> ## Documentation Index
> Fetch the complete documentation index at: https://arize-ax.mintlify.site/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Microsoft Agent Framework

> Trace Microsoft Agent Framework agents with OpenInference and send spans to Arize AX for LLM observability.

[Microsoft Agent Framework](https://learn.microsoft.com/en-us/agent-framework/overview/?pivots=programming-language-python) is Microsoft's open-source SDK for building production AI agents — chat, tools, multi-agent orchestration. The framework emits raw OpenTelemetry spans using GenAI semantic conventions; the [`openinference-instrumentation-agent-framework`](https://github.com/Arize-ai/openinference/tree/main/python/instrumentation/openinference-instrumentation-agent-framework) span processor reshapes them into the OpenInference format Arize AX expects.

<Note>
  This example is pinned to `agent-framework==1.12.1`. Microsoft Agent Framework evolves quickly; if a newer release changes the API, fix the version with `pip install agent-framework==1.12.1`.
</Note>

## Prerequisites

* Python 3.10+
* An Arize AX account ([sign up](https://arize.com/sign-up/))
* An `OPENAI_API_KEY` from the [OpenAI Platform](https://platform.openai.com/api-keys)

## Launch Arize AX

1. Sign in to your [Arize AX account](https://app.arize.com/).
2. From **Space Settings**, copy your **Space ID** and **API Key**. You will set them as `ARIZE_SPACE_ID` and `ARIZE_API_KEY` below.

## Install

```bash theme={null}
pip install arize-otel \
  openinference-instrumentation-agent-framework \
  "agent-framework==1.12.1" openai
```

## Configure credentials

```bash theme={null}
export ARIZE_SPACE_ID="<your-space-id>"
export ARIZE_API_KEY="<your-api-key>"
export ARIZE_PROJECT_NAME="microsoft-agent-framework-tracing-example"
export OPENAI_API_KEY="<your-openai-api-key>"
```

## Setup tracing

<Note>
  Instrument Agent Framework with the `openinference-instrumentation-agent-framework` processor plus `enable_instrumentation()`, regardless of model provider (OpenAI, Anthropic, Azure). Do not use a provider instrumentor such as `AnthropicInstrumentor` or `OpenAIInstrumentor` — Agent Framework drives the model through its own client layer and emits its own OpenTelemetry spans, so a provider instrumentor captures no traces. The example below uses the OpenAI client, but the same setup applies to `agent-framework-anthropic` and every other connector.
</Note>

```python theme={null}
# instrumentation.py
import os

from agent_framework.observability import enable_instrumentation
from arize.otel import (
    BatchSpanProcessor,
    PROJECT_NAME,
    Resource,
)
from openinference.instrumentation.agent_framework import (
    AgentFrameworkToOpenInferenceProcessor,
)
from opentelemetry import trace as otel_trace
from opentelemetry.sdk.trace import TracerProvider

# Microsoft Agent Framework emits raw OpenTelemetry spans (GenAI semantic
# conventions). The reshape processor must run before the OTLP exporter,
# so build the TracerProvider manually rather than using arize.otel.register().
resource = Resource.create({
    PROJECT_NAME: os.environ["ARIZE_PROJECT_NAME"],
})
tracer_provider = TracerProvider(resource=resource)

# Reshape raw Agent Framework spans into the OpenInference format.
tracer_provider.add_span_processor(
    AgentFrameworkToOpenInferenceProcessor()
)
# Then export the reshaped spans to Arize AX.
tracer_provider.add_span_processor(
    BatchSpanProcessor(
        space_id=os.environ["ARIZE_SPACE_ID"],
        api_key=os.environ["ARIZE_API_KEY"],
    )
)

otel_trace.set_tracer_provider(tracer_provider)

# enable_sensitive_data=True captures message content in spans. Required for
# full observability; only enable when you trust the trace destination.
enable_instrumentation(enable_sensitive_data=True)
print("Arize AX tracing initialized for Microsoft Agent Framework.")
```

## Run Microsoft Agent Framework

```python theme={null}
# example.py

# Importing instrumentation first ensures tracing is set up
# before `agent_framework` is imported.
from instrumentation import tracer_provider

import asyncio

from agent_framework.openai import OpenAIChatClient


async def main() -> None:
    # OpenAIChatClient reads OPENAI_API_KEY from the environment.
    agent = OpenAIChatClient(model="gpt-5.5").as_agent(
        instructions="You are a concise factual assistant.",
    )
    response = await agent.run(
        "Why is the ocean salty? Answer in two sentences."
    )
    print(response.text)


asyncio.run(main())
```

### Expected output

```text wrap theme={null}
Arize AX tracing initialized for Microsoft Agent Framework.
The ocean is salty because rivers continuously dissolve mineral salts from rocks and soil and carry them to the sea, where they accumulate over millions of years. Water leaves the ocean through evaporation but the salts remain, steadily concentrating until reaching today's roughly 3.5% salinity.
```

## Verify in Arize AX

1. Open your Arize AX space and select project **`microsoft-agent-framework-tracing-example`**.
2. You should see a new trace within \~30 seconds containing an `invoke_agent` parent span (emitted by Agent Framework, reshaped by the OpenInference processor) wrapping a chat-completion LLM child span with the prompt, response, and token usage attached.
3. If no traces appear, see [Troubleshooting](#troubleshooting).

