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A minimal guide to the one function you can call directly to get Pydantic-validated structured output from an LLM. Before you start:
  • Complete Quickstart to understand basic operations
  • Ensure you have LLM Providers configured
  • Have some text to process

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What It Is

  • Single entrypoint: LLMGateway.acreate_structured_output(text, system_prompt, response_model)
  • Returns an instance of your Pydantic response_model filled by the LLM
  • Backend-agnostic: uses BAML or LiteLLM+Instructor under the hood based on config — your code doesn’t change
This function is used by default during cognify via the extractor. The backend switch lives in cognee/infrastructure/llm/LLMGateway.py.

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Code in Action

import asyncio

from pydantic import BaseModel
from typing import List
from cognee.infrastructure.llm.LLMGateway import LLMGateway

class MiniEntity(BaseModel):
    name: str
    type: str

class MiniGraph(BaseModel):
    nodes: List[MiniEntity]

async def main():

    system_prompt = (
        "Extract entities as nodes with name and type. "
        "Use concise, literal values present in the text."
    )

    text = "Apple develops iPhone; Audi produces the R8."

    result = await LLMGateway.acreate_structured_output(text, system_prompt, MiniGraph)
    print(result)
    # MiniGraph(nodes=[MiniEntity(name='Apple', type='Organization'), ...])

if __name__ == "__main__":
    asyncio.run(main())
This simple example uses a basic schema for demonstration. In practice, you can define complex Pydantic models with nested structures, validation rules, and custom types.

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What Just Happened

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Step 1: Define Your Schema

class MiniEntity(BaseModel):
    name: str
    type: str

class MiniGraph(BaseModel):
    nodes: List[MiniEntity]
Create Pydantic models that define the structure you want the LLM to return. The LLM will fill these models with data extracted from your text.

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Step 2: Write a System Prompt

system_prompt = (
    "Extract entities as nodes with name and type. "
    "Use concise, literal values present in the text."
)
Write a clear prompt that tells the LLM what to extract and how to structure it. Short, explicit prompts work best.

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Step 3: Call the LLM

result = await LLMGateway.acreate_structured_output(text, system_prompt, MiniGraph)
This calls the LLM with your text and prompt, returning a Pydantic model instance with the extracted data.
A sync variant exists: LLMGateway.create_structured_output(...).

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Custom Tasks

This function is often used when creating custom tasks for processing data with structured output. You’ll see it in action when we cover custom task creation in a future guide.

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Backend Doesn’t Matter

The config decides the engine:
  • STRUCTURED_OUTPUT_FRAMEWORK=instructor → LiteLLM + Instructor
  • STRUCTURED_OUTPUT_FRAMEWORK=baml → BAML client/registry
Both paths return the same Pydantic model instance to your code.

Structured Output

Learn about structured output frameworks

Custom Prompts

Control extraction with custom prompts

API Reference

Explore API endpoints