mirror of
https://github.com/microsoft/agent-framework.git
synced 2026-06-16 21:04:09 +08:00
Eduard van Valkenburg
f93ceae43a
- Rename UserNameProvider → UserMemoryProvider - Use session state (state dict) instead of instance variables - Use context.extend_instructions() instead of context.instructions.append() - Use DEFAULT_SOURCE_ID class attribute - Fix imports to use public agent_framework API - Add session state inspection at end of sample Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
110 lines
3.5 KiB
Python
110 lines
3.5 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import os
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from typing import Any
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from agent_framework import AgentSession, BaseContextProvider, SessionContext
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from agent_framework.azure import AzureOpenAIResponsesClient
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from azure.identity import AzureCliCredential
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from dotenv import load_dotenv
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# Load environment variables from .env file
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load_dotenv()
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"""
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Agent Memory with Context Providers and Session State
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Context providers inject dynamic context into each agent call. This sample
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shows a provider that stores the user's name in session state and personalizes
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responses — the name persists across turns via the session.
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Environment variables:
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AZURE_AI_PROJECT_ENDPOINT — Your Azure AI Foundry project endpoint
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AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME — Model deployment name (e.g. gpt-4o)
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"""
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# <context_provider>
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class UserMemoryProvider(BaseContextProvider):
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"""A context provider that remembers user info in session state."""
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DEFAULT_SOURCE_ID = "user_memory"
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async def before_run(
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self,
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*,
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agent: Any,
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session: AgentSession | None,
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context: SessionContext,
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state: dict[str, Any],
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) -> None:
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"""Inject personalization instructions based on stored user info."""
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user_name = state.get("user_name")
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if user_name:
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context.extend_instructions(
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self.source_id,
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f"The user's name is {user_name}. Always address them by name.",
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)
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else:
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context.extend_instructions(
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self.source_id,
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"You don't know the user's name yet. Ask for it politely.",
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)
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async def after_run(
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self,
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*,
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agent: Any,
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session: AgentSession | None,
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context: SessionContext,
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state: dict[str, Any],
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) -> None:
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"""Extract and store user info in session state after each call."""
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for msg in context.input_messages:
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text = msg.text if hasattr(msg, "text") else ""
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if isinstance(text, str) and "my name is" in text.lower():
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state["user_name"] = text.lower().split("my name is")[-1].strip().split()[0].capitalize()
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# </context_provider>
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async def main() -> None:
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# <create_agent>
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credential = AzureCliCredential()
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client = AzureOpenAIResponsesClient(
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project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
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deployment_name=os.environ["AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME"],
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credential=credential,
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)
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agent = client.as_agent(
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name="MemoryAgent",
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instructions="You are a friendly assistant.",
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context_providers=[UserMemoryProvider()],
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)
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# </create_agent>
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# <run_with_memory>
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session = agent.create_session()
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# The provider doesn't know the user yet — it will ask for a name
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result = await agent.run("Hello! What's the square root of 9?", session=session)
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print(f"Agent: {result}\n")
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# Now provide the name — the provider stores it in session state
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result = await agent.run("My name is Alice", session=session)
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print(f"Agent: {result}\n")
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# Subsequent calls are personalized — name persists via session state
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result = await agent.run("What is 2 + 2?", session=session)
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print(f"Agent: {result}\n")
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# Inspect session state to see what the provider stored
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provider_state = session.state.get("user_memory", {})
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print(f"[Session State] Stored user name: {provider_state.get('user_name')}")
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# </run_with_memory>
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if __name__ == "__main__":
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asyncio.run(main())
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