---
source: ⚠️ Jupyter Notebook
title: Trace the Microsoft Agent Framework with Langfuse
sidebarTitle: Microsoft Agent Framework
logo: /images/integrations/microsoft_icon.svg
description: Learn how to use Langfuse to monitor Microsoft Agent Framework to debug and evaluate your AI agents
category: Integrations
---
# Trace the Microsoft Agent Framework with Langfuse
This notebook demonstrates how to **integrate Langfuse** into your **Microsoft Agent Framework** workflow to monitor, debug and evaluate your AI agents.
> **What is the Microsoft Agent Framework?**: The [Microsoft Agent Framework](https://github.com/microsoft/agent-framework) is an open-source framework for building intelligent agents. It provides a comprehensive set of tools for creating agents that can interact with various services, execute tasks, and handle complex workflows. The framework supports multiple LLM providers including Azure OpenAI and OpenAI, and offers built-in observability through OpenTelemetry.
> **What is Langfuse?**: [Langfuse](https://langfuse.com/) is an open-source observability platform for AI agents. It helps you visualize and monitor LLM calls, tool usage, cost, latency, and more.
## 1. Install Dependencies
Below we install the `agent-framework` library (the Microsoft Agent Framework) and `langfuse` for observability.
```python
%pip install agent-framework langfuse --pre
```
## 2. Configure Environment & Langfuse Credentials
Next, set up your Langfuse API keys. You can get these keys by signing up for a free [Langfuse Cloud](https://langfuse.com/cloud) account or by [self-hosting Langfuse](https://langfuse.com/self-hosting). These environment variables are essential for the Langfuse client to authenticate and send data to your Langfuse project.
```python
import os
# Get keys for your project from the project settings page: https://cloud.langfuse.com
os.environ.setdefault("LANGFUSE_PUBLIC_KEY", "pk-lf-...");
os.environ.setdefault("LANGFUSE_SECRET_KEY", "sk-lf-...");
os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); # 🇪🇺 EU region
# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
# Your Azure OpenAI credentials
os.environ.setdefault("AZURE_OPENAI_API_KEY", "your-azure-openai-key");
os.environ.setdefault("AZURE_OPENAI_ENDPOINT", "https://your-resource.openai.azure.com/");
os.environ.setdefault("AZURE_OPENAI_CHAT_DEPLOYMENT_NAME", "gpt-5-mini");
os.environ.setdefault("OPENAI_CHAT_MODEL_ID", "gpt-5-mini");
```
## 3. Initialize Langfuse Client
Initialize the Langfuse client to verify the connection. `get_client()` initializes the Langfuse client using the credentials provided in the environment variables.
```python
from langfuse import get_client
langfuse = get_client()
# Verify connection
if langfuse.auth_check():
print("Langfuse client is authenticated and ready!")
else:
print("Authentication failed. Please check your credentials and host.")
```
## 4. Enable Observability
The Microsoft Agent Framework includes built-in observability support through OpenTelemetry. Enable it by calling [`configure_otel_providers()`](https://learn.microsoft.com/en-us/agent-framework/user-guide/observability?pivots=programming-language-python#1-standard-opentelemetry-environment-variables-recommended) which automatically exports traces to Langfuse.
**Note:** Set `enable_sensitive_data=True` to capture full request/response data including function arguments and results.
```python
from agent_framework.observability import configure_otel_providers
configure_otel_providers(enable_sensitive_data=True)
```
## 5. Hello World Example with Tool
Below we create a **weather agent** using the Microsoft Agent Framework with Azure OpenAI. The agent has access to a `get_weather` function tool that it can call to retrieve weather information.
```python
import asyncio
from random import randint
from typing import Annotated
from agent_framework.azure import AzureOpenAIChatClient
from pydantic import Field
def get_weather(
location: Annotated[str, Field(description="The location to get the weather for.")],
) -> str:
"""Get the weather for a given location."""
conditions = ["sunny", "cloudy", "rainy", "stormy"]
return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
async def main():
# Create an agent with Azure OpenAI
async with AzureOpenAIChatClient().create_agent(
instructions="You are a helpful weather agent.",
tools=get_weather,
) as agent:
query = "What's the weather like in Seattle?"
print(f"User: {query}")
result = await agent.run(query)
print(f"Agent: {result}\n")
# Run the agent
await main()
```
## 6. Using OpenAI Directly
The Microsoft Agent Framework also supports using OpenAI directly (not through Azure). Simply use `OpenAIResponsesClient` instead of `AzureOpenAIChatClient`.
```python
# Required for OpenAI API access
os.environ.setdefault("OPENAI_API_KEY", "sk-proj-...");
os.environ.setdefault("OPENAI_RESPONSES_MODEL_ID", "gpt-5-mini");
```
```python
from agent_framework.openai import OpenAIResponsesClient
from typing import Annotated
from pydantic import Field
from random import randint
def get_weather(
location: Annotated[str, Field(description="The location to get the weather for.")],
) -> str:
"""Get the weather for a given location."""
conditions = ["sunny", "cloudy", "rainy", "stormy"]
return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
async def main():
async with OpenAIResponsesClient().create_agent(
instructions="You are a helpful assistant.",
tools=get_weather,
) as agent:
query = "What's the weather in Tokyo?"
print(f"User: {query}")
result = await agent.run(query)
print(f"Agent: {result}\n")
await main()
```
## 7. See Traces in Langfuse
[Example trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/8e419d3288419b5d944270505640e183?observation=2406e8343fd49c0e×tamp=2025-12-17T09:37:37.258Z)
## Interoperability with the Python SDK
You can use this integration together with the Langfuse [SDKs](/docs/observability/sdk/overview) to add additional attributes to the observation.
