---
source: ⚠️ Jupyter Notebook
title: Observability for Claude Agent SDK with Langfuse
sidebarTitle: Claude Agent SDK
logo: /images/integrations/claude_icon.png
description: Learn how to trace Claude Agent SDK applications with Langfuse via OpenTelemetry
category: Integrations
---
# Integrate Langfuse with Claude Agent SDK
This notebook demonstrates how to capture detailed traces from the [Claude Agent SDK](https://github.com/anthropics/claude-agent-sdk-python) with **[Langfuse](https://langfuse.com)** using OpenTelemetry.
> **What is Claude Agent SDK?**\
> The [Claude Agent SDK](https://platform.claude.com/docs/en/agent-sdk/overview) is Anthropic's open-source framework for building AI agents. It provides a clean API for creating tool-using agents, including native support for MCP.
> **What is Langfuse?**\
> [Langfuse](https://langfuse.com) is the open source AI engineering platform. It provides detailed tracing, monitoring, and analytics for every prompt, model response, and tool call in your agent, making it easy to debug, evaluate, and iterate on LLM applications.
## Step 1: Install Dependencies
```python
%pip install langfuse claude-agent-sdk openinference-instrumentation-claude-agent-sdk -q
```
## Step 2: Set Up Environment Variables
Set up your **Langfuse** API keys ([Langfuse Cloud](https://cloud.langfuse.com) or [self-hosted](https://langfuse.com/self-hosting)) and your **Anthropic** API key ([Anthropic Console](https://console.anthropic.com/)).
```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 Anthropic API key
os.environ.setdefault("ANTHROPIC_API_KEY", "sk-ant-***");
```
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 3: OpenTelemetry Instrumentation
Use [OpenInference's Claude Agent SDK instrumentation library](https://github.com/Arize-ai/openinference/tree/main/python/instrumentation/openinference-instrumentation-claude-agent-sdk) to instrument the agent SDK and send OpenTelemetry spans to Langfuse.
```python
from openinference.instrumentation.claude_agent_sdk import ClaudeAgentSDKInstrumentor
ClaudeAgentSDKInstrumentor().instrument()
```
## Step 4: Build a Hello World Agent
Every tool call and model completion is captured as an OpenTelemetry span and forwarded to Langfuse.
```python
import asyncio
from claude_agent_sdk import (
ClaudeAgentOptions,
ClaudeSDKClient,
tool,
create_sdk_mcp_server,
)
from typing import Any
@tool(
"get_weather",
"Gets the current weather for a given city",
{
"city": str,
},
)
async def get_weather(args: dict[str, Any]) -> dict[str, Any]:
"""Simulated weather lookup tool"""
city = args["city"]
# Simulated weather data
weather_data = {
"Berlin": "Cloudy, 59°F",
"New York": "Sunny, 75°F",
}
weather = weather_data.get(city, "Weather data not available")
return {"content": [{"type": "text", "text": f"Weather in {city}: {weather}"}]}
async def main():
# Create SDK MCP server with the weather tool
weather_server = create_sdk_mcp_server(
name="weather",
version="1.0.0",
tools=[get_weather],
)
options = ClaudeAgentOptions(
model="claude-sonnet-4-5-20250929",
system_prompt="You are a friendly travel assistant who helps with weather information.",
mcp_servers={"weather": weather_server},
allowed_tools=["mcp__weather__get_weather"],
)
async with ClaudeSDKClient(options=options) as client:
await client.query("What's the weather like in Berlin and New York?")
async for message in client.receive_response():
print(message)
await main()
```
## Step 5: View the Trace in Langfuse
Head over to your **Langfuse dashboard → Traces**. You should see traces including all tool calls and model inputs/outputs.
[Link to trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/ff1e1c6309735ff53b94bffedfa98b70?observation=94fa8777d45b1545×tamp=2026-05-11T07:49:30.913Z&traceId=ff1e1c6309735ff53b94bffedfa98b70)
## 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/claude-agent-sdk.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 .