mirror of
https://github.com/microsoft/agent-framework.git
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Eduard van Valkenburg
102 lines
3.3 KiB
Python
102 lines
3.3 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._sessions 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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"""
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Agent Memory with Context Providers
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Context providers let you inject dynamic instructions and context into each
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agent invocation. This sample defines a simple provider that tracks the user's
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name and enriches every request with personalization instructions.
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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 UserNameProvider(BaseContextProvider):
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"""A simple context provider that remembers the user's name."""
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def __init__(self) -> None:
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super().__init__(source_id="user-name-provider")
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self.user_name: str | None = None
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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,
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context: SessionContext,
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state: dict[str, Any],
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) -> None:
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"""Called before each agent invocation — add extra instructions."""
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if self.user_name:
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context.instructions.append(f"The user's name is {self.user_name}. Always address them by name.")
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else:
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context.instructions.append("You don't know the user's name yet. Ask for it politely.")
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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,
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context: SessionContext,
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state: dict[str, Any],
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) -> None:
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"""Called after each agent invocation — extract information."""
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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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# Simple extraction — production code should use structured extraction
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self.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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memory = UserNameProvider()
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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=[memory],
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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 extracts and stores it
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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
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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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print(f"[Memory] Stored user name: {memory.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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