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Add the Docs MCP to your AI coding tool to get help building with Respan. No API key needed.
{
  "mcpServers": {
    "respan-docs": {
      "url": "https://respan.ai/docs/mcp"
    }
  }
}

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What is Instructor?

Instructor is a Python library for getting structured data (JSON) from LLMs using Pydantic models. It patches OpenAI and other LLM clients to return validated, typed responses. The Respan integration uses respan-tracing auto-instrumentation to capture all LLM calls.

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Setup

1

Install packages

pip install instructor openai respan-tracing
2

Set environment variables

export RESPAN_API_KEY="YOUR_RESPAN_API_KEY"
export OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
3

Initialize and run

from respan_tracing import RespanTelemetry, workflow, task, Instruments
from openai import OpenAI
import instructor
from pydantic import BaseModel

# Initialize Respan — auto-instruments OpenAI calls
telemetry = RespanTelemetry(instruments={Instruments.OPENAI})

# Patch OpenAI with Instructor
client = instructor.from_openai(OpenAI())

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

@task(name="extract_user")
def extract_user(text: str) -> User:
    return client.chat.completions.create(
        model="gpt-4o-mini",
        response_model=User,
        messages=[{"role": "user", "content": f"Extract user info: {text}"}],
    )

@workflow(name="extraction_pipeline")
def extraction_pipeline():
    user = extract_user("John is 30 years old")
    print(f"Name: {user.name}, Age: {user.age}")
    return user

extraction_pipeline()
4

View your trace

Open the Traces page to see your extraction traces.

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Configuration

Instructor uses the respan-tracing SDK. Instruments.OPENAI auto-captures all OpenAI calls made by Instructor. See the Python Tracing SDK reference for configuration options.