567-labs--instructor/docs/integrations/index.md

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LLM Provider Integration Tutorials - Instructor	Complete tutorials for integrating Instructor with 15+ LLM providers. Learn structured data extraction with OpenAI, Anthropic Claude, Google Gemini, local models with Ollama, and more.

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.

• :material-cloud: Major Cloud Providers

  Leading AI providers with comprehensive features

  :octicons-arrow-right-16: OpenAI · :octicons-arrow-right-16: OpenAI Responses · :octicons-arrow-right-16: Azure · :octicons-arrow-right-16: Anthropic · :octicons-arrow-right-16: Google.GenerativeAI · :octicons-arrow-right-16: Vertex AI · :octicons-arrow-right-16: AWS Bedrock · :octicons-arrow-right-16: Google.GenAI · :octicons-arrow-right-16: xAI

• :material-cloud-outline: Additional Cloud Providers

  Other commercial AI providers with specialized offerings

  :octicons-arrow-right-16: Cohere · :octicons-arrow-right-16: Mistral · :octicons-arrow-right-16: DeepSeek · :octicons-arrow-right-16: Together AI · :octicons-arrow-right-16: Groq · :octicons-arrow-right-16: Fireworks · :octicons-arrow-right-16: Cerebras · :octicons-arrow-right-16: Writer · :octicons-arrow-right-16: Perplexity :octicons-arrow-right-16: SambaNova

• :material-open-source-initiative: Open Source

  Run open-source models locally or in the cloud

  :octicons-arrow-right-16: Ollama · :octicons-arrow-right-16: llama-cpp-python

• :material-router-wireless: Routing

  Unified interfaces for multiple providers

  :octicons-arrow-right-16: LiteLLM :octicons-arrow-right-16: OpenRouter

Common Features

All integrations support these core features:

Feature	Description	Documentation
Model Patching	Enhance provider clients with structured output capabilities	Patching
Response Models	Define expected response schema with Pydantic	Models
Validation	Ensure responses match your schema definition	Validation
Streaming	Stream partial or iterative responses	Partial, Iterable
Hooks	Add callbacks for monitoring and debugging	Hooks

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:

Mode	Description	Providers
TOOLS	Uses OpenAI-style tools/function calling	OpenAI, Anthropic, Mistral
PARALLEL_TOOLS	Multiple simultaneous tool calls	OpenAI
JSON	Direct JSON response generation	OpenAI, Gemini, Cohere, GenAI
MD_JSON	JSON embedded in markdown	Most providers

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