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 → 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
History
Semantic Kernel → Microsoft Agent Framework Migration Samples
This gallery helps Semantic Kernel (SK) developers move to the Microsoft Agent Framework (AF) with minimal guesswork. Each script pairs SK code with its AF equivalent so you can compare primitives, tooling, and orchestration patterns side by side while you migrate production workloads.
What’s Included
What’s Included
Chat completion parity
- 01_basic_chat_completion.py — Minimal SK
ChatCompletionAgentand AFChatAgentconversation. - 02_chat_completion_with_tool.py — Adds a simple tool/function call in both SDKs.
- 03_chat_completion_thread_and_stream.py — Demonstrates thread reuse and streaming prompts.
Azure AI agent parity
- 01_basic_azure_ai_agent.py — Create and run an Azure AI agent end to end.
- 02_azure_ai_agent_with_code_interpreter.py — Enable hosted code interpreter/tool execution.
- 03_azure_ai_agent_threads_and_followups.py — Persist threads and follow-ups across invocations.
OpenAI Assistants API parity
- 01_basic_openai_assistant.py — Baseline assistant comparison.
- 02_openai_assistant_with_code_interpreter.py — Code interpreter tool usage.
- 03_openai_assistant_function_tool.py — Custom function tooling.
OpenAI Responses API parity
- 01_basic_responses_agent.py — Basic responses agent migration.
- 02_responses_agent_with_tool.py — Tool-augmented responses workflows.
- 03_responses_agent_structured_output.py — Structured JSON output alignment.
Copilot Studio parity
- 01_basic_copilot_studio_agent.py — Minimal Copilot Studio agent invocation.
- 02_copilot_studio_streaming.py — Streaming responses from Copilot Studio agents.
Orchestrations
- sequential.py — Step-by-step SK Team → AF
SequentialBuildermigration. - concurrent_basic.py — Concurrent orchestration parity.
- group_chat.py — Group chat coordination with an LLM-backed manager in both SDKs.
- handoff.py - Handoff coordination between agents.
- magentic.py — Magentic Team orchestration vs. AF builder wiring.
Processes
- fan_out_fan_in_process.py — Fan-out/fan-in comparison between SK Process Framework and AF workflows.
- nested_process.py — Nested process orchestration vs. AF sub-workflows.
Each script is fully async and the main() routine runs both implementations back to back so you can observe their outputs in a single execution.
Prerequisites
- Python 3.10 or later.
- Access to the necessary model endpoints (Azure OpenAI, OpenAI, Azure AI, Copilot Studio, etc.).
- Installed SDKs:
semantic-kerneland the Microsoft Agent Framework (pip install semantic-kernel agent-framework), or the repo’s editable packages if you are developing locally. - Service credentials exposed through environment variables (for example
OPENAI_API_KEY,AZURE_OPENAI_ENDPOINT,AZURE_OPENAI_KEY, or Copilot Studio auth settings).
Running Single-Agent Samples
From the repository root:
python samples/semantic-kernel-migration/chat_completion/01_basic_chat_completion.py
Every script accepts no CLI arguments and will first call the SK implementation, followed by the AF version. Adjust the prompt or credentials inside the file as necessary before running.
Running Orchestration & Workflow Samples
Advanced comparisons are split between samantic-kernel-migration/orchestrations (Sequential, Concurrent, Magentic) and samantic-kernel-migration/processes (fan-out/fan-in, nested). You can run them directly, or isolate dependencies in a throwaway virtual environment:
cd samples/semantic-kernel-migration
uv venv --python 3.10 .venv-migration
source .venv-migration/bin/activate
uv pip install semantic-kernel agent-framework
uv run python orchestrations/sequential.py
uv run python processes/fan_out_fan_in_process.py
Swap the script path for any other workflow or process sample. Deactivate the sandbox with deactivate when you are finished.
Tips for Migration
- Keep the original SK sample open while iterating on the AF equivalent; the code is intentionally formatted so you can copy/paste across SDKs.
- Threads/conversation state are explicit in AF. When porting SK code that relies on implicit thread reuse, call
agent.get_new_thread()and pass it into eachrun/run_streamcall. - Tools map cleanly: SK
@kernel_functionplugins translate to AF@toolcallables. Hosted tools (code interpreter, web search, MCP) are available only in AF—introduce them once parity is achieved. - For multi-agent orchestration, AF workflows expose checkpoints and resume capabilities that SK Process/Team abstractions do not. Use the workflow samples as a blueprint when modernizing complex agent graphs.