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‎integrations/tracing-providers/langfuse.mdx

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---
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title: "Langfuse"
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description: "Integrate Langfuse observability with Portkey's AI gateway for comprehensive LLM monitoring and advanced routing capabilities"
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---
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Langfuse is an open-source LLM observability platform that helps you monitor, debug, and analyze your LLM applications. When combined with Portkey, you get the best of both worlds: Langfuse's detailed observability and Portkey's advanced AI gateway features.
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This integration allows you to:
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- Track all LLM requests in Langfuse while routing through Portkey
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- Use Portkey's 250+ LLM providers with Langfuse observability
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- Implement advanced features like caching, fallbacks, and load balancing
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- Maintain detailed traces and analytics in both platforms
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## Quick Start Integration
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Since Portkey provides an OpenAI-compatible API, integrating with Langfuse is straightforward using Langfuse's OpenAI wrapper.
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### Installation
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```bash
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pip install portkey-ai langfuse openai
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```
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### Basic Setup
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```python
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import os
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from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
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# Set your Langfuse credentials
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os.environ["LANGFUSE_PUBLIC_KEY"] = "YOUR_LANGFUSE_PUBLIC_KEY"
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os.environ["LANGFUSE_SECRET_KEY"] = "YOUR_LANGFUSE_SECRET_KEY"
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# Import OpenAI from langfuse
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from langfuse.openai import OpenAI
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# Initialize the client
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client = OpenAI(
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api_key="YOUR_OPENAI_API_KEY", # Your LLM provider API key
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base_url=PORTKEY_GATEWAY_URL,
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default_headers=createHeaders(
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api_key="YOUR_PORTKEY_API_KEY",
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virtual_key="YOUR_VIRTUAL_KEY", # Optional: Use virtual keys
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# config="YOUR_CONFIG_ID", # Optional: Use saved configs
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# trace_id="YOUR_TRACE_ID", # Optional: Custom trace ID
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)
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)
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# Make a request
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response = client.chat.completions.create(
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model="gpt-4o-mini",
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messages=[{"role": "user", "content": "Hello, world!"}],
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)
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print(response.choices[0].message.content)
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```
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<Note>
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This integration automatically logs requests to both Langfuse and Portkey, giving you observability data in both platforms.
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</Note>
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## Using Portkey Features with Langfuse
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### 1. Virtual Keys
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Virtual Keys in Portkey allow you to securely manage API keys and set usage limits. Use them with Langfuse for better security:
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```python
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from langfuse.openai import OpenAI
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from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
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client = OpenAI(
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api_key="dummy_key", # Not used when virtual key is provided
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base_url=PORTKEY_GATEWAY_URL,
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default_headers=createHeaders(
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api_key="YOUR_PORTKEY_API_KEY",
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virtual_key="YOUR_VIRTUAL_KEY"
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)
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)
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response = client.chat.completions.create(
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model="gpt-4o",
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messages=[{"role": "user", "content": "Explain quantum computing"}]
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)
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```
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### 2. Multiple Providers
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Switch between 250+ LLM providers while maintaining Langfuse observability:
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<Tabs>
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<Tab title="OpenAI">
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```python
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client = OpenAI(
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api_key="YOUR_OPENAI_KEY",
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base_url=PORTKEY_GATEWAY_URL,
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default_headers=createHeaders(
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api_key="YOUR_PORTKEY_API_KEY",
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provider="openai"
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)
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)
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```
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</Tab>
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<Tab title="Anthropic">
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```python
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client = OpenAI(
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api_key="YOUR_ANTHROPIC_KEY",
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base_url=PORTKEY_GATEWAY_URL,
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default_headers=createHeaders(
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api_key="YOUR_PORTKEY_API_KEY",
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provider="anthropic",
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metadata={"model_override": "claude-3-opus-20240229"}
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)
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)
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```
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</Tab>
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<Tab title="Azure OpenAI">
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```python
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client = OpenAI(
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api_key="dummy_key",
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base_url=PORTKEY_GATEWAY_URL,
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default_headers=createHeaders(
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api_key="YOUR_PORTKEY_API_KEY",
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virtual_key="YOUR_AZURE_VIRTUAL_KEY"
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)
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)
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```
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</Tab>
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</Tabs>
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### 3. Advanced Routing with Configs
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Use Portkey's config system for advanced features while tracking in Langfuse:
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```python
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# Create a config in Portkey dashboard first, then reference it
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client = OpenAI(
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api_key="dummy_key",
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base_url=PORTKEY_GATEWAY_URL,
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default_headers=createHeaders(
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api_key="YOUR_PORTKEY_API_KEY",
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config="pc-langfuse-prod" # Your saved config ID
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)
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)
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```
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Example config for fallback between providers:
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```json
