agentic-layer-sdk-msaf 0.13.5

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agentic-layer-sdk-msaf

Microsoft Agent Framework adapter for the Agentic Layer SDK.

This package provides utilities to convert a Microsoft Agent Framework agent into an instrumented A2A Starlette web application.

Usage

from agent_framework import Agent
from agenticlayer.msaf import create_metrics_middleware, create_openai_client
from agenticlayer.msaf.agent_to_a2a import to_a2a

agent = Agent(
    client=create_openai_client(),
    name="MyAgent",
    instructions="You are a helpful assistant.",
    middleware=create_metrics_middleware(),
)
app = to_a2a(agent, name="MyAgent", rpc_url="http://localhost:8000/")
# Then run with: uvicorn module:app

Configuration

OpenAI-compatible gateway (LiteLLM proxy)

Set the following environment variables to point the agent at an OpenAI-compatible gateway such as LiteLLM proxy:

Variable	Description
LITELLM_PROXY_API_BASE	Base URL of the gateway, e.g. http://litellm-proxy:4000
LITELLM_PROXY_API_KEY	API key for the gateway
OPENAI_CHAT_MODEL_ID	Model name to use, e.g. gpt-4o

create_openai_client() reads these variables automatically and passes them to OpenAIChatClient as base_url and api_key.

Observability

OpenTelemetry setup

Call setup_otel() before creating agents to configure OTLP exporters and enable instrumentation:

from agenticlayer.msaf.otel import setup_otel

setup_otel()

This reads standard OTEL_EXPORTER_OTLP_ENDPOINT / OTEL_EXPORTER_OTLP_PROTOCOL environment variables, sets up trace/log/metric providers, and enables the built-in Agent Framework telemetry layers.

Metrics

The SDK emits the following OpenTelemetry metrics:

Built-in (provided by Agent Framework telemetry layers, enabled by setup_otel()):

Metric	Type	Description
gen_ai.client.token.usage	Histogram	Input and output token counts per LLM call
gen_ai.client.operation.duration	Histogram	Duration of LLM / agent operations

Custom (provided by create_metrics_middleware(), must be added to the agent):

Metric	Type	Description
agent.invocations	Counter	Number of agent invocations
agent.llm.calls	Counter	Number of LLM calls
agent.tool.calls	Counter	Number of tool calls
agent.errors	Counter	Number of errors (with error_source attribute)

Add the metrics middleware to your agent:

from agent_framework import Agent
from agenticlayer.msaf import create_metrics_middleware, create_openai_client

agent = Agent(
    client=create_openai_client(),
    instructions="You are a helpful assistant.",
    middleware=create_metrics_middleware(),
)

If you already have other middleware, combine them:

agent = Agent(
    client=create_openai_client(),
    instructions="You are a helpful assistant.",
    middleware=[MyCustomMiddleware(), *create_metrics_middleware()],
)