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Structured Outputs and Tool Use with AI APIs — Python (Beginner Guide as of 24 Jul 2026)

Grading note. A dated snapshot — accurate as of 24 Jul 2026, frozen here and kept as a permanent archive entry. Research-drafted by a pupil, graded by the 3-lens panel + sensei. Corrections applied inline; unverifiable gaps marked ⚠ PENDING (#issue) — never guessed.

What is this, and why does it matter?

When you ask an AI model (like GPT-4o) a question, it answers with plain text. That is fine for reading, but not for code. If you want your program to pull out a specific value — say, the total on an invoice or the sentiment score of a review — you need the model to return data in a predictable shape that your code can use directly.

That is what “structured output” means: you tell the AI “give me back exactly these fields, in exactly these types,” and a library handles the translation from the model’s text into real Python objects.

This guide covers the tools that make that happen. The recommended starting point is the instructor library, which wraps any LLM API and gives you back validated Python objects automatically. Pydantic (the schema definition library instructor uses) and LangChain (a broader orchestration framework) are also covered. You do not need to use all of them — start with instructor and Pydantic.

How to read the labels

Prerequisites

You need an API key from your LLM provider (for example, OpenAI or Anthropic). An API key is a secret password that lets your code talk to the AI service. Store it in an environment variable — a named value your terminal holds in memory — rather than writing it directly into your code.

To set it in your terminal on Linux or Mac:

Instructor reads these variables automatically when it runs.

⚠️ WARNING: Never paste your API key directly into a Python file, and never commit it to version control (such as Git). If you do, the key will be visible to anyone who can see your code — and they can run up charges on your account without your knowledge.

You also need Python 3.9 or higher. Check your version with python --version in your terminal.

Practice: Use the instructor library to get validated Python objects from any LLM

Do: Install instructor by running this in your terminal:

pip install instructor

Then wrap your LLM client with instructor.from_provider("openai/gpt-4o"). Call client.create(response_model=MyPydanticModel, messages=[...]). The return value is a fully-validated Python object — no JSON parsing needed on your part.

Why (beginner): Without instructor, the LLM returns plain text. You have to parse that text as JSON yourself, deal with malformed output, and check field types by hand — all of which is tedious and error-prone. Instructor does all of that automatically. If the model returns bad output, instructor retries the call and feeds the exact error message back to the model so it can fix it. What you get back is a real Python object with real types, not a raw string.

Caveat / contested: 🕒 verify live — The current latest version is 1.15.4 (released 2026-06-28). The older from_openai() and from_anthropic() provider-specific methods are no longer the primary recommended API; from_provider() is the current interface. Check the instructor changelog for the formal deprecation timeline if you are migrating from older code. Instructor requires Python 3.9 or higher.

⚠️ WARNING: Every retry costs additional API tokens (the units the AI provider charges for). With max_retries=3 and a complex schema, a single bad call can multiply your token usage by 4. Monitor retry rates in production.

Sources:

Confidence: 📄 vendor-documented (multiple instructor official pages, same publisher)

Practice: Define output schemas as Pydantic v2 BaseModel subclasses with Field descriptions

Background: A schema is a description of the shape of your data — which fields exist, what type each field holds, and what it means. Pydantic (pronounced py-dan-tic) is the Python library that lets you define schemas as classes and automatically validates that data matches them.

Do: Install Pydantic v2:

pip install 'pydantic>=2.0'

If you already have Pydantic installed, confirm you have version 2 (not the older version 1, which has a different API):

python -c 'import pydantic; print(pydantic.VERSION)'

The output should start with 2..

Define a class that inherits from pydantic.BaseModel. Give each field a Python type hint (such as str, float, or int) and a Field(description="...") that explains in plain English what the field should contain. When instructor sends this schema to the model, those descriptions travel with the schema and act as per-field instructions to the AI — essentially prompt engineering embedded in your data definition.

from pydantic import BaseModel, Field

class Invoice(BaseModel):
    vendor_name: str = Field(description="Legal name of the company that issued the invoice")
    total_amount: float = Field(description="Total due in USD, as a number without currency symbols")
    due_date: str = Field(description="Payment due date in YYYY-MM-DD format")

Why (beginner): Without descriptions, the model guesses what each field means from its name alone. With descriptions, you give the model precise instructions for every field. Pydantic v2 generates JSON Schema from these definitions automatically via model_json_schema(), which instructor then sends to the LLM.

