LLM Provider Integration Tutorials¶

Learn how to integrate Instructor with various AI model providers. These comprehensive tutorials cover everything from cloud-based services like OpenAI and Anthropic to local open-source models, helping you extract structured outputs from any LLM.

Common Features¶

All integrations support these core features:

However, each provider has different capabilities and limitations. Refer to the specific provider documentation for details.

Provider Modes¶

Providers support different methods for generating structured outputs:

See the Modes Comparison guide for details.

Getting Started¶

There are two ways to use providers with Instructor:

1. Using Provider Initialization (Recommended)¶

The simplest way to get started is using the provider initialization:

import instructor
from pydantic import BaseModel

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

# Initialize any provider with a simple string
client = instructor.from_provider("openai/gpt-5.4-mini")
# Or use async client
async_client = instructor.from_provider("anthropic/claude-3-sonnet", async_client=True)

# Use the same interface for all providers
response = client.create(
    response_model=UserInfo,
    messages=[{"role": "user", "content": "Your prompt"}]
)

Supported provider strings: - openai/model-name: OpenAI models - anthropic/model-name: Anthropic models - google/model-name: Google models - mistral/model-name: Mistral models - cohere/model-name: Cohere models - perplexity/model-name: Perplexity models - groq/model-name: Groq models - writer/model-name: Writer models - bedrock/model-name: AWS Bedrock models - cerebras/model-name: Cerebras models - fireworks/model-name: Fireworks models - vertexai/model-name: Vertex AI models - genai/model-name: Google GenAI models - ollama/model-name: Ollama models

Provider Checklist¶

Use these example strings with from_provider to quickly get started:

• instructor.from_provider("openai/gpt-5-nano")
• instructor.from_provider("anthropic/claude-3-sonnet")
• instructor.from_provider("google/gemini-2.5-flash")
• instructor.from_provider("mistral/mistral-large-latest")
• instructor.from_provider("cohere/command-r")
• instructor.from_provider("perplexity/sonar-small")
• instructor.from_provider("groq/llama3-8b-8192")
• instructor.from_provider("writer/palmyra-x-004")
• instructor.from_provider("bedrock/anthropic.claude-3-sonnet-20240229-v1:0")
• instructor.from_provider("cerebras/llama3.1-70b")
• instructor.from_provider("fireworks/llama-v3-70b-instruct")
• instructor.from_provider("vertexai/gemini-3-flash")
• instructor.from_provider("genai/gemini-3-flash")
• instructor.from_provider("ollama/llama3.2")

2. Manual Client Setup¶

Alternatively, you can manually set up the client:

1. Install the required dependencies:

   pip install "instructor[provider]"  # e.g., instructor[anthropic]
   

2. Import the provider client and patch it with Instructor:

   import instructor
   from provider_package import Client
   
   client = instructor.from_provider(Client())
   

3. Use the patched client with your Pydantic model:

   response = client.create(
       response_model=YourModel,
       messages=[{"role": "user", "content": "Your prompt"}]
   )
   

For provider-specific setup and examples, visit each provider's documentation page.

Need Help?¶

If you need assistance with a specific integration:

1. Check the provider-specific documentation
2. Browse the examples and cookbooks
3. Search existing GitHub issues
4. Join our Discord community