llm-output-guard

A production-ready Python package that validates LLM outputs against schemas, with automatic JSON extraction and retry logic.

Features

• Schema-agnostic — works with Pydantic v2/v1, JSON Schema, and plain Python dicts
• Automatic JSON extraction — strips markdown fences and prose wrappers from LLM responses
• Configurable retry — fixed, exponential, or linear back-off with jitter
• Zero hard dependencies — the core works with nothing installed; extras add integrations
• Integrations — OpenAI SDK, LangChain, FastAPI, and local models via Ollama
• CLI — validate JSON files against schemas from the terminal
• Fully typed — py.typed marker, complete mypy --strict coverage

The Problem It Solves

Getting structured data out of an LLM reliably requires a surprising amount of boilerplate — JSON extraction, schema validation, retry logic with error feedback, and prompt engineering. Here's what that looks like today versus with this package.

import json
import re
import time
from typing import Any, Dict, Optional


class LLMJobValidator:
    def __init__(self, model="gpt-4"):
        self.model = model
        self.max_retries = 3

    def generate_job(self, description: str) -> Optional[Dict]:
        prompt = self._build_prompt(description)

        for attempt in range(self.max_retries):
            response = self._call_llm(prompt)

            data = self._extract_json(response)
            if not data:
                continue

            errors = self._validate_job_data(data)
            if not errors:
                return data

            prompt = self._build_retry_prompt(prompt, errors, response)
            time.sleep(1 * (attempt + 1))

        return None

    def _build_prompt(self, description):
        return f"""
        Create a job posting for: {description}

        Output must be JSON with:
        - title (string): Job title
        - salary (integer): Annual salary in USD
        - skills (array of strings): Required skills
        - is_remote (boolean): Whether remote work is possible

        Example:
        {{
            "title": "Software Engineer",
            "salary": 100000,
            "skills": ["Python", "SQL"],
            "is_remote": true
        }}
        """

    def _extract_json(self, text):
        try:
            return json.loads(text)
        except Exception:
            pass

        match = re.search(r'```(?:json)?\s*([\s\S]*?)\s*```', text)
        if match:
            try:
                return json.loads(match.group(1))
            except Exception:
                pass

        match = re.search(r'\{[^{}]*(?:\{[^{}]*\}[^{}]*)*\}', text)
        if match:
            try:
                return json.loads(match.group(0))
            except Exception:
                pass

        return None

    def _validate_job_data(self, data):
        errors = {}

        if 'title' not in data:
            errors['title'] = 'Missing title'
        elif not isinstance(data['title'], str):
            errors['title'] = 'Title must be string'

        if 'salary' not in data:
            errors['salary'] = 'Missing salary'
        elif not isinstance(data['salary'], (int, float)):
            errors['salary'] = 'Salary must be number'
        elif data['salary'] <= 0:
            errors['salary'] = 'Salary must be positive'

        if 'skills' not in data:
            errors['skills'] = 'Missing skills'
        elif not isinstance(data['skills'], list):
            errors['skills'] = 'Skills must be array'

        if 'is_remote' not in data:
            errors['is_remote'] = 'Missing is_remote'
        elif not isinstance(data['is_remote'], bool):
            errors['is_remote'] = 'is_remote must be boolean'

        return errors

    def _build_retry_prompt(self, original_prompt, errors, bad_response):
        error_text = "\n".join([f"- {k}: {v}" for k, v in errors.items()])
        return f"""
        {original_prompt}

        Your previous response had these errors:
        {error_text}

        Bad response: {bad_response}

        Please fix these errors and provide valid JSON.
        """

    def _call_llm(self, prompt):
        # your real API call here
        ...

