Evan Mattson
c35a63ed8d
* Python: feat: cross-session origin attribution on context messages Add an optional origin_session_id parameter to SessionContext.extend_messages that propagates into the existing _attribution payload on Message.additional_properties. Downstream context observers can use it to detect when a provider injects content stored under a different session than the requesting one. Populate the field from the harness memory consolidation pipeline (_harness/_memory.py) when injected topics include contributions from sessions other than the current one. Add a self-contained sample observer under samples/02-agents/context_providers/cross_session_observer.py demonstrating how to subscribe to the signal. Backward-compatible: omitting the parameter preserves the existing attribution shape exactly. Tests added in test_sessions.py and test_harness_memory.py cover the new parameter, the harness cross-session case, and the same-session case. Motivated by Dai et al., Stateful Agent Backdoor (arXiv:2605.06158, May 2026), which specifically surveys MAF in section 6.1 / Table 10. See #5914 for design discussion. Surfaced during independent audit conducted by @finnoybu (Ken Tannenbaum, AEGIS Initiative); [MEDIUM, python/packages/core]. * Address cross-session attribution review feedback * Address follow-up review feedback * Address follow-up review comments * Address origin attribution review feedback --------- Co-authored-by: finnoybu <21694570+finnoybu@users.noreply.github.com>
78 lines
2.7 KiB
Python
78 lines
2.7 KiB
Python
# /// script
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# requires-python = ">=3.10"
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# dependencies = [
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# "agent-framework-foundry",
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# "microsoft-opentelemetry",
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# ]
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# ///
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# Run with any PEP 723 compatible runner, e.g.:
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# uv run python/samples/02-agents/observability/microsoft_opentelemetry_distro.py
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from random import randint
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from typing import Annotated
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from agent_framework import Agent, tool
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from agent_framework.foundry import FoundryChatClient
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from agent_framework.observability import get_tracer
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from azure.identity import AzureCliCredential
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from dotenv import load_dotenv
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from microsoft.opentelemetry import use_microsoft_opentelemetry
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from opentelemetry.trace import SpanKind
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from opentelemetry.trace.span import format_trace_id
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from pydantic import Field
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# Load environment variables from .env file
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load_dotenv()
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@tool(approval_mode="never_require")
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async def get_weather(
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location: Annotated[str, Field(description="The location to get the weather for.")],
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) -> str:
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"""Get the weather for a given location."""
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await asyncio.sleep(randint(0, 10) / 10.0) # Simulate a network call
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conditions = ["sunny", "cloudy", "rainy", "stormy"]
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return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
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async def main():
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# Set up Azure monitor exporters for telemetry
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# This will automatically enable instrumentation for Agent Framework
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# Install the Microsoft OpenTelemetry Distro package to enable this functionality:
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# pip install microsoft-opentelemetry
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# Requires the following environment variables to be set:
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# OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4318
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# APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey...
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use_microsoft_opentelemetry(enable_azure_monitor=True)
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questions = [
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"What's the weather in Amsterdam?",
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"and in Paris, and which is better?",
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"Why is the sky blue?",
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]
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with get_tracer().start_as_current_span("Scenario: Agent Chat", kind=SpanKind.CLIENT) as current_span:
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print(f"Trace ID: {format_trace_id(current_span.get_span_context().trace_id)}")
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agent = Agent(
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client=FoundryChatClient(credential=AzureCliCredential()),
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tools=get_weather,
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name="WeatherAgent",
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instructions="You are a weather assistant.",
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id="weather-agent",
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)
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session = agent.create_session()
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for question in questions:
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print(f"\nUser: {question}")
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print(f"{agent.name}: ", end="")
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async for update in agent.run(question, session=session, stream=True):
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if update.text:
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print(update.text, end="")
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if __name__ == "__main__":
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asyncio.run(main())
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