Python: [BREAKING] Simplify API: ChatAgent -> Agent, ChatMessage -> Message (#3747)

* [BREAKING] Rename ChatAgent -> Agent, ChatMessage -> Message, ChatClientProtocol -> SupportsChatGetResponse

Simplify the public API by removing redundant 'Chat' prefix from core types:
- ChatAgent -> Agent
- RawChatAgent -> RawAgent
- ChatMessage -> Message
- ChatClientProtocol -> SupportsChatGetResponse

Also renamed internal WorkflowMessage (was Message in _runner_context) to avoid collision.

No backward compatibility aliases - this is a clean breaking change.

* [BREAKING] Rename Agent chat_client parameter to client

* Fix rebase issues: WorkflowMessage references and broken markdown links

* Fix formatting and lint issues from code quality checks

* Fix import ordering in workflow sample files

* fixed rebase

* Fix test failures: use WorkflowMessage and A2AMessage after ChatMessage→Message rename

- Replace Message(data=..., source_id=...) with WorkflowMessage(...) in workflow tests
- Fix isinstance check in A2A agent to use A2AMessage instead of Message
- Fix import in test_workflow_observability.py (Message→WorkflowMessage)

* Fix lint, fmt, and sample errors after ChatMessage→Message rename

- Auto-fix 70+ ruff lint issues across samples (ChatMessage→Message refs)
- Fix HostedVectorStoreContent→Content.from_hosted_vector_store in file search sample
- Fix _normalize_messages→normalize_messages in custom agent sample
- Fix context.terminate→raise MiddlewareTermination in middleware samples
- Fix with_update_hook→with_transform_hook in override middleware sample
- Add TOptions_co import back to custom_chat_client sample
- Add noqa for FastAPI File() default in chatkit sample
- Fix B023 loop variable capture in weather agent sample

* fix: update Agent constructor calls from chat_client to client in declaration-only tool tests

* fix: add register_cleanup to devui lazy-loading proxy and type stub

* fixed tests and updated new pieces

* fix agui typevar

* fix merge errors

* fix merge conflicts

* fiux merge

* Remove unused links

---------

Co-authored-by: Evan Mattson <evan.mattson@microsoft.com>

python/samples/getting_started/orchestrations/README.md

@@ -30,32 +30,29 @@ from agent_framework.orchestrations import (
 
 | Sample                                            | File                                                                                 | Concepts                                                                                                         |
 | ------------------------------------------------- | ------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------- |
-| Concurrent Orchestration (Default Aggregator)     | [concurrent_agents.py](./concurrent_agents.py)                                       | Fan-out to multiple agents; fan-in with default aggregator returning combined ChatMessages                       |
+| Concurrent Orchestration (Default Aggregator)     | [concurrent_agents.py](./concurrent_agents.py)                                       | Fan-out to multiple agents; fan-in with default aggregator returning combined Messages                       |
 | Concurrent Orchestration (Custom Aggregator)      | [concurrent_custom_aggregator.py](./concurrent_custom_aggregator.py)                 | Override aggregator via callback; summarize results with an LLM                                                  |
-| Concurrent Orchestration (Custom Agent Executors) | [concurrent_custom_agent_executors.py](./concurrent_custom_agent_executors.py)       | Child executors own ChatAgents; concurrent fan-out/fan-in via ConcurrentBuilder                                  |
-| Concurrent Orchestration (Participant Factory)    | [concurrent_participant_factory.py](./concurrent_participant_factory.py)             | Use participant factories for state isolation between workflow instances                                         |
+| Concurrent Orchestration (Custom Agent Executors) | [concurrent_custom_agent_executors.py](./concurrent_custom_agent_executors.py)       | Child executors own Agents; concurrent fan-out/fan-in via ConcurrentBuilder                                  |
 | Group Chat with Agent Manager                     | [group_chat_agent_manager.py](./group_chat_agent_manager.py)                         | Agent-based manager using `with_orchestrator(agent=)` to select next speaker                                     |
 | Group Chat Philosophical Debate                   | [group_chat_philosophical_debate.py](./group_chat_philosophical_debate.py)           | Agent manager moderates long-form, multi-round debate across diverse participants                                |
 | Group Chat with Simple Function Selector          | [group_chat_simple_selector.py](./group_chat_simple_selector.py)                     | Group chat with a simple function selector for next speaker                                                      |
 | Handoff (Simple)                                  | [handoff_simple.py](./handoff_simple.py)                                             | Single-tier routing: triage agent routes to specialists, control returns to user after each specialist response  |
 | Handoff (Autonomous)                              | [handoff_autonomous.py](./handoff_autonomous.py)                                     | Autonomous mode: specialists iterate independently until invoking a handoff tool using `.with_autonomous_mode()` |
-| Handoff (Participant Factory)                     | [handoff_participant_factory.py](./handoff_participant_factory.py)                   | Use participant factories for state isolation between workflow instances                                         |
 | Handoff with Code Interpreter                     | [handoff_with_code_interpreter_file.py](./handoff_with_code_interpreter_file.py)     | Retrieve file IDs from code interpreter output in handoff workflow                                               |
 | Magentic Workflow (Multi-Agent)                   | [magentic.py](./magentic.py)                                                         | Orchestrate multiple agents with Magentic manager and streaming                                                  |
 | Magentic + Human Plan Review                      | [magentic_human_plan_review.py](./magentic_human_plan_review.py)                     | Human reviews/updates the plan before execution                                                                  |
 | Magentic + Checkpoint Resume                      | [magentic_checkpoint.py](./magentic_checkpoint.py)                                   | Resume Magentic orchestration from saved checkpoints                                                             |
 | Sequential Orchestration (Agents)                 | [sequential_agents.py](./sequential_agents.py)                                       | Chain agents sequentially with shared conversation context                                                       |
 | Sequential Orchestration (Custom Executor)        | [sequential_custom_executors.py](./sequential_custom_executors.py)                   | Mix agents with a summarizer that appends a compact summary                                                      |
-| Sequential Orchestration (Participant Factories)  | [sequential_participant_factory.py](./sequential_participant_factory.py)             | Use participant factories for state isolation between workflow instances                                         |
 
 ## Tips
 
 **Magentic checkpointing tip**: Treat `MagenticBuilder.participants` keys as stable identifiers. When resuming from a checkpoint, the rebuilt workflow must reuse the same participant names; otherwise the checkpoint cannot be applied and the run will fail fast.
 
-**Handoff workflow tip**: Handoff workflows maintain the full conversation history including any `ChatMessage.additional_properties` emitted by your agents. This ensures routing metadata remains intact across all agent transitions. For specialist-to-specialist handoffs, use `.add_handoff(source, targets)` to configure which agents can route to which others with a fluent, type-safe API.
+**Handoff workflow tip**: Handoff workflows maintain the full conversation history including any `Message.additional_properties` emitted by your agents. This ensures routing metadata remains intact across all agent transitions. For specialist-to-specialist handoffs, use `.add_handoff(source, targets)` to configure which agents can route to which others with a fluent, type-safe API.
 
 **Sequential orchestration note**: Sequential orchestration uses a few small adapter nodes for plumbing:
-- `input-conversation` normalizes input to `list[ChatMessage]`
+- `input-conversation` normalizes input to `list[Message]`
 - `to-conversation:<participant>` converts agent responses into the shared conversation
 - `complete` publishes the final output event (type='output')