pytest-llm-assert 0.2.4

Simple LLM-powered assertions for any pytest test

pytest-llm-assert

Natural language assertions for pytest.

Testing a text-to-SQL agent? Validating LLM-generated code? Checking if error messages are helpful? Now you can:

def test_sql_agent_output(llm):
    sql = my_agent.generate("Get names of users over 21")
    
    assert llm(sql, "Is this a valid SQL query that selects user names filtered by age > 21?")

The LLM evaluates your criterion and returns pass/fail — no regex, no parsing, no exact string matching.

Features

• Semantic assertions — Assert meaning, not exact strings
• Multiple LLM providers — OpenAI, Azure, Anthropic, Gemini, Groq via Pydantic AI
• pytest native — Works as a standard pytest plugin/fixture
• Response introspection — Access tokens and reasoning via llm.response
• Type-safe — Built with Pydantic for structured outputs

Installation

pip install pytest-llm-assert

Quick Start

# conftest.py
import pytest
from pytest_llm_assert import LLMAssert

@pytest.fixture
def llm():
    return LLMAssert(model="openai:gpt-4o-mini")

# test_my_agent.py
def test_generated_sql_is_correct(llm):
    sql = "SELECT name FROM users WHERE age > 21 ORDER BY name"
    assert llm(sql, "Is this a valid SELECT query that returns names of users over 21?")

def test_error_message_is_helpful(llm):
    error = "ValidationError: 'port' must be an integer, got 'abc'"
    assert llm(error, "Does this explain what went wrong and how to fix it?")

def test_summary_captures_key_points(llm):
    summary = generate_summary(document)
    assert llm(summary, "Does this mention the contract duration and parties involved?")

Setup

Works out of the box with cloud identity — no API keys to manage:

# Azure (Entra ID)
export AZURE_API_BASE=https://your-resource.openai.azure.com
az login

# Google Cloud (Vertex AI)
gcloud auth application-default login

# AWS (Bedrock)
aws configure  # Uses IAM credentials

Supports multiple providers via Pydantic AI — including API key auth for OpenAI, Anthropic, and more.

Documentation

• Documentation — Full documentation with examples
• Configuration — All providers, CLI options, environment variables
• API Reference — Full API documentation
• Comparing Judge Models — Evaluate which LLM works best for your assertions
• Examples — Working pytest examples
• Integration Tests — Running tests with real LLM APIs

Related

• pytest-aitest — Full framework for testing MCP servers, CLIs, and AI agents
• Contributing — Development setup and guidelines

Requirements

• Python 3.11+
• pytest 9.0+
• An LLM (OpenAI, Azure, Anthropic, etc.) or local Ollama

Security

• Sensitive data: Test content is sent to LLM providers — consider data policies

License

MIT