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Python: Add Foundry Memory Context Provider (#3943)
* Initial plan * Add FoundryMemoryProvider and tests Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Add sample and documentation for FoundryMemoryProvider Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Address code review feedback for FoundryMemoryProvider Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Address PR review comments: Add DEFAULT_SOURCE_ID, use logging.getLogger, move state to session.state Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Fix Foundry memory ItemParam usage and exports Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Refactor provider hook state and standardize source IDs Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Support endpoint-based Foundry memory init Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * updated implementation and sample * updated code and samples * Fix foundry memory provider tests: mock structure and field names - Use Mock objects with memory_item.content for memory mocks - Assert 'content' instead of 'text' on SDK message items - Update exception types from ServiceInitializationError to ValueError - Remove unused ServiceInitializationError import Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix mypy errors in foundry memory provider Add type: ignore[arg-type] for scope (str | None vs str) and items (list variance) passed to Azure SDK methods. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix import --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> Co-authored-by: eavanvalkenburg <github@vanvalkenburg.eu> Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import os
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from datetime import datetime, timezone
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from agent_framework import Agent, InMemoryHistoryProvider
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from agent_framework.azure import AzureOpenAIResponsesClient, FoundryMemoryProvider
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from azure.ai.projects.aio import AIProjectClient
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from azure.ai.projects.models import (
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MemoryStoreDefaultDefinition,
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MemoryStoreDefaultOptions,
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)
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from azure.identity.aio import AzureCliCredential
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from dotenv import load_dotenv
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"""
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Azure AI Agent with Foundry Memory Context Provider Example
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This sample demonstrates using the FoundryMemoryProvider as a context provider
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to add semantic memory capabilities to your agents. The provider automatically:
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1. Retrieves static (user profile) memories on first run
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2. Searches for contextual memories based on conversation
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3. Updates the memory store with new conversation messages
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The sample creates a temporary memory store with user profile enabled (and chat summary
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disabled), scopes memories to a specific user ID ("user_123"), and sets update_delay=0
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so memories are stored immediately (in production, use a delay to batch updates and
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reduce costs). Conversation history is intentionally not stored (neither service-side
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via ``store=False`` nor client-side via ``load_messages=False`` on the history provider),
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so that follow-up responses demonstrate the agent relying solely on Foundry Memory
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rather than chat history. The memory store is deleted at the end of the run.
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Prerequisites:
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1. Set AZURE_AI_PROJECT_ENDPOINT environment variable
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2. Set AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME for the chat/responses model
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3. Set AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME for the embedding model
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4. Deploy both a chat model (e.g. gpt-4) and an embedding model (e.g. text-embedding-3-small)
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"""
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load_dotenv()
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async def main() -> None:
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endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
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async with (
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AzureCliCredential() as credential,
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AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
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):
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# Generate a unique memory store name to avoid conflicts
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memory_store_name = f"agent_framework_memory_{datetime.now(timezone.utc).strftime('%Y%m%d')}"
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# Specify memory store options
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options = MemoryStoreDefaultOptions(
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chat_summary_enabled=False,
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user_profile_enabled=True,
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user_profile_details="Avoid irrelevant or sensitive data, such as age, financials, precise location, and credentials",
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)
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memory_store_definition = MemoryStoreDefaultDefinition(
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chat_model=os.environ["AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME"],
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embedding_model=os.environ["AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME"],
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options=options,
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)
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print(f"Creating memory store '{memory_store_name}'...")
