Integrate Langfuse with AutoGen
This notebook provides a step-by-step guide on integrating Langfuse with AutoGen to achieve comprehensive observability and debugging for your multi-agent LLM applications.
What is AutoGen? AutoGen (GitHub) is an open-source framework developed by Microsoft for building LLM applications, including agents capable of complex reasoning and interactions. AutoGen simplifies the creation of conversational agents that can collaborate or compete to solve tasks.
What is Langfuse? Langfuse is an open-source LLM engineering platform. It offers tracing and monitoring capabilities for AI applications. Langfuse helps developers debug, analyze, and optimize their AI systems by providing detailed insights and integrating with a wide array of tools and frameworks through native integrations, OpenTelemetry, and dedicated SDKs.
Getting Started
Let’s walk through a practical example of using AutoGen and integrating it with Langfuse via OpenTelemetry for comprehensive tracing.
Step 1: Install Dependencies
Note: This notebook utilizes the Langfuse OTel Python SDK v3. For users of Python SDK v2, please refer to our legacy AutoGen integration guide.
%pip install langfuse openlit "autogen-agentchat" "autogen-ext[openai]" -UStep 2: Configure Langfuse SDK
Next, set up your Langfuse API keys. You can get these keys by signing up for a free Langfuse Cloud account or by self-hosting Langfuse. These environment variables are essential for the Langfuse client to authenticate and send data to your Langfuse project.
import os
# Get keys for your project from the project settings page: https://cloud.langfuse.com
os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-lf-..."
os.environ["LANGFUSE_SECRET_KEY"] = "sk-lf-..."
os.environ["LANGFUSE_HOST"] = "https://cloud.langfuse.com" # 🇪🇺 EU region
# os.environ["LANGFUSE_HOST"] = "https://us.cloud.langfuse.com" # 🇺🇸 US region
# Your OpenAI key
os.environ["OPENAI_API_KEY"] = "sk-proj-..."With the environment variables set, we can now initialize the Langfuse client. get_client() initializes the Langfuse client using the credentials provided in the environment variables.
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.")
Step 3: Initialize OpenLit Instrumentation
Now, we initialize the OpenLit instrumentation. OpenLit automatically captures AutoGen operations and exports OpenTelemetry (OTel) spans to Langfuse.
import openlit
# Initialize OpenLIT instrumentation. The disable_batch flag is set to true to process traces immediately.
openlit.init(tracer=langfuse._otel_tracer, disable_batch=True)Step 5: Basic AutoGen Application
Let’s create a straightforward AutoGen application. In this example, we’ll create a simple multi-agent conversation where an Assistant agent answers a user’s question. This will serve as the foundation for demonstrating Langfuse tracing.
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
model_client = OpenAIChatCompletionClient(model="gpt-4o")
agent = AssistantAgent("assistant", model_client=model_client)
print(await agent.run(task="Say 'Hello World!'"))
await model_client.close()Step 6: View Traces in Langfuse
After executing the application, navigate to your Langfuse Trace Table. You will find detailed traces of the application’s execution, providing insights into the agent conversations, LLM calls, inputs, outputs, and performance metrics. The trace will show the complete flow from the initial user query through the agent interactions to the final response.
You can also view the public trace here: Langfuse Trace Example
Add Additional Attributes
Langfuse allows you to pass additional attributes to your spans. These can include user_id, tags, session_id, and custom metadata. Enriching traces with these details is important for analysis, debugging, and monitoring of your application’s behavior across different users or sessions.
The following code demonstrates how to start a custom span with langfuse.start_as_current_span and then update the trace associated with this span using span.update_trace().
→ Learn more about Updating Trace and Span Attributes.
with langfuse.start_as_current_span(
name="autogen-chat-trace",
) as span:
# Run your application here
task= "Say 'Hello World!'"
model_client = OpenAIChatCompletionClient(model="gpt-4o")
agent = AssistantAgent("assistant", model_client=model_client)
response = await agent.run(task=task)
print(response.messages[1].content)
await model_client.close()
# Pass additional attributes to the span
span.update_trace(
input=task,
output=response.messages[1].content,
user_id="user_123",
session_id="session_abc",
tags=["dev", "autogen"],
metadata={"email": "[email protected]"},
version="1.0.0"
)
# Flush events in short-lived applications
langfuse.flush()Score Traces and Spans
Langfuse lets you ingest custom scores for individual spans or entire traces. This scoring workflow enables you to implement custom quality checks at runtime or facilitate human-in-the-loop evaluation processes.
In the example below, we demonstrate how to score a specific span for relevance (a numeric score) and the overall trace for feedback (a categorical score). This helps in systematically assessing and improving your application.