Caveat / contested: Avoid Python Union types (for example str | int) unless you have a specific reason. Union types produce ambiguous schemas that confuse models and increase the chance of validation failures. Prefer explicit types. Use Optional[str] when a field can be absent (meaning: it might not be there at all), rather than str | int (meaning: it might be one of two completely different types). Keep schema nesting to 2–3 levels maximum — deeper nesting increases error rates.

Sources:

Confidence: ✅ independently-corroborated (Pydantic official docs + Machine Learning Mastery + instructor docs, three different publishers)

Practice: Use LangChain’s .with_structured_output() when already inside a LangChain chain

Background: LangChain is a framework for building applications that chain together multiple AI calls, tools, and steps. If you are not already using LangChain for something else, you do not need it just for structured output — instructor is simpler for that purpose. But if your project is already built on LangChain, use the pattern below.

Do: Install the required packages:

pip install langchain langchain-openai

Chain .with_structured_output(MyPydanticModel) onto any LangChain LLM object. This returns a runnable (a callable object in LangChain’s system) that, when invoked, returns a validated instance of your model.

from langchain_openai import ChatOpenAI
from pydantic import BaseModel

class Summary(BaseModel):
    headline: str
    key_points: list[str]

llm = ChatOpenAI(model="gpt-4o")
structured_llm = llm.with_structured_output(Summary)
result = structured_llm.invoke("Summarise the following article: ...")
# result is a Summary instance

Why (beginner): .with_structured_output() is the standard LangChain pattern for this. It uses the underlying LLM’s tool-calling API (a feature where the model can invoke pre-defined functions) automatically — you do not have to wire up tool schemas yourself. For providers that support native structured output (OpenAI, Anthropic, xAI/Grok, Google Gemini), it uses that natively; for others it falls back to tool calling.

Caveat / contested: This only works with providers that support tool calling or native structured output. If your LLM does not support either, you need an output parser instead. Pydantic validation errors surface only at runtime, not at definition time. Chaining adds abstraction layers that make debugging harder compared to direct API calls. 🕒 verify live — LangChain 1.3.14 (released 2026-07-16) is current; LangChain provider integrations sometimes lag behind new model releases (for example, Fable 5 and Mythos 5 compatibility should be tested before use in production).

Sources:

Confidence: ✅ independently-corroborated (Mirascope independent blog + LangChain official docs, two different publishers)

Practice: Use instructor’s max_retries to automatically repair validation failures

Background: Even with a clear schema, an LLM sometimes returns output that does not match what you asked for — a date in the wrong format, a field with the wrong type, and so on. Instructor can automatically fix this for you by retrying the call.

Do: Pass max_retries=3 (or another small integer) when creating the instructor client. When the model returns output that fails Pydantic validation, instructor automatically sends the exact validation error message back to the model as a follow-up prompt and retries. You write no retry logic yourself.

import instructor
from instructor.exceptions import InstructorRetryException

client = instructor.from_provider("openai/gpt-4o", max_retries=3)

try:
    result = client.create(response_model=MyModel, messages=[...])
except InstructorRetryException as e:
    print(f"All retries failed. Last error: {e.last_completion}")

If all retries run out without a valid response, instructor raises InstructorRetryException. That exception contains the full failure history so you can debug what went wrong.

Why (beginner): LLMs are not perfectly reliable at following schema constraints on every single call. Sending the exact error message (“field ‘due_date’ must be in YYYY-MM-DD format, got ‘07/24/2026’") gives the model precise, natural-language repair instructions — much more effective than just re-sending the original prompt.

Caveat / contested:

⚠️ WARNING: Set a small max_retries value (2–3). A high value multiplies your API costs and slows your application down. If you are seeing many retries, the root cause is usually a schema that needs better Field(description=...) text, not a higher retry count.

You can also layer tenacity (pip install tenacity) retry decorators for exponential backoff on rate-limit errors (retry_if_exception_type(RateLimitError)). This is separate from instructor’s built-in schema-validation retry — the two handle different failure modes and can be combined.

