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* 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
113 lines
4.1 KiB
Python
113 lines
4.1 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import time
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from collections.abc import Awaitable, Callable
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from random import randint
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from typing import Annotated
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from agent_framework import (
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AgentContext,
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FunctionInvocationContext,
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tool,
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)
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from agent_framework.azure import AzureAIAgentClient
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from azure.identity.aio import AzureCliCredential
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from pydantic import Field
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"""
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Function-based MiddlewareTypes Example
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This sample demonstrates how to implement middleware using simple async functions instead of classes.
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The example includes:
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- Security middleware that validates agent requests for sensitive information
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- Logging middleware that tracks function execution timing and parameters
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- Performance monitoring to measure execution duration
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Function-based middleware is ideal for simple, stateless operations and provides a more
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lightweight approach compared to class-based middleware. Both agent and function middleware
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can be implemented as async functions that accept context and call_next parameters.
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"""
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# 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.
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@tool(approval_mode="never_require")
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def get_weather(
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location: Annotated[str, Field(description="The location to get the weather for.")],
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) -> str:
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"""Get the weather for a given location."""
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conditions = ["sunny", "cloudy", "rainy", "stormy"]
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return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
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async def security_agent_middleware(
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context: AgentContext,
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call_next: Callable[[], Awaitable[None]],
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) -> None:
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"""Agent middleware that checks for security violations."""
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# Check for potential security violations in the query
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# For this example, we'll check the last user message
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last_message = context.messages[-1] if context.messages else None
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if last_message and last_message.text:
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query = last_message.text
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if "password" in query.lower() or "secret" in query.lower():
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print("[SecurityAgentMiddleware] Security Warning: Detected sensitive information, blocking request.")
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# Simply don't call call_next() to prevent execution
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return
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print("[SecurityAgentMiddleware] Security check passed.")
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await call_next()
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async def logging_function_middleware(
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context: FunctionInvocationContext,
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call_next: Callable[[], Awaitable[None]],
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) -> None:
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"""Function middleware that logs function calls."""
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function_name = context.function.name
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print(f"[LoggingFunctionMiddleware] About to call function: {function_name}.")
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start_time = time.time()
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await call_next()
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end_time = time.time()
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duration = end_time - start_time
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print(f"[LoggingFunctionMiddleware] Function {function_name} completed in {duration:.5f}s.")
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async def main() -> None:
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"""Example demonstrating function-based middleware."""
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print("=== Function-based MiddlewareTypes Example ===")
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# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
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# authentication option.
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async with (
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AzureCliCredential() as credential,
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AzureAIAgentClient(credential=credential).as_agent(
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name="WeatherAgent",
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instructions="You are a helpful weather assistant.",
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tools=get_weather,
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middleware=[security_agent_middleware, logging_function_middleware],
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) as agent,
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):
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# Test with normal query
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print("\n--- Normal Query ---")
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query = "What's the weather like in Tokyo?"
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print(f"User: {query}")
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result = await agent.run(query)
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print(f"Agent: {result.text if result.text else 'No response'}\n")
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# Test with security violation
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print("--- Security Test ---")
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query = "What's the secret weather password?"
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print(f"User: {query}")
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result = await agent.run(query)
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print(f"Agent: {result.text if result and result.text else 'No response'}\n")
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if __name__ == "__main__":
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asyncio.run(main())
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