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/02-agents/context_providers/redis/redis_basics.py

@@ -32,12 +32,12 @@ import os
 
 from agent_framework import Message, tool
 from agent_framework.openai import OpenAIChatClient
-from agent_framework_redis._provider import RedisProvider
+from agent_framework.redis import RedisContextProvider
 from redisvl.extensions.cache.embeddings import EmbeddingsCache
 from redisvl.utils.vectorize import OpenAITextVectorizer
 
 
-# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/02-agents/tools/function_tool_with_approval.py and samples/02-agents/tools/function_tool_with_approval_and_threads.py.
+# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/02-agents/tools/function_tool_with_approval.py and samples/02-agents/tools/function_tool_with_approval_and_sessions.py.
 @tool(approval_mode="never_require")
 def search_flights(origin_airport_code: str, destination_airport_code: str, detailed: bool = False) -> str:
     """Simulated flight-search tool to demonstrate tool memory.
@@ -104,7 +104,7 @@ async def main() -> None:
     # Recommend default for OPENAI_CHAT_MODEL_ID is gpt-4o-mini
 
     # We attach an embedding vectorizer so the provider can perform hybrid (text + vector)
-    # retrieval. If you prefer text-only retrieval, instantiate RedisProvider without the
+    # retrieval. If you prefer text-only retrieval, instantiate RedisContextProvider without the
     # 'vectorizer' and vector_* parameters.
     vectorizer = OpenAITextVectorizer(
         model="text-embedding-ada-002",
@@ -114,7 +114,7 @@ async def main() -> None:
     # The provider manages persistence and retrieval. application_id/agent_id/user_id
     # scope data for multi-tenant separation; thread_id (set later) narrows to a
     # specific conversation.
-    provider = RedisProvider(
+    provider = RedisContextProvider(
         redis_url="redis://localhost:6379",
         index_name="redis_basics",
         application_id="matrix_of_kermits",
@@ -133,21 +133,27 @@ async def main() -> None:
         Message("system", ["runA CONVO: System Message"]),
     ]
 
-    # Declare/start a conversation/thread and write messages under 'runA'.
-    # Threads are logical boundaries used by the provider to group and retrieve
-    # conversation-specific context.
-    await provider.thread_created(thread_id="runA")
-    await provider.invoked(request_messages=messages)
+    # Use the provider's before_run/after_run API to store and retrieve messages.
+    # In practice, the agent handles this automatically; this shows the low-level API.
+    from agent_framework import AgentSession, SessionContext
 
-    # Retrieve relevant memories for a hypothetical model call. The provider uses
-    # the current request messages as the retrieval query and returns context to
-    # be injected into the model's instructions.
-    ctx = await provider.invoking([Message("system", ["B: Assistant Message"])])
+    session = AgentSession(session_id="runA")
+    context = SessionContext()
+    context.extend_messages("input", messages)
+    state = session.state
+
+    # Store messages via after_run
+    await provider.after_run(agent=None, session=session, context=context, state=state)
+
+    # Retrieve relevant memories via before_run
+    query_context = SessionContext()
+    query_context.extend_messages("input", [Message("system", ["B: Assistant Message"])])
+    await provider.before_run(agent=None, session=session, context=query_context, state=state)
 
     # Inspect retrieved memories that would be injected into instructions
     # (Debug-only output so you can verify retrieval works as expected.)
-    print("Model Invoking Result:")
-    print(ctx)
+    print("Before Run Result:")
+    print(query_context)
 
     # Drop / delete the provider index in Redis
     await provider.redis_index.delete()
@@ -163,7 +169,7 @@ async def main() -> None:
         cache=EmbeddingsCache(name="openai_embeddings_cache", redis_url="redis://localhost:6379"),
     )
     # Recreate a clean index so the next scenario starts fresh
-    provider = RedisProvider(
+    provider = RedisContextProvider(
         redis_url="redis://localhost:6379",
         index_name="redis_basics_2",
         prefix="context_2",
@@ -187,7 +193,7 @@ async def main() -> None:
             "Before answering, always check for stored context"
         ),
         tools=[],
-        context_provider=provider,
+        context_providers=[provider],
     )
 
     # Teach a user preference; the agent writes this to the provider's memory
@@ -210,7 +216,7 @@ async def main() -> None:
     print("\n3. Agent + provider + tool: store and recall tool-derived context")
     print("-" * 40)
     # Text-only provider (full-text search only). Omits vectorizer and related params.
-    provider = RedisProvider(
+    provider = RedisContextProvider(
         redis_url="redis://localhost:6379",
         index_name="redis_basics_3",
         prefix="context_3",
@@ -229,7 +235,7 @@ async def main() -> None:
             "Before answering, always check for stored context"
         ),
         tools=search_flights,
-        context_provider=provider,
+        context_providers=[provider],
     )
     # Invoke the tool; outputs become part of memory/context
     query = "Are there any flights from new york city (jfk) to la? Give me details"