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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
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Multi-Agent
This sample demonstrates how to host multiple AI agents with different tools in a single worker-client setup using the Durable Task Scheduler.
Key Concepts Demonstrated
- Hosting multiple agents (WeatherAgent and MathAgent) in a single worker process.
- Each agent with its own specialized tools and instructions.
- Interacting with different agents using separate conversation threads.
- Worker-client architecture for multi-agent systems.
Environment Setup
See the README.md file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
Running the Sample
With the environment setup, you can run the sample using the combined approach or separate worker and client processes:
Option 1: Combined (Recommended for Testing)
cd samples/getting_started/durabletask/02_multi_agent
python sample.py
Option 2: Separate Processes
Start the worker in one terminal:
python worker.py
In a new terminal, run the client:
python client.py
The client will interact with both agents:
Starting Durable Task Multi-Agent Client...
Using taskhub: default
Using endpoint: http://localhost:8080
================================================================================
Testing WeatherAgent
================================================================================
Created weather conversation thread: <guid>
User: What is the weather in Seattle?
๐ง [TOOL CALLED] get_weather(location=Seattle)
โ [TOOL RESULT] {'location': 'Seattle', 'temperature': 72, 'conditions': 'Sunny', 'humidity': 45}
WeatherAgent: The current weather in Seattle is sunny with a temperature of 72ยฐF and 45% humidity.
================================================================================
Testing MathAgent
================================================================================
Created math conversation thread: <guid>
User: Calculate a 20% tip on a $50 bill
๐ง [TOOL CALLED] calculate_tip(bill_amount=50.0, tip_percentage=20.0)
โ [TOOL RESULT] {'bill_amount': 50.0, 'tip_percentage': 20.0, 'tip_amount': 10.0, 'total': 60.0}
MathAgent: For a $50 bill with a 20% tip, the tip amount is $10.00 and the total is $60.00.
Viewing Agent State
You can view the state of both agents in the Durable Task Scheduler dashboard:
- Open your browser and navigate to
http://localhost:8082 - In the dashboard, you can view:
- The state of both WeatherAgent and MathAgent entities (dafx-WeatherAgent, dafx-MathAgent)
- Each agent's conversation state across multiple interactions