The [`@observe()` decorator](/docs/observability/sdk/instrumentation#custom-instrumentation) provides a convenient way to automatically wrap your instrumented code and add additional attributes to the observation.
```python
from langfuse import observe, propagate_attributes, get_client
langfuse = get_client()
@observe()
def my_llm_pipeline(input):
# Add additional attributes (user_id, session_id, metadata, version, tags) to all spans created within this execution scope
with propagate_attributes(
user_id="user_123",
session_id="session_abc",
tags=["agent", "my-observation"],
metadata={"email": "user@langfuse.com"},
version="1.0.0"
):
# YOUR APPLICATION CODE HERE
result = call_llm(input)
return result
# Run the function
my_llm_pipeline("Hi")
```
Learn more about using the Decorator in the [Langfuse SDK instrumentation docs](/docs/observability/sdk/instrumentation#custom-instrumentation).
The [Context Manager](/docs/observability/sdk/instrumentation#custom-instrumentation) allows you to wrap your instrumented code using context managers (with `with` statements), which allows you to add additional attributes to the observation.
```python
from langfuse import get_client, propagate_attributes
langfuse = get_client()
with langfuse.start_as_current_observation(
as_type="span",
name="my-observation",
trace_context={"trace_id": "abcdef1234567890abcdef1234567890"}, # Must be 32 hex chars
) as observation:
# Add additional attributes (user_id, session_id, metadata, version, tags)
# to all observations created within this execution scope
with propagate_attributes(
user_id="user_123",
session_id="session_abc",
metadata={"experiment": "variant_a", "env": "prod"},
version="1.0",
):
# YOUR APPLICATION CODE HERE
result = call_llm("some input")
# Flush events in short-lived applications
langfuse.flush()
```
Learn more about using the Context Manager in the [Langfuse SDK instrumentation docs](/docs/observability/sdk/instrumentation#custom-instrumentation).
## Troubleshooting
No observations appearing
First, enable [debug mode](/docs/observability/sdk/advanced-features#logging--debugging) in the Python SDK:
```bash
export LANGFUSE_DEBUG="True"
```
Then run your application and check the debug logs:
- **OTel observations appear in the logs:** Your application is instrumented correctly but observations are not reaching Langfuse. To resolve this:
1. Call [`langfuse.flush()`](/docs/observability/sdk/instrumentation#client-lifecycle--flushing) at the end of your application to ensure all observations are exported.
2. Verify that you are using the correct API keys and base URL.
- **No OTel spans in the logs:** Your application is not instrumented correctly. Make sure the instrumentation runs before your application code.
Unwanted observations in Langfuse
The Langfuse SDK is based on OpenTelemetry. Other libraries in your application may emit OTel spans that are not relevant to you. These still count toward your [billable units](/docs/administration/billable-units), so you should filter them out. See [Unwanted spans in Langfuse](/faq/all/unwanted-http-database-spans) for details.
Missing attributes
Some attributes may be stored in the metadata object of the observation rather than being mapped to the Langfuse data model. If a mapping or integration does not work as expected, please [raise an issue on GitHub](/issues).
## Next Steps
Once you have instrumented your code, you can manage, evaluate and debug your application:
- [Manage prompts in Langfuse](/docs/prompts/get-started)
- [Add evaluation scores](/docs/evaluation/features/evaluation-methods/custom-scores)
- [Run LLM-as-a-judge Evaluators](/docs/scores/model-based-evals)
- [Create datasets](/docs/datasets/overview)
- [Create custom dashboards](/docs/analytics/custom-dashboards)
- [Test queries in the Playground](/docs/playground)
---
## Agent Instructions
This page is part of the [Langfuse](https://langfuse.com) documentation, published as plain Markdown for AI agents. Every page is available as Markdown by appending `.md` to its URL, or by sending an `Accept: text/markdown` header. This page: `https://langfuse.com/integrations/frameworks/microsoft-agent-framework.md`.
### Querying these docs
If the answer is not on this page, query the documentation instead of guessing:
- **Semantic search** across all Langfuse docs, returning an answer with the relevant pages and excerpts. Ask a specific, self-contained question:
```bash
curl -sG "https://langfuse.com/api/search-docs" --data-urlencode "query=How do I trace a LangGraph agent?"
```
- **Index of every page**: , with per-section indexes [llms-docs.txt](https://langfuse.com/llms-docs.txt), [llms-integrations.txt](https://langfuse.com/llms-integrations.txt), and [llms-self-hosting.txt](https://langfuse.com/llms-self-hosting.txt).
### Before writing Langfuse code
- **Install the [Langfuse Agent Skill](https://langfuse.com/docs/api-and-data-platform/features/agent-skill).** It encodes Langfuse's own best practices for instrumentation, prompt management, and evaluation, and materially improves results.
- **Read [What does a good trace look like?](https://langfuse.com/docs/observability/best-practices.md)** before instrumenting an application.
- **Verify endpoints, parameters, and response fields** against the [API reference](https://api.reference.langfuse.com) instead of inferring them from code examples.
- **Use the [Langfuse CLI](https://langfuse.com/docs/api-and-data-platform/features/cli)** (`npx langfuse-cli api `) to read or write traces, prompts, datasets, and scores from the terminal.
Found an error in these docs? Please open an issue at .