Sources:

Confidence: 📄 vendor-documented (both sources are instructor’s own documentation)

Practice: Extract lists of entities using List[ChildModel] fields, but cap nesting at 2–3 levels

Background: Sometimes you want to extract multiple items of the same type from a document — for example, every line item on an invoice, or every person mentioned in an article. Pydantic and instructor handle this with a list field.

Do: Define a child BaseModel for the repeating item and reference it as List[ChildModel] in the parent model. Instructor and Pydantic handle the list automatically — you do not need to loop over anything yourself.

from pydantic import BaseModel, Field
from typing import List

class LineItem(BaseModel):
    description: str
    quantity: int
    unit_price: float

class Invoice(BaseModel):
    vendor: str
    line_items: List[LineItem] = Field(description="All individual line items on the invoice")

Why (beginner): This is the standard way to extract one-to-many relationships (an invoice with many line items, an article with many named people). Pydantic validates every element in the list. Without this pattern, you would need to parse arrays from raw JSON by hand.

Caveat / contested: Keep nesting at 2–3 levels or fewer as general guidance. A schema like Invoice > Section > LineItem > SubItem (four levels deep) increases validation error rates because the model must track more structure simultaneously. Also, models sometimes populate only the first item in a long list when the document is large — check that the list has the number of entries you expected if completeness matters to you.

Sources:

Confidence: ✅ independently-corroborated (Machine Learning Mastery + instructor docs, two different publishers)

Practice: Use instructor’s streaming mode with create_partial() to show progressive UI updates

Background: By default, instructor waits for the entire model response before returning anything. Streaming lets your application display information to the user as it arrives, token by token.

Do: Use create_partial() to get a generator (a Python object you iterate over with a for loop) of partial model instances. Fields populate incrementally as the model generates tokens. The last value yielded by the generator is the complete, fully-populated model.

⚠️ WARNING: Pydantic field validators do not run on intermediate partial results during streaming — only on the final complete model. Do not treat intermediate values as validated data. If your model uses Literal types (fields that must be one of a fixed set of values), you must import PartialLiteralMixin to avoid parse errors on incomplete tokens.

client = instructor.from_provider("openai/gpt-4o")

for partial_user in client.create_partial(
    response_model=UserProfile,
    messages=[{"role": "user", "content": "Extract: Jane Smith, CEO, 42..."}],
    stream=True
):
    if partial_user.name:
        print(f"Name so far: {partial_user.name}")
# After loop ends, partial_user is the complete model

Why (beginner): For long documents or slow models, streaming dramatically improves how responsive your application feels. The user sees information appearing as it is extracted rather than staring at a blank screen until everything is ready.

Caveat / contested: The older instructor API used from_openai() and from_anthropic() wrappers. The current unified API uses from_provider() with an async_client=True flag for async. If you see tutorials using instructor.from_openai(AsyncOpenAI()), they target an older version. 🕒 verify live.

Sources:

Confidence: 📄 vendor-documented (both sources are instructor’s own documentation)

Practice: Use Marvin for quick single-call extractions when you don’t need retry control

Background: Marvin is a separate Python library that wraps LLM calls with an even simpler API than instructor. It is a good starting point for quick experiments before you need the full power of instructor.

Do: Install marvin:

pip install marvin

Use marvin.extract() to pull typed values from unstructured text in one line. For custom logic, use the @marvin.fn decorator to define a function by its signature and docstring — Marvin generates the implementation at runtime using an LLM.

⚠️ WARNING: Every time you call a @marvin.fn-decorated function, it makes a real API call to the LLM. This costs tokens and takes time. Do not call it inside a tight loop (a loop that runs many times quickly) without caching.

import marvin

# Extract a list of strings from text
locations = marvin.extract("The conference is held in Tokyo, Berlin, and Austin.", target=str)

# Or use @marvin.fn for custom logic
@marvin.fn
def extract_sentiment(text: str) -> float:
    """Return a sentiment score between -1.0 (negative) and 1.0 (positive)."""

Why (beginner): Marvin has a simpler API than instructor for quick tasks where you just want a typed value out of some text and do not need fine-grained control over retries, schema design, or provider selection.

Caveat / contested: Marvin 3.0 switched from its own LLM integration to using PydanticAI under the hood. Version 3.2.7 is the current release (2026-03-04) — this release is approximately 5 months old with no newer release, which may indicate maintenance-mode status; evaluate accordingly. 🕒 verify live. The API changed substantially between v2 and v3; many tutorials target the older API. The @ai_model decorator seen in some tutorials is from an older API; the current primary APIs are marvin.extract(), marvin.cast(), marvin.fn, and marvin.classify() (confirmed on askmarvin.ai 2026-07-24).

