* 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
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:
- Copy
.env.exampleto.envin the same directory - Fill in your actual API keys
- DevUI automatically loads
.envfiles 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:
- Create a folder for your agent
- Add an
__init__.pythat exportsagentorworkflow - (Optional) Add a
.envfile 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