→ Learn more about Custom Scores in Langfuse.
with langfuse.start_as_current_span(
name="autogen-chat-trace",
) as span:
# Run your application here
task= "Say 'Hello World!'"
model_client = OpenAIChatCompletionClient(model="gpt-4o")
agent = AssistantAgent("assistant", model_client=model_client)
response = await agent.run(task=task)
print(response.messages[1].content)
await model_client.close()
# Score this specific span
span.score(name="relevance", value=0.9, data_type="NUMERIC")
# Score the overall trace
span.score_trace(name="feedback", value="positive", data_type="CATEGORICAL")
# Flush events in short-lived applications
langfuse.flush()Manage Prompts with Langfuse
Langfuse Prompt Management allows you to collaboratively create, version, and deploy prompts. You can manage prompts either through the Langfuse SDK or directly within the Langfuse UI. These managed prompts can then be fetched into your application at runtime.
The code below illustrates fetching a prompt named agent-system-message from Langfuse, compiling it with an input variable, and then using this compiled prompt to configure your AutoGen agent.
→ Get started with Langfuse Prompt Management.
Note: Linking the Langfuse Prompt and the Generation is currently not possible. This is on our roadmap and we are tracking this here.
# Fetch the prompt from langfuse
langfuse_prompt = langfuse.get_prompt(name="answer-question")
# Compile the prompt with the input
compiled_prompt = langfuse_prompt.compile(country = "France")
# Run your application
with langfuse.start_as_current_span(
name="autogen-trace",
) as span:
# Run your application here
model_client = OpenAIChatCompletionClient(model="gpt-4o")
agent = AssistantAgent("assistant", model_client=model_client)
response = await agent.run(task=compiled_prompt)
print(response.messages[1].content)
await model_client.close()
# Flush events in short-lived applications
langfuse.flush()
Dataset Experiments
Offline evaluation using datasets is a critical part of the LLM development lifecycle. Langfuse supports this through Dataset Experiments. The typical workflow involves:
- Benchmark Dataset: Defining a dataset with input prompts and their corresponding expected outputs.
- Application Run: Running your LLM application against each item in the dataset.
- Evaluation: Comparing the generated outputs against the expected results or using other scoring mechanisms (e.g., model-based evaluation) to assess performance.
The following example demonstrates how to use a pre-existing dataset containing questions and their expected answers to run an experiment with your AutoGen application.
→ Learn more about Langfuse Dataset Experiments.
from langfuse import get_client
langfuse = get_client()
# Fetch an existing dataset
dataset = langfuse.get_dataset(name="capital_cities_11")
for item in dataset.items:
print(f"Input: {item.input['country']}, Expected Output: {item.expected_output}")
Next, we iterate through each item in the dataset, run our AutoGen application with the item’s input, and log the results as a run associated with that dataset item in Langfuse. This allows for structured evaluation and comparison of different application versions or prompt configurations.
The item.run() context manager is used to create a new trace for each dataset item processed in the experiment. Optionally you can score the dataset runs.
from langfuse import get_client
langfuse = get_client()
dataset_name = "capital_cities_11"
current_run_name = "capital_cities_run-autogen_02" # Identifies this specific evaluation run
current_run_metadata={"model_provider": "OpenAI", "temperature_setting": 0.7}
current_run_description="Evaluation run for Q&A model on June 4th"
# Assume 'your_application' is your instrumented application function
async def your_application(question):
with langfuse.start_as_current_span(name="autogen-trace") as span:
model_client = OpenAIChatCompletionClient(model="gpt-4o")
agent = AssistantAgent("assistant", model_client=model_client)
response = await agent.run(task=question)
print(response.messages[1].content)
await model_client.close()
# Update the trace with the input and output
span.update_trace(
input=question,
output=response.messages[1].content,
)
return response.messages[1].content
dataset = langfuse.get_dataset(name=dataset_name) # Fetch your pre-populated dataset
for item in dataset.items:
print(f"Running evaluation for item: {item.id} (Input: {item.input['country']})")
# Use the item.run() context manager
with item.run(
run_name=current_run_name,
run_metadata=current_run_metadata,
run_description=current_run_description
) as root_span:
# All subsequent langfuse operations within this block are part of this trace.
generated_answer = await your_application(
question="What is the capital of " + item.input["country"] + "? Just answer with the name of the city.",
)
# Optionally, score the result against the expected output
if item.expected_output and generated_answer == item.expected_output:
root_span.score_trace(name="exact_match", value=1.0)
else:
root_span.score_trace(name="exact_match", value=0.0)
print(f"\nFinished processing dataset '{dataset_name}' for run '{current_run_name}'.")
Explore More Langfuse Features
Langfuse offers more features to enhance your LLM development and observability workflow: