Python: AzureAI OpenAPI + Memory Search Samples

python/samples/getting_started/agents/azure_ai/README.md

@@ -25,7 +25,9 @@ This folder contains examples demonstrating different ways to create and use age
 | [`azure_ai_with_sharepoint.py`](azure_ai_with_sharepoint.py) | Shows how to use SharePoint grounding with Azure AI agents to search through SharePoint content and answer user questions with proper citations. Requires a SharePoint connection configured in your Azure AI project. |
 | [`azure_ai_with_thread.py`](azure_ai_with_thread.py) | Demonstrates thread management with Azure AI agents, including automatic thread creation for stateless conversations and explicit thread management for maintaining conversation context across multiple interactions. |
 | [`azure_ai_with_image_generation.py`](azure_ai_with_image_generation.py) | Shows how to use the `ImageGenTool` with Azure AI agents to generate images based on text prompts. |
+| [`azure_ai_with_memory_search.py`](azure_ai_with_memory_search.py) | Shows how to use memory search functionality with Azure AI agents for conversation persistence. Demonstrates creating memory stores and enabling agents to search through conversation history. |
 | [`azure_ai_with_microsoft_fabric.py`](azure_ai_with_microsoft_fabric.py) | Shows how to use Microsoft Fabric with Azure AI agents to query Fabric data sources and provide responses based on data analysis. Requires a Microsoft Fabric connection configured in your Azure AI project. |
+| [`azure_ai_with_openapi.py`](azure_ai_with_openapi.py) | Shows how to integrate OpenAPI specifications with Azure AI agents using dictionary-based tool configuration. Demonstrates using external REST APIs for dynamic data lookup. |
 | [`azure_ai_with_web_search.py`](azure_ai_with_web_search.py) | Shows how to use the `HostedWebSearchTool` with Azure AI agents to perform web searches and retrieve up-to-date information from the internet. |
 