### Check from the skill, CLI, or SDK

Confirm spans are actually reaching your Arize AX project. Use whichever fits your workflow — the skill and CLI work for any framework; the SDK check is shown for each language.

<Tabs>
  <Tab title="Arize skill (agent)">
    Install the [Arize Skills](https://github.com/Arize-ai/arize-skills) plugin and let your coding agent check for you:

    ```bash theme={null}
    npx skills add Arize-ai/arize-skills
    ```

    Then prompt your agent:

    > Use the `arize-trace` skill to export and analyze recent traces from my project. Confirm spans are arriving, and summarize any errors or latency issues.
  </Tab>

  <Tab title="AX CLI">
    Export recent spans for your project — any rows mean traces are landing:

    ```bash theme={null}
    ax spans export "$ARIZE_PROJECT_NAME" --space "$ARIZE_SPACE_ID" \
      --limit 5 --stdout | jq 'length'
    ```

    A non-zero count confirms spans reached Arize AX. Run `ax auth login` first if you have not authenticated. See the [`ax spans` reference](/docs/api-clients/cli/spans).
  </Tab>

  <Tab title="SDK">
    Query the project's spans and check that at least one came back.

    <CodeGroup>
      ```python Python theme={null}
      import os
      from arize import ArizeClient

      client = ArizeClient(api_key=os.environ["ARIZE_API_KEY"])
      resp = client.spans.list(
          project=os.environ["ARIZE_PROJECT_NAME"],
          space=os.environ["ARIZE_SPACE_ID"],
          limit=5,
      )
      count = len(resp.spans)
      print(
          f"{count} span(s) found" if count else "No spans yet — recheck setup"
      )
      ```

      ```typescript TypeScript theme={null}
      // Reads ARIZE_API_KEY from the environment.
      import { listSpans } from "@arizeai/ax-client";

      const { data: spans } = await listSpans({
        project: process.env.ARIZE_PROJECT_NAME!,
        space: process.env.ARIZE_SPACE_ID!,
        limit: 5,
      });
      const count = spans.length;
      console.log(
        count ? `${count} span(s) found` : "No spans yet — recheck setup",
      );
      ```

      ```go Go theme={null}
      client, err := arize.NewClient(
          arize.Config{APIKey: os.Getenv("ARIZE_API_KEY")},
      )
      if err != nil {
          log.Fatal(err)
      }
      resp, err := client.Spans.List(ctx, spans.ListRequest{
          Project: os.Getenv("ARIZE_PROJECT_NAME"),
          Space:   os.Getenv("ARIZE_SPACE_ID"),
          Limit:   5,
      })
      if err != nil {
          log.Fatal(err)
      }
      fmt.Printf("%d span(s) found\n", len(resp.Spans))
      ```
    </CodeGroup>

    SDK span references: [Python](/docs/api-clients/python/version-8/client-resources/spans) · [TypeScript](/docs/api-clients/typescript/version-1/client-resources/spans) · [Go](/docs/api-clients/go/version-2/client-resources/spans).
  </Tab>
</Tabs>

## Troubleshooting

* **No traces in Arize AX.** Confirm `ARIZE_SPACE_ID` and `ARIZE_API_KEY` are set in the same shell that runs `example.py`. Enable OpenTelemetry debug logs with `export OTEL_LOG_LEVEL=debug` and re-run.
* **Code ran but no spans appear.** `enable_instrumentation()` from `agent_framework.observability` must run after the global tracer provider is set. Confirm `otel_trace.set_tracer_provider(tracer_provider)` runs first, then `enable_instrumentation(enable_sensitive_data=True)`.
* **Spans missing message content.** Pass `enable_sensitive_data=True` to `enable_instrumentation()`. Without it, prompts and responses are stripped from the spans.
* **`401` from OpenAI.** Verify `OPENAI_API_KEY` is set and has access to `gpt-5.5`. Swap for a model your key can call.
* **API breaking changes.** Microsoft Agent Framework evolves quickly; pin `agent-framework==1.12.1` if a newer release breaks the example.
* **Other LLM providers.** Use `agent_framework.azure.AzureOpenAIChatClient` for Azure OpenAI (set `AZURE_OPENAI_API_KEY` + `AZURE_OPENAI_ENDPOINT` + `AZURE_OPENAI_API_VERSION`) or other connectors per the [framework docs](https://learn.microsoft.com/en-us/agent-framework/overview/?pivots=programming-language-python).

## Resources

<CardGroup>
  <Card icon="book-open" href="https://learn.microsoft.com/en-us/agent-framework/overview/?pivots=programming-language-python" title="Microsoft Agent Framework Documentation" horizontal />

  <Card icon="terminal" href="https://github.com/Arize-ai/openinference/tree/main/python/instrumentation/openinference-instrumentation-agent-framework" title="OpenInference Agent Framework Span Processor" horizontal />

  <Card icon="github" href="https://github.com/microsoft/agent-framework" title="Microsoft Agent Framework GitHub" horizontal />
</CardGroup>