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Python: Fix broken samples and add missing READMEs (#5038)
* Python: Fix broken samples and add missing READMEs - simple_context_provider: move instructions kwarg into options dict - suspend_resume_session: use OpenAIChatCompletionClient for in-memory demo - foundry_chat_client_with_hosted_mcp: move store kwarg into options dict - Add README.md for context_providers and conversations sample folders Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Python: Fix additional sample issues in context_providers - mem0_basic: send preferences query before sleep so Mem0 can learn them, print result from new session recall - mem0_sessions: add session for multi-turn conversation in agent-scoped example, remove user_id from agent-scoped provider (Mem0 API stores memories without user_id when agent_id is provided), use single message for storing preferences - redis_basics: print retrieved context messages instead of raw object - redis_sessions: add missing load_dotenv() call - redis_basics/redis_sessions: fix docstrings referencing wrong client type - azure_redis_conversation: replace duplicate copyright with load_dotenv() Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Python: Fix broken link in declarative README openai_responses_agent.py was renamed to openai_agent.py Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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‎python/samples/02-agents/context_providers/README.md‎

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# Context Provider Samples
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These samples demonstrate how to use context providers to enrich agent conversations with external knowledge — from custom logic to Azure AI Search (RAG) and memory services.
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## Samples
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| File / Folder | Description |
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|---------------|-------------|
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| [`simple_context_provider.py`](simple_context_provider.py) | Implement a custom context provider by extending `BaseContextProvider` to extract and inject structured user information across turns. |
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| [`azure_ai_foundry_memory.py`](azure_ai_foundry_memory.py) | Use `FoundryMemoryProvider` to add semantic memory — automatically retrieves, searches, and stores memories via Azure AI Foundry. |
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| [`azure_ai_search/`](azure_ai_search/) | Retrieval Augmented Generation (RAG) with Azure AI Search in semantic and agentic modes. See its own [README](azure_ai_search/README.md). |
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| [`mem0/`](mem0/) | Memory-powered context using the Mem0 integration (open-source and managed). See its own [README](mem0/README.md). |
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| [`redis/`](redis/) | Redis-backed context providers for conversation memory and sessions. See its own [README](redis/README.md). |
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## Prerequisites
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**For `simple_context_provider.py`:**
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- `FOUNDRY_PROJECT_ENDPOINT`: Your Azure AI Foundry project endpoint
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- `FOUNDRY_MODEL`: Model deployment name
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- Azure CLI authentication (`az login`)
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**For `azure_ai_foundry_memory.py`:**
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- `FOUNDRY_PROJECT_ENDPOINT`: Your Azure AI Foundry project endpoint
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- `FOUNDRY_MODEL`: Chat/responses model deployment name
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- `AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME`: Embedding model deployment name (e.g., `text-embedding-ada-002`)
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- Azure CLI authentication (`az login`)
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See each subfolder's README for provider-specific prerequisites.

‎python/samples/02-agents/context_providers/mem0/mem0_basic.py‎

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# Now tell the agent the company code and the report format that you want to use
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# and it should be able to invoke the tool and return the report.
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query = "I always work with CNTS and I always want a detailed report format. Please remember and retrieve it."
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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}\n")
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# Mem0 processes and indexes memories asynchronously.
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# Wait for memories to be indexed before querying in a new thread.
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# In production, consider implementing retry logic or using Mem0's
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# eventual consistency handling instead of a fixed delay.
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print("Waiting for memories to be processed...")
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await asyncio.sleep(12) # Empirically determined delay for Mem0 indexing
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await asyncio.sleep(15) # Empirically determined delay for Mem0 indexing
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print("\nRequest within a new session:")
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# Create a new session for the agent.
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# The new session has no context of the previous conversation.
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session = agent.create_session()
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# Since we have the mem0 component in the session, the agent should be able to
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# retrieve the company report without asking for clarification, as it will
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# be able to remember the user preferences from Mem0 component.
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query = "Please retrieve my company report"
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print(f"User: {query}")
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result = await agent.run(query, session=session)
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print(f"Agent: {result}")
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if __name__ == "__main__":

‎python/samples/02-agents/context_providers/mem0/mem0_sessions.py‎

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print("2. Agent-Scoped Memory Example:")
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print("-" * 40)
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user_id = "user123"
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async with (
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AzureCliCredential() as credential,
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Agent(
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context_providers=[
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Mem0ContextProvider(
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source_id="mem0",
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user_id=user_id,
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agent_id="scoped_assistant",
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)
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],
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) as scoped_agent,
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):
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# Store some information
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query = "Remember that for this conversation, I'm working on a Python project about data analysis."
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print(f"User: {query}")
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result = await scoped_agent.run(query)
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print(f"Agent: {result}\n")
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# Test memory retrieval
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query = "What project am I working on?"
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query = (
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"Remember that I'm working on a Python project about data analysis "
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"and I prefer using pandas and matplotlib."
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)
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print(f"User: {query}")
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result = await scoped_agent.run(query)
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print(f"Agent: {result}\n")
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# Store more information
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query = "Also remember that I prefer using pandas and matplotlib for this project."
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print(f"User: {query}")
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result = await scoped_agent.run(query)
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print(f"Agent: {result}\n")
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# Test comprehensive memory retrieval
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new_session = scoped_agent.create_session()
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query = "What do you know about my current project and preferences?"
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print(f"User: {query}")
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result = await scoped_agent.run(query)
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print(f"User (new session): {query}")
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result = await scoped_agent.run(query, session=new_session)
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print(f"Agent: {result}\n")
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‎python/samples/02-agents/context_providers/redis/azure_redis_conversation.py‎