 ## Environment Variables

python/samples/getting_started/agents/azure_ai/azure_ai_with_memory_search.py

@@ -0,0 +1,93 @@
+# Copyright (c) Microsoft. All rights reserved.
+import asyncio
+import os
+
+from agent_framework.azure import AzureAIClient
+from azure.ai.projects.aio import AIProjectClient
+from azure.ai.projects.models import MemoryStoreDefaultDefinition, MemoryStoreDefaultOptions
+from azure.core.exceptions import ResourceNotFoundError
+from azure.identity.aio import AzureCliCredential
+
+"""
+Azure AI Agent with Memory Search Example
+
+This sample demonstrates usage of AzureAIClient with memory search capabilities
+to retrieve relevant past user messages and maintain conversation context across sessions.
+It shows explicit memory store creation using Azure AI Projects client and agent creation
+using the Agent Framework.
+
+Prerequisites:
+1. Set AZURE_AI_PROJECT_ENDPOINT and AZURE_AI_MODEL_DEPLOYMENT_NAME environment variables.
+2. Set AZURE_AI_CHAT_MODEL_DEPLOYMENT_NAME for the memory chat model.
+3. Set AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME for the memory embedding model.
+4. Deploy both a chat model (e.g. gpt-4.1) and an embedding model (e.g. text-embedding-3-small).
+"""
+
+
+async def main() -> None:
+    endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
+    memory_store_name = "agent_framework_memory_store"
+
+    async with AzureCliCredential() as credential:
+        # First, create the memory store using Azure AI Projects client
+        async with AIProjectClient(endpoint=endpoint, credential=credential) as project_client:
+            # Delete memory store if it already exists
+            try:
+                await project_client.memory_stores.delete(memory_store_name)
+                print(f"Memory store `{memory_store_name}` deleted")
+            except ResourceNotFoundError:
+                # Memory store does not exist, so nothing to delete. Safe to ignore.
+                pass
+
+            # Create a memory store using proper model classes
+            memory_store_definition = MemoryStoreDefaultDefinition(
+                chat_model=os.environ["AZURE_AI_CHAT_MODEL_DEPLOYMENT_NAME"],
+                embedding_model=os.environ["AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME"],
+                options=MemoryStoreDefaultOptions(user_profile_enabled=True, chat_summary_enabled=True),
+            )
+
+            memory_store = await project_client.memory_stores.create(
+                name=memory_store_name,
+                description="Memory store for Agent Framework conversations",
+                definition=memory_store_definition,
+            )
+            print(f"Created memory store: {memory_store.name} ({memory_store.id}): {memory_store.description}")
+
+        # Then, create the agent using Agent Framework
+        async with AzureAIClient(async_credential=credential).create_agent(
+            name="MyMemoryAgent",
+            instructions="""You are a helpful assistant that remembers past conversations.
+            Use the memory search tool to recall relevant information from previous interactions.""",
+            tools={
+                "type": "memory_search",
+                "memory_store_name": memory_store.name,
+                "scope": "user_123",
+                "update_delay": 1,  # Wait 1 second before updating memories (use higher value in production)
+            },
+        ) as agent:
+            # First interaction - establish some preferences
+            print("=== First conversation ===")
+            query1 = "I prefer dark roast coffee"
+            print(f"User: {query1}")
+            result1 = await agent.run(query1)
+            print(f"Agent: {result1}\n")
+
+            # Wait for memories to be processed
+            print("Waiting for memories to be stored...")
+            await asyncio.sleep(5)  # Reduced wait time for demo purposes
+
+            # Second interaction - test memory recall
+            print("=== Second conversation ===")
+            query2 = "Please order my usual coffee"
+            print(f"User: {query2}")
+            result2 = await agent.run(query2)
+            print(f"Agent: {result2}\n")
+
+        # Clean up - delete the memory store
+        async with AIProjectClient(endpoint=endpoint, credential=credential) as project_client:
+            await project_client.memory_stores.delete(memory_store_name)
+            print("Memory store deleted")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/getting_started/agents/azure_ai/azure_ai_with_openapi.py

@@ -0,0 +1,54 @@
+# Copyright (c) Microsoft. All rights reserved.
+import asyncio
+import json
+from pathlib import Path
+
+import aiofiles
+from agent_framework.azure import AzureAIClient
+from azure.identity.aio import AzureCliCredential
+
+"""
+Azure AI Agent with OpenAPI Tool Example
+
+This sample demonstrates usage of AzureAIClient with OpenAPI tools
+to call external APIs defined by OpenAPI specifications.
+
+Prerequisites:
+1. Set AZURE_AI_PROJECT_ENDPOINT and AZURE_AI_MODEL_DEPLOYMENT_NAME environment variables.
+2. The countries.json OpenAPI specification is included in the resources folder.
+"""
+
+
+async def main() -> None:
+    # Load the OpenAPI specification
+    resources_path = Path(__file__).parent.parent / "resources" / "countries.json"
+
+    async with aiofiles.open(resources_path, "r") as f:
+        content = await f.read()
+        openapi_countries = json.loads(content)
+
+    async with (
+        AzureCliCredential() as credential,
+        AzureAIClient(async_credential=credential).create_agent(
+            name="MyOpenAPIAgent",
+            instructions="""You are a helpful assistant that can use country APIs to provide information.
+            Use the available OpenAPI tools to answer questions about countries, currencies, and demographics.""",
+            tools={
+                "type": "openapi",
+                "openapi": {
+                    "name": "get_countries",
+                    "spec": openapi_countries,
+                    "description": "Retrieve information about countries by currency code",
+                    "auth": {"type": "anonymous"},
+                },
+            },
+        ) as agent,
+    ):
+        query = "What is the name and population of the country that uses currency with abbreviation THB?"
+        print(f"User: {query}")
+        result = await agent.run(query)
+        print(f"Agent: {result}\n")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())