---
source: ⚠️ Jupyter Notebook
title: DSPy - Observability & Tracing
sidebarTitle: DSPy
logo: /images/integrations/dspy_icon.png
description: Open-source observability for DSPy, a framework that systematically optimizes language model prompts and weights.
category: Integrations
---

# DSPy - Observability & Tracing

This cookbook demonstrates how to use DSPy with Langfuse.

> **What is DSPy?** [DSPy](https://github.com/stanfordnlp/dspy) is a framework that systematically optimizes language model prompts and weights, making it easier to build and refine complex systems with LMs by automating the tuning process and improving reliability. For further information on DSPy, please visit the [documentation](https://dspy-docs.vercel.app/docs/intro).

> **What is Langfuse?** [Langfuse](https://langfuse.com) is an open-source AI engineering platform. It offers tracing and monitoring capabilities for LLM 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.

## Prerequisites

Install the latest versions of DSPy and langfuse.

```python
%pip install langfuse dspy openinference-instrumentation-dspy -U
```

<Steps>
## Step 1: Setup Langfuse Environment Variables

First, we configure the environment variables. We set the OpenTelemetry endpoint, protocol, and authorization headers so that the traces from DSPy are correctly sent to Langfuse. You can get your Langfuse API keys by signing up for [Langfuse Cloud](https://cloud.langfuse.com) or [self-hosting Langfuse](https://langfuse.com/self-hosting).

```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 OpenAI key
os.environ.setdefault("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.

```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.")

```

## Step 2: Enable Tracing for DSPy

Next, we use the [OpenInference Instrumentation](https://pypi.org/project/openinference-instrumentation-dspy/) module for DSPy to automatically capture your DSPy traces. This is done by a single call which instruments DSPy’s LM calls.

```python
from openinference.instrumentation.dspy import DSPyInstrumentor

DSPyInstrumentor().instrument()
```

## Step 3: Configure DSPy

Next, we set up DSPy. This involves initializing a language model and configuring DSPy to use it. You can then run various DSPy modules that showcase its features.

```python
import dspy
lm = dspy.LM('openai/gpt-4o-mini')
dspy.configure(lm=lm)
```

## Step 4: Running DSPy Modules with Observability

Here are a few examples from the [DSPy documentation](https://dspy.ai/) showing core features. Each example automatically sends trace data to Langfuse.

### Example 1: Using the Chain-of-Thought Module (Math Reasoning)

```python
math = dspy.ChainOfThought("question -> answer: float")
math(question="Two dice are tossed. What is the probability that the sum equals two?")
```

### Example 2: Building a RAG Pipeline

```python
def search_wikipedia(query: str) -> list[str]:
    results = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')(query, k=3)
    return [x['text'] for x in results]

rag = dspy.ChainOfThought('context, question -> response')

question = "What's the name of the castle that David Gregory inherited?"
rag(context=search_wikipedia(question), question=question)
```

### Example 3: Running a Classification Module with DSPy Signatures

```python
def evaluate_math(expression: str):
    return dspy.PythonInterpreter({}).execute(expression)

def search_wikipedia(query: str):
    results = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')(query, k=3)
    return [x['text'] for x in results]

react = dspy.ReAct("question -> answer: float", tools=[evaluate_math, search_wikipedia])

pred = react(question="What is 9362158 divided by the year of birth of David Gregory of Kinnairdy castle?")
print(pred.answer)
```

## Step 5: Viewing Traces in Langfuse

After running your DSPy application, you can inspect the traced events in Langfuse:

![Example trace in Langfuse](https://langfuse.com/images/cookbook/integration-dspy/dspy-example-trace.png)

_[Public example trace link in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/c6182b14e8e7ac7543645e19cdf1a895?timestamp=2025-06-06T15%3A59%3A47.264Z&display=details&observation=dfec1f8a95e91773)_

</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/dspy.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>.