Python: [BREAKING] PR2 — Wire context provider pipeline, remove old types, update all consumers (#3850)

* PR2: Wire context provider pipeline and update all internal consumers

- Replace AgentThread with AgentSession across all packages
- Replace ContextProvider with BaseContextProvider across all packages
- Replace context_provider param with context_providers (Sequence)
- Replace thread= with session= in run() signatures
- Replace get_new_thread() with create_session()
- Add get_session(service_session_id) to agent interface
- DurableAgentThread -> DurableAgentSession
- Remove _notify_thread_of_new_messages from WorkflowAgent
- Wire before_run/after_run context provider pipeline in RawAgent
- Auto-inject InMemoryHistoryProvider when no providers configured

* fix: update all tests for context provider pipeline, fix lazy-loaders, remove old test files

* refactor: update all sample files for context provider pipeline (AgentThread→AgentSession, ContextProvider→BaseContextProvider)

* fix: update remaining ag-ui references (client docstring, getting_started sample)

* fix: make get_session service_session_id keyword-only to avoid confusion with session_id

* refactor: rename _RunContext.thread_messages to session_messages

* refactor: remove _threads.py, _memory.py, and old provider files; migrate devui to use plain message lists

* rename: remove _new_ prefix from test files

* refactor: rewrite SlidingWindowChatMessageStore as SlidingWindowHistoryProvider(InMemoryHistoryProvider)

* fix: read full history from session state directly instead of reaching into provider internals

* fix: update stale .pyi stubs, sample imports, and README references for new provider types

* fix: remove stale message_store, _notify_thread_of_new_messages, and session_id.key references in samples

* refactor: merge context_providers and sessions sample folders into sessions, remove aggregate_context_provider

* refactor: UserInfoMemory stores state in session.state instead of instance attributes

* feat: add Pydantic BaseModel support to session state serialization

Pydantic models stored in session.state are now automatically serialized
via model_dump() and restored via model_validate() during to_dict()/from_dict()
round-trips. Models are auto-registered on first serialization; use
register_state_type() for cold-start deserialization.

Also export register_state_type as a public API.

* fix mem0

* Update sample README links and descriptions for session terminology

- Replace 'thread' with 'session' in sample descriptions across all READMEs
- Update file links for renamed samples (mem0_sessions, redis_sessions, etc.)
- Fix Threads section → Sessions section in main samples/README.md
- Update tools, middleware, workflows, durabletask, azure_functions READMEs
- Update architecture diagrams in concepts/tools/README.md
- Update migration guides (autogen, semantic-kernel)

* Fix broken Redis README link to renamed sample

* Fix Mem0 OSS client search: pass scoping params as direct kwargs

AsyncMemory (OSS) expects user_id/agent_id/run_id as direct kwargs,
while AsyncMemoryClient (Platform) expects them in a filters dict.
Adds tests for both client types.

Port of fix from #3844 to new Mem0ContextProvider.

* Fix rebase issues: restore missing _conversation_state.py and checkpoint decode logic

- Add back _conversation_state.py (encode/decode_chat_messages) lost in rebase
- Fix on_checkpoint_restore to decode cache/conversation with decode_chat_messages
- Fix on_checkpoint_restore to use decode_checkpoint_value for pending requests
- Add tests/workflow/__init__.py for relative import support
- Fix test_agent_executor checkpoint selection (checkpoints[1] not superstep)

* Add STORES_BY_DEFAULT ClassVar to skip redundant InMemoryHistoryProvider injection

Chat clients that store history server-side by default (OpenAI Responses API,
Azure AI Agent) now declare STORES_BY_DEFAULT = True. The agent checks this
during auto-injection and skips InMemoryHistoryProvider unless the user
explicitly sets store=False.

* Fix broken markdown links in azure_ai and redis READMEs

* Fix getting-started samples to use session API instead of removed thread/ContextProvider API

* updates to workflow as agent

* fix group chat import

* Rename Thread→Session throughout, fix service_session_id propagation, remove stale AGUIThread

- Fix: Propagate conversation_id from ChatResponse back to session.service_session_id
  in both streaming and non-streaming paths in _agents.py
- Rename AgentThreadException → AgentSessionException
- Remove stale AGUIThread from ag_ui lazy-loader
- Rename use_service_thread → use_service_session in ag-ui package
- Rename test functions from *_thread_* to *_session_*
- Rename sample files from *_thread* to *_session*
- Update docstrings and comments: thread → session
- Update _mcp.py kwargs filter: add 'session' alongside 'thread'
- Fix ContinuationToken docstring example: thread=thread → session=session
- Fix _clients.py docstring: 'Agent threads' → 'Agent sessions'

* Fix broken markdown links after thread→session file renames

* fix azure ai test

python/samples/03-workflows/checkpoint/workflow_as_agent_checkpoint.py

@@ -15,9 +15,9 @@ What you learn:
 - How to resume a workflow-as-agent from a checkpoint
 
 Key concepts:
-- Thread (AgentThread): Maintains conversation history across agent invocations
+- Thread (AgentSession): Maintains conversation history across agent invocations
 - Checkpoint: Persists workflow execution state for pause/resume capability
-- These are complementary: threads track conversation, checkpoints track workflow state
+- These are complementary: sessions track conversation, checkpoints track workflow state
 
 Prerequisites:
 - AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
@@ -28,8 +28,6 @@ import asyncio
 import os
 
 from agent_framework import (
-    AgentThread,
-    ChatMessageStore,
     InMemoryCheckpointStorage,
 )
 from agent_framework.azure import AzureOpenAIResponsesClient
@@ -102,21 +100,21 @@ async def checkpointing_with_thread() -> None:
     workflow = SequentialBuilder(participants=[assistant]).build()
     agent = workflow.as_agent(name="MemoryAgent")
 
-    # Create both thread (for conversation) and checkpoint storage (for workflow state)
-    thread = AgentThread(message_store=ChatMessageStore())
+    # Create both session (for conversation) and checkpoint storage (for workflow state)
+    session = agent.create_session()
     checkpoint_storage = InMemoryCheckpointStorage()
 
     # First turn
     query1 = "My favorite color is blue. Remember that."
     print(f"\n[Turn 1] User: {query1}")
-    response1 = await agent.run(query1, thread=thread, checkpoint_storage=checkpoint_storage)
+    response1 = await agent.run(query1, session=session, checkpoint_storage=checkpoint_storage)
     if response1.messages:
         print(f"[assistant]: {response1.messages[0].text}")
 
-    # Second turn - agent should remember from thread history
+    # Second turn - agent should remember from session history
     query2 = "What's my favorite color?"
     print(f"\n[Turn 2] User: {query2}")
-    response2 = await agent.run(query2, thread=thread, checkpoint_storage=checkpoint_storage)
+    response2 = await agent.run(query2, session=session, checkpoint_storage=checkpoint_storage)
     if response2.messages:
         print(f"[assistant]: {response2.messages[0].text}")
 
@@ -124,9 +122,9 @@ async def checkpointing_with_thread() -> None:
     checkpoints = await checkpoint_storage.list_checkpoints(workflow_name=workflow.name)
     print(f"\nTotal checkpoints across both turns: {len(checkpoints)}")
 
-    if thread.message_store:
-        history = await thread.message_store.list_messages()
-        print(f"Messages in thread history: {len(history)}")
+    memory_state = session.state.get("memory", {})
+    history = memory_state.get("messages", [])
+    print(f"Messages in session history: {len(history)}")
 
 
 async def streaming_with_checkpoints() -> None: