---
title: "Literal AI"
url: "/docs/observability/literalai_integration"
canonical_url: "https://docs.litellm.ai/docs/observability/literalai_integration"
type: "docs"
last_updated: "2026-10-08"
related:
  - "/docs/observability/levo_integration"
  - "/docs/observability/logfire_integration"
---
# Literal AI

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


[Literal AI](https://literalai.com) 是一個協作式可觀測性、評估與分析平台，用於建構可供正式環境使用的 LLM 應用程式。

## 先決條件 {#pre-requisites}

請確認您已安裝 `literalai` 套件：

```shell
uv add literalai litellm
```

## 快速開始 {#quick-start}

```python
import litellm
import os

os.environ["LITERAL_API_KEY"] = ""
os.environ['OPENAI_API_KEY']= ""
os.environ['LITERAL_BATCH_SIZE'] = "1" # You won't see logs appear until the batch is full and sent

litellm.success_callback = ["literalai"] # Log Input/Output to LiteralAI
litellm.failure_callback = ["literalai"] # Log Errors to LiteralAI

# openai call
response = litellm.completion(
  model="gpt-5.6-luna",
  messages=[
    {"role": "user", "content": "Hi 👋 - i'm openai"}
  ]
)
```

## 多步驟追蹤 {#multi-step-traces}

此整合與 Literal AI SDK 裝飾器相容，可啟用對話與代理程式追蹤

```py
import litellm
from literalai import LiteralClient
import os

os.environ["LITERAL_API_KEY"] = ""
os.environ['OPENAI_API_KEY']= ""
os.environ['LITERAL_BATCH_SIZE'] = "1" # You won't see logs appear until the batch is full and sent

litellm.input_callback = ["literalai"] # Support other Literal AI decorators and prompt templates
litellm.success_callback = ["literalai"] # Log Input/Output to LiteralAI
litellm.failure_callback = ["literalai"] # Log Errors to LiteralAI

literalai_client = LiteralClient()

@literalai_client.run
def my_agent(question: str):
    # agent logic here
    response = litellm.completion(
        model="gpt-5.6-luna",
        messages=[
            {"role": "user", "content": question}
        ],
        metadata={"literalai_parent_id": literalai_client.get_current_step().id}
    )
    return response

my_agent("Hello world")

# Waiting to send all logs before exiting, not needed in a production server
literalai_client.flush()
```

深入了解 [Literal AI 記錄功能](https://docs.literalai.com/guides/logs)。

## 將生成內容綁定至其提示範本 {#bind-a-generation-to-its-prompt-template}

此整合可直接與在 Literal AI 上管理的提示搭配使用。這表示特定的 LLM 生成內容會綁定到其範本。

深入了解 Literal AI 上的 [提示管理](https://docs.literalai.com/guides/prompt-management#pull-a-prompt-template-from-literal-ai)。

## OpenAI Proxy 使用方式 {#openai-proxy-usage}

如果您使用的是 Lite LLM proxy，您可以使用 Literal AI OpenAI instrumentation 來記錄您的請求。

```py
from literalai import LiteralClient
from openai import OpenAI

client = OpenAI(
    api_key="anything",            # litellm proxy virtual key
    base_url="http://0.0.0.0:4000" # litellm proxy base_url
)

literalai_client = LiteralClient(api_key="")

# Instrument the OpenAI client
literalai_client.instrument_openai()

settings = {
    "model": "gpt-5.6-luna", # model you want to send litellm proxy
    "temperature": 0,
    # ... more settings
}

response = client.chat.completions.create(
        messages=[
            {
                "content": "You are a helpful bot, you always reply in Spanish",
                "role": "system"
            },
            {
                "content": message.content,
                "role": "user"
            }
        ],
        **settings
    )

```

## Related pages

- [Levo](https://docs.litellm.ai/docs/observability/levo_integration.md)
- [Logfire](https://docs.litellm.ai/docs/observability/logfire_integration.md)