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try:
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# Create a memory store
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memory_store = await project_client.memory_stores.create(
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name=memory_store_name,
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description="Memory store for Agent Framework with FoundryMemoryProvider",
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definition=memory_store_definition,
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)
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except Exception as e:
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print(f"Failed to create memory store: {e}")
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return
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print(f"Created memory store: {memory_store.name} ({memory_store.id})")
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print(f"Description: {memory_store.description}\n")
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print("==========================================")
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# Create the chat client
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client = AzureOpenAIResponsesClient(project_client=project_client)
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# Create the Foundry Memory context provider
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memory_provider = FoundryMemoryProvider(
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project_client=project_client,
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memory_store_name=memory_store.name,
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scope="user_123", # Scope memories to a specific user, if not set, the session_id
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# will be used as scope, which means memories are only shared within the same session
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update_delay=0, # Do not wait to update memories after each interaction (for demo purposes)
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# In production, consider setting a delay to batch updates and reduce costs
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)
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# Create an agent with the memory context provider
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async with Agent(
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name="MemoryAgent",
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client=client,
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instructions="""You are a helpful assistant that remembers past conversations.
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The memories from previous interactions are automatically provided to you.""",
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context_providers=[memory_provider, InMemoryHistoryProvider(load_messages=False)],
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default_options={"store": False},
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) as agent:
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try:
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# note that we will use the service side storage, nor load messsages from the history provider,
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# but we include it to demonstrate that it can be used alongside the Foundry provider for other use cases.
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session = agent.create_session()
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# First interaction - establish some preferences
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print("=== First conversation ===")
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query1 = "I prefer dark roast coffee and I'm allergic to nuts"
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print(f"User: {query1}")
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result1 = await agent.run(query1, session=session)
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print(f"Agent: {result1}\n")
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# Wait for memories to be processed
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print("Waiting for memories to be stored...")
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await asyncio.sleep(8)
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# Second interaction - test memory recall
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print("=== Second conversation ===")
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query2 = "Can you recommend a coffee and snack for me?"
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print(f"User: {query2}")
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result2 = await agent.run(query2, session=session)
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print(f"Agent: {result2}\n")
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# Third interaction - continue the conversation
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print("=== Third conversation ===")
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query3 = "What do you remember about my preferences?"
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print(f"User: {query3}")
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result3 = await agent.run(query3, session=session)
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print(f"Agent: {result3}\n")
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print(f"Stored memories from: {memory_store.name} ({memory_store.id})")
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res = await project_client.memory_stores.search_memories(name=memory_store.name, scope="user_123")
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for memory in res.memories:
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print(f"Memory: {memory.memory_item.content}")
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except Exception as e:
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print(f"An error occurred: {e}")
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finally:
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await project_client.memory_stores.delete(memory_store_name)
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print("==========================================")
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print("Memory store deleted")
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if __name__ == "__main__":
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asyncio.run(main())
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"""
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Example output:
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Creating memory store 'agent_framework_memory_20260223'...
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Created memory store: agent_framework_memory_20260223 (memstore_57c1f95bb4040c6d00RVOP71Q8tS23opIc4G4ZE8DuALiBFx44)
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Description: Memory store for Agent Framework with FoundryMemoryProvider
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==========================================
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=== First conversation ===
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User: I prefer dark roast coffee and I'm allergic to nuts
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Agent: Got it—I’ll remember: you prefer dark roast coffee, and you’re allergic to nuts.
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Waiting for memories to be stored...
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=== Second conversation ===
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User: Can you recommend a coffee and snack for me?
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Agent: For coffee: **dark roast drip or Americano** (choose a **dark roast** like French/Italian roast). If you like it smoother, try a **dark-roast cold brew**.
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For a snack (nut-free): **Greek yogurt with berries**, or a **cheese stick + whole-grain crackers**. If you want something sweet: **dark chocolate (check “may contain nuts” warnings)**.
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=== Third conversation ===
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User: What do you remember about my preferences?
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Agent: - You’re allergic to nuts.
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- You prefer dark roast coffee.
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Stored memories from: agent_framework_memory_20260223 (memstore_57c1f95bb4040c6d00RVOP71Q8tS23opIc4G4ZE8DuALiBFx44)
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Memory: The user is allergic to nuts.
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Memory: The user prefers dark roast coffee.
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==========================================
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Memory store deleted
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"""
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