Marvin does not provide the same level of schema-level retry and validation feedback loop as instructor. For production batch pipelines, instructor gives more control.

Sources:

Confidence: 📄 vendor-documented (all sources are Marvin’s own documentation; skeptic re-fetch confirmed askmarvin.ai loads and @marvin.fn is documented)

Practice: Test structured output pipelines by mocking the LLM client with pre-recorded responses

Background: Testing code that calls an LLM is tricky because real API calls cost money, are slow, and return slightly different results each time. The solution is mocking — replacing the real API client with a fake one that returns a pre-recorded response you control.

Do: In pytest (Python’s standard testing framework), use unittest.mock.patch to replace the LLM client (or instructor client) with a mock that returns a pre-recorded response fixture. Store realistic response fixtures as JSON files in tests/fixtures/. Assert on what your parsing code does with the response, not on the exact text the LLM produced.

# tests/test_extraction.py
from unittest.mock import patch, MagicMock
import pytest
from my_app import extract_invoice  # your function that calls instructor

def test_invoice_extraction():
    mock_response = Invoice(vendor="Acme Corp", total_amount=1234.56, due_date="2026-08-01")
    with patch("my_app.instructor_client.create", return_value=mock_response):
        result = extract_invoice("Invoice from Acme Corp for $1,234.56 due August 1.")
    assert result.vendor == "Acme Corp"
    assert result.total_amount == 1234.56

Why (beginner): Without mocking, every test run makes real API calls. That costs money, is slow (seconds per test), and is non-deterministic (you might get a different answer each time). Mocked tests run in milliseconds, cost nothing, and always return the same result. They let you verify that your schema parsing, field mapping, and error handling logic works correctly regardless of what the LLM actually produces.

Caveat / contested: Mocks can give false confidence if your pre-recorded fixture does not match the real API response structure. Always run at least one integration test (with a real API call) in CI — your automated test system — gated behind a flag like --run-integration, to catch drift between your mock and the real API.

Sources:

Confidence: ✅ independently-corroborated (two independent third-party publishers)

Practice: Cache LLM responses by (model, prompt hash, schema hash) to avoid redundant API calls in batch pipelines

Background: A cache is a store of results you have already computed. When you run the same LLM call again with the same inputs, you get the stored result instantly instead of making a new API call. This saves money and time.

Do: Install diskcache:

pip install diskcache

Use diskcache (or Redis for distributed workloads) to cache LLM responses. Build your cache key — the identifier for a stored result — from a hash of the model name, prompt text, and Model.model_json_schema(). Store Pydantic model instances serialised with model.model_dump_json() and deserialise with Model.model_validate_json(). Include the schema hash in the key so that the cache automatically invalidates (discards old results) when you change your schema.

⚠️ WARNING: If you change your Pydantic model’s fields without including the schema in the cache key, you will silently get back stale responses that do not match your new schema. Always hash the schema.

import diskcache
import hashlib, json
from my_schema import Invoice

cache = diskcache.Cache("./llm_cache")

def cached_extract(prompt: str, model_name: str) -> Invoice:
    key = hashlib.sha256(
        json.dumps({"model": model_name, "prompt": prompt,
                    "schema": Invoice.model_json_schema()}).encode()
    ).hexdigest()
    if key in cache:
        return Invoice.model_validate_json(cache[key])
    result = call_llm(prompt, model_name)          # your instructor call
    cache[key] = result.model_dump_json()
    return result

Why (beginner): In a batch pipeline extracting structured data from thousands of documents, many documents may be identical or near-identical. Without caching, you pay API costs and incur latency for every document on every run, even if the prompt and schema are unchanged.

Caveat / contested: Disk-based caches (diskcache) store data in plain files on disk in clear text. If your prompts or responses contain personal or confidential data, treat the cache directory with the same security controls as your source data (restrict permissions, do not store in a public cloud bucket without encryption). For distributed pipelines across multiple workers, use Redis or a shared object store instead.

Do not cache responses that must reflect real-time data (for example, news summarisation where freshness matters).

