---
source: ⚠️ Jupyter Notebook
title: Observability for smolagents with Langfuse
sidebarTitle: SmolAgents
logo: /images/integrations/huggingface_icon.svg
description: Learn how to integrate Langfuse with Hugging Face smolagents using OpenTelemetry. This lets you trace and debug your AI agents.
category: Integrations
---

# Integrate Langfuse with smolagents

This notebook shows how to monitor and debug your Hugging Face **smolagents** with **Langfuse** using the `SmolagentsInstrumentor`. By the end of this guide, you will be able to trace your smolagents applications with Langfuse.

> **What are smolagents?** [smolagents](https://github.com/huggingface/smolagents) is a minimalist and open-source AI agent framework developed by Hugging Face, designed to simplify the creation and deployment of powerful agents with just a few lines of code. It focuses on simplicity and efficiency, making it easy for developers to leverage LLMs for various applications.

> **What is Langfuse?** [Langfuse](https://langfuse.com) is an open-source platform for LLM engineering. It provides tracing and monitoring capabilities for AI agents, helping developers debug, analyze, and optimize their products. Langfuse integrates with various tools and frameworks via native integrations, OpenTelemetry, and SDKs.

## Get Started

We'll walk through a simple example of using smolagents and integrating it with Langfuse.

<Steps>
### Step 1: Install Dependencies

```python
%pip install langfuse 'smolagents[telemetry]' opentelemetry-sdk opentelemetry-exporter-otlp openinference-instrumentation-smolagents ddgs
```

### Step 2: Set Up Environment Variables

Get your Langfuse API keys by signing up for [Langfuse Cloud](https://cloud.langfuse.com) or [self-hosting Langfuse](https://langfuse.com/self-hosting).

Also, add your [Hugging Face token](https://huggingface.co/settings/tokens) (`HF_TOKEN`) as an environment variable.

```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 Hugging Face token
os.environ.setdefault("HF_TOKEN", "hf_...");
```

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.

```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.")
```

    Langfuse client is authenticated and ready!

### Step 3: Initialize the `SmolagentsInstrumentor`

Initialize the `[SmolagentsInstrumentor](https://pypi.org/project/openinference-instrumentation-smolagents/)` before your application code.

```python
from openinference.instrumentation.smolagents import SmolagentsInstrumentor

SmolagentsInstrumentor().instrument()
```

### Step 4: Run your smolagent

This smolagent example has a manager `CodeAgent` that orchestrates the `managed_agent`, which can perform web searches to gather data. By using tools like `DuckDuckGoSearchTool` and `VisitWebpageTool`, it retrieves the U.S. 2024 growth rate and calculates how many years it will take for the GDP to double.

```python
from smolagents import (
    CodeAgent,
    ToolCallingAgent,
    DuckDuckGoSearchTool,
    VisitWebpageTool,
    InferenceClientModel,
)

model = InferenceClientModel(
    model_id="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
)

search_agent = ToolCallingAgent(
    tools=[DuckDuckGoSearchTool(), VisitWebpageTool()],
    model=model,
    name="search_agent",
    description="This is an agent that can do web search.",
)

manager_agent = CodeAgent(
    tools=[],
    model=model,
    managed_agents=[search_agent],
)
manager_agent.run(
    "How can Langfuse be used to monitor and improve the reasoning and decision-making of smolagents when they execute multi-step tasks, like dynamically adjusting a recipe based on user feedback or available ingredients?"
)
```

### Step 5: View Traces in Langfuse

After running the agent, you can view the traces generated by your smolagents application in [Langfuse](https://cloud.langfuse.com). You should see detailed steps of the LLM interactions, which can help you debug and optimize your AI agent.

![smolagents example trace](https://langfuse.com/images/cookbook/integration-smolagents/smolagent_example_trace.png)

_[Public example trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/ce5160f9bfd5a6cd63b07d2bfcec6f54?timestamp=2025-02-11T09%3A25%3A45.163Z&display=details)_

</Steps>

## 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.

<Tabs items={["Decorator", "Context Manager"]}>
<Tab>

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).

</Tab>
<Tab>

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).

</Tab>
</Tabs>

## Troubleshooting

<details>
<summary>No observations appearing</summary>

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.

</details>

<details>
<summary>Unwanted observations in Langfuse</summary>

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.

</details>

<details>
<summary>Missing attributes</summary>

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).

</details>

## 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 -->

---

## 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/smolagents.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**: <https://langfuse.com/llms.txt>, 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 <resource> <action>`) to read or write traces, prompts, datasets, and scores from the terminal.

Found an error in these docs? Please open an issue at <https://github.com/langfuse/langfuse-docs/issues>.