---
title: "Langfuse SDK"
url: "/docs/pass_through/langfuse"
canonical_url: "https://docs.litellm.ai/docs/pass_through/langfuse"
type: "docs"
last_updated: "2026-10-08"
related:
  - "/docs/pass_through/google_ai_studio"
  - "/docs/pass_through/mistral"
---
# Langfuse SDK

> Index of all LiteLLM docs: https://docs.litellm.ai/llms.txt


適用於 Langfuse 的轉接端點 - 使用 LiteLLM Virtual Key 呼叫 langfuse 端點。

只要將 `https://us.cloud.langfuse.com` 替換為 `LITELLM_PROXY_BASE_URL/langfuse` 即可 🚀

#### **範例用法** {#example-usage}
```python
from langfuse import Langfuse

langfuse = Langfuse(
    host="http://localhost:4000/langfuse", # your litellm proxy endpoint
    public_key="anything",        # no key required since this is a pass through
    secret_key="LITELLM_VIRTUAL_KEY",        # no key required since this is a pass through
)

print("sending langfuse trace request")
span = langfuse.start_observation(name="test-trace-litellm-proxy-passthrough")
span.end()  # the root observation's name/input/output become the trace's
print("flushing langfuse request")
langfuse.flush()

print("flushed langfuse request")
```

支援 **所有** Langfuse 端點。

[**查看所有 Langfuse 端點**](https://api.reference.langfuse.com/)

## 快速開始 {#quick-start}

我們來將一個 trace 記錄到 Langfuse。

1. 將 Langfuse 公開/私密金鑰加入環境

```bash
export LANGFUSE_PUBLIC_KEY=""
export LANGFUSE_SECRET_KEY=""
```

2. 啟動 LiteLLM Proxy

```bash
litellm

# RUNNING on http://0.0.0.0:4000
```

3. 測試它！

我們來將一個 trace 記錄到 Langfuse！

```python
from langfuse import Langfuse

langfuse = Langfuse(
    host="http://localhost:4000/langfuse", # your litellm proxy endpoint
    public_key="anything",        # no key required since this is a pass through
    secret_key="anything",        # no key required since this is a pass through
)

print("sending langfuse trace request")
span = langfuse.start_observation(name="test-trace-litellm-proxy-passthrough")
span.end()  # the root observation's name/input/output become the trace's
print("flushing langfuse request")
langfuse.flush()

print("flushed langfuse request")
```

## 進階 - 搭配 Virtual Key 使用  {#advanced---use-with-virtual-keys}

前置需求
- [使用 DB 設定 proxy](../proxy/virtual_keys.md#setup)

使用這個方法，可避免將原始 Google AI Studio 金鑰提供給開發人員，同時仍讓他們能使用 Google AI Studio 端點。

### 用法 {#usage}

1. 設定環境

```bash
export DATABASE_URL=""
export LITELLM_MASTER_KEY=""
export LANGFUSE_PUBLIC_KEY=""
export LANGFUSE_SECRET_KEY=""
```

```bash
litellm

# RUNNING on http://0.0.0.0:4000
```

2. 產生 virtual key 

```bash
curl -X POST 'http://0.0.0.0:4000/key/generate' \
-H "Authorization: Bearer $LITELLM_API_KEY" \
-H 'Content-Type: application/json' \
-d '{}'
```

預期回應 

```bash
{
    ...
    "key": "sk-<virtual-key>"
}
```

3. 測試它！ 

```python
from langfuse import Langfuse

langfuse = Langfuse(
    host="http://localhost:4000/langfuse", # your litellm proxy endpoint
    public_key="anything",        # no key required since this is a pass through
    secret_key="sk-<your-litellm-api-key>",        # no key required since this is a pass through
)

print("sending langfuse trace request")
span = langfuse.start_observation(name="test-trace-litellm-proxy-passthrough")
span.end()  # the root observation's name/input/output become the trace's
print("flushing langfuse request")
langfuse.flush()

print("flushed langfuse request")
```

## [進階 - 將記錄寫入不同的 langfuse 專案（依金鑰/團隊）](../proxy/team_logging.md) {#advanced---log-to-separate-langfuse-projects-by-keyteam}

## Related pages

- [Google AI Studio SDK](https://docs.litellm.ai/docs/pass_through/google_ai_studio.md)
- [Mistral](https://docs.litellm.ai/docs/pass_through/mistral.md)