After — just the schema:

from pydantic import BaseModel, Field
from llm_output_guard.integrations.openai import GuardedOpenAI


class JobPost(BaseModel):
    title: str = Field(description="Job title")
    salary: int = Field(description="Annual salary in USD", gt=0)
    skills: list[str] = Field(description="Required skills")
    is_remote: bool = Field(description="Whether remote work is possible")


# That's ALL you write. Everything else is handled automatically:
# JSON extraction · schema validation · retry with error feedback · prompt engineering
guard = GuardedOpenAI(schema=JobPost, model="gpt-4o-mini", max_retries=3)

result = guard.guard("Create a software engineer job posting.")
if result.success:
    job: JobPost = result.data          # fully typed Pydantic instance
    print(f"{job.title} — ${job.salary:,} — remote: {job.is_remote}")

Installation

# Core (no dependencies)
pip install llm-output-guard

# With Pydantic v2
pip install "llm-output-guard[pydantic]"

# With JSON Schema validation
pip install "llm-output-guard[jsonschema]"

# With OpenAI integration
pip install "llm-output-guard[openai]"

# With LangChain integration
pip install "llm-output-guard[langchain]"

# With Ollama integration (local models)
pip install "llm-output-guard[ollama]"

# Everything
pip install "llm-output-guard[all]"

Quick Start

JSON Schema

from llm_output_guard import Validator

schema = {
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "age":  {"type": "integer"},
    },
    "required": ["name", "age"],
}

def my_llm(prompt: str) -> str:
    return '{"name": "Alice", "age": 30}'

validator = Validator(schema=schema, llm_callable=my_llm, max_retries=2)
result = validator.guard("Who is Alice?")

print(result.success)   # True
print(result.data)      # {'name': 'Alice', 'age': 30}
print(result.attempts)  # 1

Pydantic Model

from pydantic import BaseModel
from llm_output_guard import Validator

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

validator = Validator(schema=Person, llm_callable=my_llm)
result = validator.guard("Describe Alice.")

person: Person = result.data   # fully typed Pydantic instance
print(person.name)             # Alice

Validate an Existing String

result = Validator(schema=Person).validate_output('{"name": "Bob", "age": 25}')
print(result.success)  # True

Raise on Failure

from llm_output_guard.core.exceptions import MaxRetriesExceededError

validator = Validator(schema=schema, llm_callable=my_llm, raise_on_failure=True)
try:
    result = validator.guard("…")
except MaxRetriesExceededError as e:
    print(f"Failed after {e.attempts} attempts.")

Integrations

OpenAI

from llm_output_guard.integrations.openai import GuardedOpenAI

guard = GuardedOpenAI(schema=Person, model="gpt-4o-mini", max_retries=3)
result = guard.guard("Tell me about Alice.")

LangChain

from langchain_openai import ChatOpenAI
from llm_output_guard.integrations.langchain import GuardedLLM

guarded = GuardedLLM(llm=ChatOpenAI(), schema=Person, max_retries=3)
result = guarded.invoke("Tell me about Alice.")

Ollama (Local Models)

from llm_output_guard.integrations.ollama import GuardedOllama

# Basic usage
guard = GuardedOllama(schema=Person, model="mistral", max_retries=3)
result = guard.guard("Tell me about Alice.")

# With custom configuration
guard = GuardedOllama(
    schema=Person,
    model="mistral",
    base_url="http://localhost:11434",  # default
    temperature=0.7,
    max_tokens=500,
)
result = guard.guard("Tell me about Alice.")

Environment variable configuration:

export OLLAMA_BASE_URL=http://localhost:11434  # Optional, defaults to localhost:11434
export OLLAMA_MODEL=mistral                     # Optional, must override in code if not set

Requirements:

• Ollama must be running locally
• A model must be available (e.g., ollama pull mistral)

FastAPI

from fastapi import FastAPI
from llm_output_guard import Validator

app = FastAPI()
validator = Validator(schema=Person, llm_callable=my_llm)

@app.post("/person")
async def get_person(prompt: str):
    result = validator.guard(prompt)
    result.raise_for_status()
    return result.data

CLI

# Validate a JSON file against a JSON Schema file
llm-guard validate output.json schema.json

# Print the JSON Schema of a Pydantic model
llm-guard schema mypackage.models.Person

GuardResult

Every .guard() or .validate_output() call returns a GuardResult:

Retry Strategies

Validator(schema=schema, llm_callable=llm, retry_strategy="exponential", retry_delay=1.0)

Development

git clone https://github.com/Ujjwal-Bajpayee/llm-output-guard
cd llm-output-guard
pip install -e ".[dev]"
pytest --cov=llm_output_guard

Contributing

See CONTRIBUTING.md.

License

MIT