---
title: "Instructor"
url: "/docs/tutorials/instructor"
canonical_url: "https://docs.litellm.ai/docs/tutorials/instructor"
type: "docs"
last_updated: "2026-10-08"
related:
  - "/docs/projects/openai-agents"
  - "/docs/manage_with_ai_agents"
---
# Instructor

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


將 LiteLLM 與 [jxnl 的 instructor 函式庫](https://github.com/jxnl/instructor) 結合，以取得更穩健的結構化輸出。輸出會自動驗證為 Pydantic 類型，且驗證錯誤會回傳給模型，以提高重試時成功回應的機會。

## 用法（同步） {#usage-sync}

```python
import instructor
from litellm import completion
from pydantic import BaseModel

client = instructor.from_litellm(completion)

class User(BaseModel):
    name: str
    age: int

def extract_user(text: str):
    return client.chat.completions.create(
        model="gpt-5.6-luna",
        response_model=User,
        messages=[
            {"role": "user", "content": text},
        ],
        max_retries=3,
    )

user = extract_user("Jason is 25 years old")

assert isinstance(user, User)
assert user.name == "Jason"
assert user.age == 25
print(f"{user=}")
```

## 用法（非同步） {#usage-async}

```python
import asyncio

import instructor
from litellm import acompletion
from pydantic import BaseModel

client = instructor.from_litellm(acompletion)

class User(BaseModel):
    name: str
    age: int

async def extract(text: str) -> User:
    return await client.chat.completions.create(
        model="gpt-5.6-luna",
        response_model=User,
        messages=[
            {"role": "user", "content": text},
        ],
        max_retries=3,
    )

user = asyncio.run(extract("Alice is 30 years old"))

assert isinstance(user, User)
assert user.name == "Alice"
assert user.age == 30
print(f"{user=}")
```

## Related pages

- [OpenAI Agents SDK](https://docs.litellm.ai/docs/projects/openai-agents.md)
- [Manage with AI Agents](https://docs.litellm.ai/docs/manage_with_ai_agents.md)