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---
title: "Structured outputs with xAI, a complete guide with instructor"
description: "Learn how to use Instructor with xAI's Grok models for type-safe, structured outputs. Complete guide with examples and best practices."
---
# Structured outputs with xAI, a complete guide with instructor
xAI provides access to Grok models through the `xai-sdk` package, enabling structured outputs with Instructor. This guide covers everything you need to know about using xAI's Grok models with Instructor for type-safe, validated responses.
## Quick Start
Instructor is distributed without xAI dependencies by default. Install xAI support with the optional `xai` extra:
```bash
pip install "instructor[xai]"
```
Or using uv:
```bash
uv pip install "instructor[xai]"
```
⚠️ **Important**: You must set your xAI API key before using the client. You can do this in two ways:
1. Set the environment variable:
```bash
export XAI_API_KEY='your-api-key-here'
```
2. The xAI SDK will use this environment variable automatically.
## Simple User Example (Sync)
```python
import instructor
from pydantic import BaseModel
# Auto-configure xAI client
client = instructor.from_provider("xai/grok-3-mini")
class User(BaseModel):
name: str
age: int
# Create structured output
user = client.create(
response_model=User,
messages=[
{"role": "user", "content": "Extract: Jason is 25 years old"},
],
)
print(user)
#> User(name='Jason', age=25)
```
## Simple User Example (Async)
```python
import instructor
from pydantic import BaseModel
import asyncio
# Auto-configure async xAI client
client = instructor.from_provider("xai/grok-3-mini", async_client=True)
class User(BaseModel):
name: str
age: int
async def extract_user():
user = await client.create(
response_model=User,
messages=[
{"role": "user", "content": "Extract: Jason is 25 years old"},
],
)
return user
# Run async function
user = asyncio.run(extract_user())
print(user)
#> User(name='Jason', age=25)
```
## Nested Example
```python
from pydantic import BaseModel
from typing import List
import instructor
class Address(BaseModel):
street: str
city: str
country: str
class User(BaseModel):
name: str
age: int
addresses: List[Address]
# Auto-configure xAI client
client = instructor.from_provider("xai/grok-3-mini")
# Create structured output with nested objects
user = client.create(
response_model=User,
messages=[
{"role": "user", "content": """
Extract: Jason is 25 years old.
He lives at 123 Main St, New York, USA
and has a summer house at 456 Beach Rd, Miami, USA
"""},
],
)
print(user)
#> {
#> 'name': 'Jason',
#> 'age': 25,
#> 'addresses': [
#> {
#> 'street': '123 Main St',
#> 'city': 'New York',
#> 'country': 'USA'
#> },
#> {
#> 'street': '456 Beach Rd',
#> 'city': 'Miami',
#> 'country': 'USA'
#> }
#> ]
#> }
```
## Instructor Modes
xAI supports the following modes:
1. `instructor.Mode.JSON` : Forces the model to return JSON output (default)
2. `instructor.Mode.TOOLS` : Uses function calling for structured outputs
```python
import instructor
from instructor import Mode
# Using JSON mode (default)
client = instructor.from_provider("xai/grok-3-mini", mode=Mode.JSON)
# Using TOOLS mode
client = instructor.from_provider("xai/grok-3-mini", mode=Mode.TOOLS)
```
## Available Models
xAI provides access to the following models:
- **grok-3** - The most capable Grok model for complex reasoning tasks
- **grok-3-mini** - A smaller, faster version optimized for speed and cost
## Limitations
### Streaming Support
⚠️ **Note**: Streaming responses (`create_iterable` and `create_partial`) are not yet supported due to differences in xAI's streaming API. See [issue #1663](https://github.com/567-labs/instructor/issues/1663) for updates.
### Python Version
⚠️ **Requires Python 3.10+**: The xAI SDK requires Python 3.10 or higher.
## Best Practices
### 1. API Key Management
Store your xAI API key securely using environment variables:
```bash
export XAI_API_KEY="your-api-key-here"
```
### 2. Model Selection
- Use `grok-3-mini` for:
- Simple extraction tasks
- High-volume processing
- Cost-sensitive applications
- Use `grok-3` for:
- Complex reasoning tasks
- Multi-step analysis
- Higher accuracy requirements
### 3. Error Handling
Always handle potential API errors gracefully:
```python
try:
user = client.create(
response_model=User,
messages=[{"role": "user", "content": "Extract user data"}],
)
except Exception as e:
print(f"Error: {e}")
```
## Common Use Cases
- Data Extraction from unstructured text
- Form parsing and validation
- Content classification
- Entity recognition
- Structured data generation
## Related Resources
- [xAI Documentation](https://docs.x.ai/)
- [Instructor Core Concepts](../concepts/index.md)
- [Type Validation Guide](../concepts/validation.md)
- [Advanced Usage Examples](../examples/index.md)
## Updates and Compatibility
Instructor maintains compatibility with the latest xAI SDK versions. Check the [changelog](https://github.com/jxnl/instructor/blob/main/CHANGELOG.md) for updates.