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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
217 lines
8.3 KiB
Python
217 lines
8.3 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import re
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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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AgentResponse,
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AgentResponseUpdate,
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ChatContext,
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ChatResponse,
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ChatResponseUpdate,
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Message,
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ResponseStream,
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Role,
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tool,
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)
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from agent_framework.openai import OpenAIResponsesClient
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from pydantic import Field
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"""
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Result Override with MiddlewareTypes (Regular and Streaming)
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This sample demonstrates how to use middleware to intercept and modify function results
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after execution, supporting both regular and streaming agent responses. The example shows:
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- How to execute the original function first and then modify its result
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- Replacing function outputs with custom messages or transformed data
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- Using middleware for result filtering, formatting, or enhancement
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- Detecting streaming vs non-streaming execution using context.stream
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- Overriding streaming results with custom async generators
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The weather override middleware lets the original weather function execute normally,
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then replaces its result with a custom "perfect weather" message. For streaming responses,
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it creates a custom async generator that yields the override message in chunks.
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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 weather_override_middleware(context: ChatContext, call_next: Callable[[], Awaitable[None]]) -> None:
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"""Chat middleware that overrides weather results for both streaming and non-streaming cases."""
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# Let the original agent execution complete first
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await call_next()
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# Check if there's a result to override (agent called weather function)
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if context.result is not None:
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# Create custom weather message
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chunks = [
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"due to special atmospheric conditions, ",
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"all locations are experiencing perfect weather today! ",
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"Temperature is a comfortable 22°C with gentle breezes. ",
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"Perfect day for outdoor activities!",
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]
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if context.stream and isinstance(context.result, ResponseStream):
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index = {"value": 0}
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def _update_hook(update: ChatResponseUpdate) -> ChatResponseUpdate:
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for content in update.contents or []:
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if not content.text:
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continue
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content.text = f"Weather Advisory: [{index['value']}] {content.text}"
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index["value"] += 1
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return update
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context.result.with_transform_hook(_update_hook)
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else:
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# For non-streaming: just replace with a new message
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current_text = context.result.text if isinstance(context.result, ChatResponse) else ""
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custom_message = f"Weather Advisory: [0] {''.join(chunks)} Original message was: {current_text}"
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context.result = ChatResponse(messages=[Message(role=Role.ASSISTANT, text=custom_message)])
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async def validate_weather_middleware(context: ChatContext, call_next: Callable[[], Awaitable[None]]) -> None:
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"""Chat middleware that simulates result validation for both streaming and non-streaming cases."""
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await call_next()
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validation_note = "Validation: weather data verified."
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if context.result is None:
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return
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if context.stream and isinstance(context.result, ResponseStream):
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def _append_validation_note(response: ChatResponse) -> ChatResponse:
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response.messages.append(Message(role=Role.ASSISTANT, text=validation_note))
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return response
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context.result.with_finalizer(_append_validation_note)
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elif isinstance(context.result, ChatResponse):
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context.result.messages.append(Message(role=Role.ASSISTANT, text=validation_note))
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async def agent_cleanup_middleware(context: AgentContext, call_next: Callable[[], Awaitable[None]]) -> None:
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"""Agent middleware that validates chat middleware effects and cleans the result."""
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await call_next()
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if context.result is None:
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return
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validation_note = "Validation: weather data verified."
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state = {"found_prefix": False}
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def _sanitize(response: AgentResponse) -> AgentResponse:
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found_prefix = state["found_prefix"]
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found_validation = False
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cleaned_messages: list[Message] = []
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for message in response.messages:
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text = message.text
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if text is None:
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cleaned_messages.append(message)
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continue
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if validation_note in text:
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found_validation = True
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text = text.replace(validation_note, "").strip()
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if not text:
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continue
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if "Weather Advisory:" in text:
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found_prefix = True
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text = text.replace("Weather Advisory:", "")
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text = re.sub(r"\[\d+\]\s*", "", text)
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cleaned_messages.append(
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Message(
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role=message.role,
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text=text.strip(),
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author_name=message.author_name,
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message_id=message.message_id,
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additional_properties=message.additional_properties,
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raw_representation=message.raw_representation,
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)
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)
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if not found_prefix:
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raise RuntimeError("Expected chat middleware prefix not found in agent response.")
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if not found_validation:
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raise RuntimeError("Expected validation note not found in agent response.")
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cleaned_messages.append(Message(role=Role.ASSISTANT, text=" Agent: OK"))
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response.messages = cleaned_messages
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return response
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if context.stream and isinstance(context.result, ResponseStream):
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def _clean_update(update: AgentResponseUpdate) -> AgentResponseUpdate:
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for content in update.contents or []:
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if not content.text:
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continue
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text = content.text
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if "Weather Advisory:" in text:
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state["found_prefix"] = True
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text = text.replace("Weather Advisory:", "")
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text = re.sub(r"\[\d+\]\s*", "", text)
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content.text = text
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return update
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context.result.with_transform_hook(_clean_update)
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context.result.with_finalizer(_sanitize)
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elif isinstance(context.result, AgentResponse):
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context.result = _sanitize(context.result)
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async def main() -> None:
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"""Example demonstrating result override with middleware for both streaming and non-streaming."""
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print("=== Result Override 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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agent = OpenAIResponsesClient(
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middleware=[validate_weather_middleware, weather_override_middleware],
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).as_agent(
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name="WeatherAgent",
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instructions="You are a helpful weather assistant. Use the weather tool to get current conditions.",
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tools=get_weather,
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middleware=[agent_cleanup_middleware],
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)
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# Non-streaming example
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print("\n--- Non-streaming Example ---")
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query = "What's the weather like in Seattle?"
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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}")
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# Streaming example
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print("\n--- Streaming Example ---")
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query = "What's the weather like in Portland?"
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print(f"User: {query}")
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print("Agent: ", end="", flush=True)
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response = agent.run(query, stream=True)
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async for chunk in response:
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if chunk.text:
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print(chunk.text, end="", flush=True)
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print("\n")
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print(f"Final Result: {(await response.get_final_response()).text}")
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if __name__ == "__main__":
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asyncio.run(main())
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