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agent-framework/python/samples/getting_started/devui
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Eduard van Valkenburg 838a7fd61d Python: [BREAKING] Types API Review improvements (#3647)
* Replace Role and FinishReason classes with NewType + Literal

- Remove EnumLike metaclass from _types.py
- Replace Role class with NewType('Role', str) + RoleLiteral
- Replace FinishReason class with NewType('FinishReason', str) + FinishReasonLiteral
- Update all usages across codebase to use string literals
- Remove .value access patterns (direct string comparison now works)
- Add backward compatibility for legacy dict serialization format
- Update tests to reflect new string-based types

Addresses #3591, #3615

* Simplify ChatResponse and AgentResponse type hints (#3592)

- Remove overloads from ChatResponse.__init__
- Remove text parameter from ChatResponse.__init__
- Remove | dict[str, Any] from finish_reason and usage_details params
- Remove **kwargs from AgentResponse.__init__
- Both now accept ChatMessage | Sequence[ChatMessage] | None for messages
- Update docstrings and examples to reflect changes
- Fix tests that were using removed kwargs
- Fix Role type hint usage in ag-ui utils

* Remove text parameter from ChatResponseUpdate and AgentResponseUpdate (#3597)

- Remove text parameter from ChatResponseUpdate.__init__
- Remove text parameter from AgentResponseUpdate.__init__
- Remove **kwargs from both update classes
- Simplify contents parameter type to Sequence[Content] | None
- Update all usages to use contents=[Content.from_text(...)] pattern
- Fix imports in test files
- Update docstrings and examples

* Rename from_chat_response_updates to from_updates (#3593)

- ChatResponse.from_chat_response_updates → ChatResponse.from_updates
- ChatResponse.from_chat_response_generator → ChatResponse.from_update_generator
- AgentResponse.from_agent_run_response_updates → AgentResponse.from_updates

* Remove try_parse_value method from ChatResponse and AgentResponse (#3595)

- Remove try_parse_value method from ChatResponse
- Remove try_parse_value method from AgentResponse
- Remove try_parse_value calls from from_updates and from_update_generator methods
- Update samples to use try/except with response.value instead
- Update tests to use response.value pattern
- Users should now use response.value with try/except for safe parsing

* Add agent_id to AgentResponse and clarify author_name documentation (#3596)

- Add agent_id parameter to AgentResponse class
- Document that author_name is on ChatMessage objects, not responses
- Update ChatResponse docstring with author_name note
- Update AgentResponse docstring with author_name note

* Simplify ChatMessage.__init__ signature (#3618)

- Make contents a positional argument accepting Sequence[Content | str]
- Auto-convert strings in contents to TextContent
- Remove overloads, keep text kwarg for backward compatibility with serialization
- Update _parse_content_list to handle string items
- Update all usages across codebase to use new format: ChatMessage("role", ["text"])

* Allow Content as input on run and get_response

- Update prepare_messages and normalize_messages to accept Content
- Update type signatures in _agents.py and _clients.py
- Add tests for Content input handling

* Fix ChatMessage usage across packages and samples

Update all remaining ChatMessage(role=..., text=...) to use new
ChatMessage('role', ['text']) signature.

* Fix Role string usage and response format parsing

- Fix redis provider: remove .value access on string literals
- Fix durabletask ensure_response_format: set _response_format before accessing .value

* Fix ollama .value and ai_model_id issues, handle None in content list

- Fix ollama _chat_client: remove .value on string literals
- Fix ollama _chat_client: rename ai_model_id to model_id
- Fix _parse_content_list: skip None values gracefully

* Fix A2AAgent type signature to include Content

* Fix Role/FinishReason NewType dict annotations and improve test coverage to 95%

* Fix mypy errors for Role/FinishReason NewType usage

* Fix Role.TOOL and Role.ASSISTANT usage in _orchestrator_helpers.py

* Fix Role NewType usage in durabletask _models.py
838a7fd61d · 2026-02-04 10:13:23 +00:00
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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
declarative/ Declarative YAML workflow with conditional branching YAML-based workflow definition, conditional logic, no Python code required None - uses mock data
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