversation item

Health

• GET /health - Health check

Implementation

• Discovery: agent_framework_devui/_discovery.py
• Execution: agent_framework_devui/_executor.py
• Message Mapping: agent_framework_devui/_mapper.py
• Conversations: agent_framework_devui/_conversations.py
• API Server: agent_framework_devui/_server.py
• CLI: agent_framework_devui/_cli.py

Examples

See working implementations in python/samples/getting_started/devui/

License

MIT