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from agent_framework.redis import RedisHistoryProvider
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from azure.identity import AzureCliCredential
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from azure.identity.aio import AzureCliCredential as AsyncAzureCliCredential
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from dotenv import load_dotenv
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from redis.credentials import CredentialProvider
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# Copyright (c) Microsoft. All rights reserved.
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load_dotenv()
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class AzureCredentialProvider(CredentialProvider):

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

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def create_chat_client() -> FoundryChatClient:
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"""Create an Azure OpenAI Responses client using a Foundry project endpoint."""
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"""Create a FoundryChatClient using a Foundry project endpoint."""
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return FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ["FOUNDRY_MODEL"],

‎python/samples/02-agents/context_providers/redis/redis_sessions.py‎

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from agent_framework.foundry import FoundryChatClient
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from agent_framework.redis import RedisContextProvider
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from azure.identity import AzureCliCredential
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from dotenv import load_dotenv
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from redisvl.extensions.cache.embeddings import EmbeddingsCache
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from redisvl.utils.vectorize import OpenAITextVectorizer
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# Copyright (c) Microsoft. All rights reserved.
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# Load environment variables from .env file
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load_dotenv()
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# Default Redis URL for local Redis Stack.
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# Please set OPENAI_API_KEY to use the OpenAI vectorizer.
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# For chat responses, also set FOUNDRY_PROJECT_ENDPOINT and FOUNDRY_MODEL.
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def create_chat_client() -> FoundryChatClient:
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"""Create an Azure OpenAI Responses client using a Foundry project endpoint."""
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"""Create a FoundryChatClient using a Foundry project endpoint."""
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return FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ["FOUNDRY_MODEL"],

‎python/samples/02-agents/context_providers/simple_context_provider.py‎

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# Use the chat client to extract structured information
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result = await self._chat_client.get_response(
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messages=request_messages, # type: ignore
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instructions="Extract the user's name and age from the message if present. "
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"If not present return nulls.",
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options={"response_format": UserInfo},
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options={
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"instructions": "Extract the user's name and age from the message if present. "
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"If not present return nulls.",
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"response_format": UserInfo,
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},
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)
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# Update user info with extracted data

‎python/samples/02-agents/conversations/README.md‎

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# Conversation & Session Management Samples
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These samples demonstrate different approaches to managing conversation history and session state in Agent Framework.
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## Samples
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| File | Description |
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|------|-------------|
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| [`suspend_resume_session.py`](suspend_resume_session.py) | Suspend and resume conversation sessions, comparing service-managed sessions (Azure AI Foundry) with in-memory sessions (OpenAI). |
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| [`custom_history_provider.py`](custom_history_provider.py) | Implement a custom history provider by extending `BaseHistoryProvider`, enabling conversation persistence in your preferred storage backend. |
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| [`redis_history_provider.py`](redis_history_provider.py) | Use Redis as a history provider for persistent conversation history storage across sessions. |
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## Prerequisites
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**For `suspend_resume_session.py`:**
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- `FOUNDRY_PROJECT_ENDPOINT`: Your Azure AI Foundry project endpoint (service-managed session)
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- `FOUNDRY_MODEL`: The Foundry model deployment name
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- `OPENAI_API_KEY`: Your OpenAI API key (in-memory session)
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- Azure CLI authentication (`az login`)
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**For `custom_history_provider.py`:**
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- `OPENAI_API_KEY`: Your OpenAI API key
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**For `redis_history_provider.py`:**
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- `OPENAI_API_KEY`: Your OpenAI API key
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- A running Redis server — default URL is `redis://localhost:6379`
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- Override via the `REDIS_URL` environment variable for remote or authenticated instances
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- Quickstart with Docker: `docker run -d --name redis-stack -p 6379:6379 redis/redis-stack-server:latest`

‎python/samples/02-agents/conversations/suspend_resume_session.py‎

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from agent_framework import Agent, AgentSession
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from agent_framework.foundry import FoundryChatClient
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from agent_framework.openai import OpenAIChatCompletionClient
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from azure.identity.aio import AzureCliCredential
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from dotenv import load_dotenv
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# OpenAI Chat Client is used as an example here,
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# other chat clients can be used as well.
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agent = Agent(
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client=FoundryChatClient(),
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client=OpenAIChatCompletionClient(),
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name="MemoryBot",
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instructions="You are a helpful assistant that remembers our conversation.",
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)

‎python/samples/02-agents/declarative/README.md‎

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**Key concepts**: Azure OpenAI integration, credential management, structured outputs
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### 5. **OpenAI Responses Agent** ([`openai_responses_agent.py`](./openai_responses_agent.py))
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### 5. **OpenAI Responses Agent** ([`openai_agent.py`](./openai_agent.py))
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Demonstrates the simplest possible agent using OpenAI directly.
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