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{
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"strategy": {
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"mode": "fallback"
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},
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"targets": [
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{
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"virtual_key": "openai-key",
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"override_params": {"model": "gpt-4o"}
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},
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{
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"virtual_key": "anthropic-key",
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"override_params": {"model": "claude-3-opus-20240229"}
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}
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]
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}
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```
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### 4. Caching for Cost Optimization
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Enable caching to reduce costs while maintaining full observability:
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```python
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config = {
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"cache": {
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"mode": "semantic",
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"max_age": 3600
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},
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"virtual_key": "YOUR_VIRTUAL_KEY"
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}
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client = OpenAI(
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api_key="dummy_key",
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base_url=PORTKEY_GATEWAY_URL,
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default_headers=createHeaders(
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api_key="YOUR_PORTKEY_API_KEY",
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config=config
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)
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)
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```
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### 5. Custom Metadata and Tracing
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Add custom metadata visible in both Langfuse and Portkey:
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```python
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client = OpenAI(
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api_key="YOUR_API_KEY",
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base_url=PORTKEY_GATEWAY_URL,
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default_headers=createHeaders(
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api_key="YOUR_PORTKEY_API_KEY",
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provider="openai",
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metadata={
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"user_id": "user_123",
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"session_id": "session_456",
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"environment": "production"
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},
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trace_id="langfuse-trace-001"
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)
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)
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```
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<CardGroup cols={3}>
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<Card title="Fallbacks" icon="life-ring" href="/product/ai-gateway/fallbacks">
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Automatically switch to backup targets if the primary target fails.
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</Card>
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<Card title="Conditional Routing" icon="route" href="/product/ai-gateway/conditional-routing">
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Route requests to different targets based on specified conditions.
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</Card>
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<Card title="Load Balancing" icon="key" href="/docs/product/ai-gateway/load-balancing">
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Distribute requests across multiple targets based on defined weights.
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</Card>
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<Card title="Caching" icon="database" href="/product/ai-gateway/caching">
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Enable caching of responses to improve performance and reduce costs.
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</Card>
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<Card title="Smart Retries" icon="database" href="/product/ai-gateway/retries">
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Automatic retry handling with exponential backoff for failed requests
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</Card>
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<Card title="Budget Limits" icon="shield-check" href="/product/ai-gateway/virtual-keys/budget-limits">
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Set and manage budget limits across teams and departments. Control costs with granular budget limits and usage tracking.
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</Card>
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</CardGroup>
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## Observability Features
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With this integration, you get:
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### In Langfuse:
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- request/response logging
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- Latency tracking
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- Token usage analytics
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- Cost calculation
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- Trace visualization
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### In Portkey:
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- Request logs with provider details
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- Advanced analytics across providers
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- Cost tracking and budgets
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- Performance metrics
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- Custom dashboards
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- Token usage analytics
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<Frame>
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<img src="/images/integrations/observability.png" width="600"/>
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</Frame>
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## Migration Guide
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If you're already using Langfuse with OpenAI, migrating to use Portkey is simple:
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<CodeGroup>
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```python Before
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from langfuse.openai import OpenAI
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client = OpenAI(
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api_key="YOUR_OPENAI_KEY"
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)
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```
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```python After
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from langfuse.openai import OpenAI
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from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
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client = OpenAI(
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api_key="YOUR_OPENAI_KEY",
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base_url=PORTKEY_GATEWAY_URL,
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default_headers=createHeaders(
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api_key="YOUR_PORTKEY_API_KEY",
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provider="openai"
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)
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)
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```
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</CodeGroup>
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## Next Steps
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- [Create Virtual Keys](/product/ai-gateway/virtual-keys) for secure API key management
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- [Build Configs](/product/ai-gateway/configs) for advanced routing
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- [Set up Guardrails](/product/guardrails) for content filtering
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- [Implement Caching](/product/ai-gateway/cache-simple-and-semantic) for cost optimization
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## Resources
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- [Langfuse Documentation](https://langfuse.com/docs)
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- [Portkey AI Gateway Guide](/product/ai-gateway)
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- [Portkey Python SDK Reference](/api-reference/portkey-sdk-client)
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<Note>
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For enterprise support and custom features, contact our [enterprise team](https://calendly.com/portkey-ai).
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</Note>

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