Sources:

Confidence: ✅ independently-corroborated (instructor blog + independent Medium article, two different publishers)

Practice: Use async_client=True with instructor and asyncio.gather() for concurrent batch extractions

Background: By default, Python runs one thing at a time. When you call an LLM API, your code waits (blocking) until it gets a response before starting the next call. For a batch of 100 documents at 1 second per call, that is 100 seconds of waiting.

Asyncio is Python’s built-in system for running multiple tasks at once without waiting — each API call runs “in the background” while others start. asyncio.gather() kicks off all the tasks and waits for all of them to finish. A Semaphore is a counter that limits how many tasks run at the same time, preventing you from sending too many requests at once.

Do: Enable the async client with instructor.from_provider("openai/gpt-4o", async_client=True). Define an async def extraction function and run a batch with asyncio.gather(). Use asyncio.Semaphore to cap concurrency and avoid hitting API rate limits.

⚠️ WARNING: Without a Semaphore, asyncio.gather() on a large batch will fire all requests at once and almost certainly trigger rate-limit errors from the API. If you are on a free or trial API account, even 5 concurrent requests can hit your quota in seconds and generate charges. Start with Semaphore(1) (sequential — effectively no parallelism) to verify the pipeline works correctly end-to-end, then increase the number gradually.

import asyncio
import instructor
from pydantic import BaseModel

client = instructor.from_provider("openai/gpt-4o", async_client=True)

class Summary(BaseModel):
    headline: str

async def extract_one(text: str, sem: asyncio.Semaphore) -> Summary:
    async with sem:
        return await client.create(response_model=Summary,
                                   messages=[{"role": "user", "content": text}])

async def extract_batch(texts: list[str]) -> list[Summary]:
    sem = asyncio.Semaphore(5)   # max 5 concurrent requests; tune to your API tier
    return await asyncio.gather(*[extract_one(t, sem) for t in texts])

Why (beginner): With asyncio.gather(), all calls are in-flight simultaneously within the concurrency cap, and the total time drops close to the latency of a single call. This matters enormously for batch pipelines — the same 100-document job that took 100 seconds can finish in around 1–2 seconds.

Caveat / contested: A value of 5–20 concurrent requests is a reasonable starting point; tune it to stay below your API tier’s requests-per-minute limit. 🕒 verify live — rate limits vary by model and API tier. Use tenacity (pip install tenacity) with retry_if_exception_type(RateLimitError) and exponential backoff for resilient production code.

Sources:

Confidence: 📄 vendor-documented (both sources are instructor’s own documentation)

Held pending fixes (not publish-ready)

CHANGELOG (grading → this entry)

  1. Beginner KILL (prerequisites): Added API key setup section at top of article with storage guidance and security warning.
  2. Beginner KILL (async): Moved ⚠️ WARNING about Semaphore/rate-limits to ABOVE the code block; added free/trial-tier quota caution.
  3. Beginner FIX (Pydantic): Added explicit pip install 'pydantic>=2.0' instruction and version-check command; noted v1 vs v2 behavioral differences.
  4. Beginner FIX (diskcache): Added pip install diskcache instruction and plaintext-on-disk data-sensitivity warning.
  5. Beginner FIX (InstructorRetryException): Added catch/import example with from instructor.exceptions import InstructorRetryException and e.last_completion.
  6. Beginner FIX (Marvin): Added ⚠️ WARNING that every @marvin.fn call makes a real API call (cost + latency).
  7. Skeptic CLEAR (Marvin PENDING): Cleared — askmarvin.ai now loads; @marvin.fn confirmed in v3 by skeptic re-fetch.
  8. Skeptic CLEAR (instructor PENDING): Softened deprecation claim — no formal date found; updated to “no longer primary recommended API.”
  9. Timekeeper FIX (LangChain): Added LangChain 1.3.14 version note and Fable 5/Mythos 5 compatibility caveat.
  10. Timekeeper FIX (Marvin): Added note that v3.2.7 is 5 months old with no newer release, indicating possible maintenance-mode status.
  11. Beginner re-level by rings-beginner-author, 2026-07-24.
  12. Link-check gate (2026-07-25): markaicode.com (403 bot-protection, confirmed live) → unlinked to plain text (1 occurrence). medium.com/@Shamimw (403 bot-protection, confirmed live) → unlinked to plain text (1 occurrence).