mirror of
https://github.com/microsoft/agent-framework.git
synced 2026-06-16 21:04:09 +08:00
Eduard van Valkenburg
838a7fd61d
* 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 โ ChatResponse.from_updates
- ChatResponse.from_chat_response_generator โ ChatResponse.from_update_generator
- AgentResponse.from_agent_run_response_updates โ 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
ยท
2026-02-04 10:13:23 +00:00
History
Declarative Workflows
Declarative workflows allow you to define multi-agent orchestration patterns in YAML, including:
- Variable manipulation and state management
- Control flow (loops, conditionals, branching)
- Agent invocations
- Human-in-the-loop patterns
See the main workflows README for the list of available samples.
Prerequisites
pip install agent-framework-declarative
Running Samples
Each sample directory contains:
workflow.yaml- The declarative workflow definitionmain.py- Python code to load and execute the workflowREADME.md- Sample-specific documentation
To run a sample:
cd <sample_directory>
python main.py
Workflow Structure
A basic workflow YAML file looks like:
name: my-workflow
description: A simple workflow example
actions:
- kind: SetValue
path: turn.greeting
value: Hello, World!
- kind: SendActivity
activity:
text: =turn.greeting
Action Types
Variable Actions
SetValue- Set a variable in stateSetVariable- Set a variable (.NET style naming)AppendValue- Append to a listResetVariable- Clear a variable
Control Flow
If- Conditional branchingSwitch- Multi-way branchingForeach- Iterate over collectionsRepeatUntil- Loop until conditionGotoAction- Jump to labeled action
Output
SendActivity- Send text/attachments to userEmitEvent- Emit custom events
Agent Invocation
InvokeAzureAgent- Call an Azure AI agentInvokePromptAgent- Call a local prompt agent
Human-in-Loop
Question- Request user inputWaitForInput- Pause for external input