Test your AI outputs like you test your code with pytest for LLMs.
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Tracked since2026
0 reviews trackedThe Bottom Line
Entry price
Free, no paid tier
Biggest pro
Leverages familiar pytest framework for easy adoption by Python developers
Biggest con
Requires Python knowledge and familiarity with pytest
TL;DR - LLMTest
- Tests LLM outputs and performance using a pytest-based framework.
- Offers 22+ deterministic assertions for text, performance, and agent behavior without extra LLM calls.
- Supports multiple LLM providers and integrates Pydantic for structured validation.
Pricing: Free forever
Best for: Individuals & startups
What is LLMTest?
LLMTest is an open-source, MIT-licensed Python library designed to facilitate robust testing of Large Language Models (LLMs). It integrates seamlessly with pytest, allowing developers to apply familiar software testing methodologies to their AI outputs. The tool provides a comprehensive set of assertions for text, performance, and agent behavior, enabling validation of LLM responses without relying on LLM judges or complex YAML configurations.
This library is built for developers working with LLMs from providers like OpenAI, Anthropic, and Ollama, as well as DeepSeek, Gemini, and Mistral. It's particularly useful for ensuring the reliability, accuracy, and efficiency of AI agents and their interactions. By offering deterministic and instant assertions, LLMTest helps reduce testing costs and speeds up the development cycle, making it easier to build and deploy high-quality LLM-powered applications.
Available on: Web
Pros & Cons
Pros
- Leverages familiar pytest framework for easy adoption by Python developers
- Reduces testing costs by avoiding LLM judge calls for most assertions
- Provides robust validation for complex agent behaviors
- Open-source and MIT licensed, promoting community contributions and flexibility
- Supports a wide range of popular LLM providers
Cons
- Requires Python knowledge and familiarity with pytest
- Newer tool (v0.1.0) may have evolving features or limited community resources compared to mature tools
Key Features
Pytest integration for LLM testing22+ built-in assertions (text, performance, agent, composable)Multi-provider support (OpenAI, Anthropic, Ollama, DeepSeek, Gemini, Mistral)Zero LLM calls for most assertions (deterministic and instant)Pydantic integration for auto-validation and JSON serializationAgent testing capabilities (tool call validation, loop detection, call ordering)Built-in retry support for non-deterministic outputs
Pricing Plans
Open Source
Free
- MIT licensed
- Pytest for LLMs
- OpenAI, Claude, Ollama, DeepSeek, Gemini, Mistral support
- Basic JSON, Structured, Agent Tools, Reliability, Rerun
- Zero LLM Calls for most assertions
- Built on Pydantic
- 22+ Assertions (Text, performance, agent, composable)
- Multi-Provider support
- Agent Testing
- Retry Support
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LLMTest FAQ
How does LLMTest avoid using an LLM judge for assertions?
LLMTest's assertions are designed to be deterministic, meaning they evaluate the LLM's output directly against predefined criteria (like regex, JSON schema, or specific text content) rather than relying on another LLM to interpret and judge the output. This makes most checks instant and cost-free.
What specific types of agent behaviors can be tested with LLMTest?
LLMTest allows for comprehensive testing of AI agents, including validating that specific tools are called, detecting infinite loops in agent execution, and ensuring the correct ordering of tool calls within an agent's workflow.
Can I combine multiple assertions to create complex test conditions?
Yes, LLMTest supports composable assertions using logical operators like AND (&) and OR (|). This allows developers to create sophisticated test conditions, such as checking for the presence of one phrase AND the absence of another.
How does LLMTest handle the non-deterministic nature of LLM outputs?
The framework includes built-in retry support, which can be applied at both the decorator and fixture levels. This allows tests to re-run an LLM call or assertion multiple times to account for variability in LLM responses and ensure robustness.
What is the role of Pydantic in LLMTest?
Pydantic is integrated into LLMTest to provide auto-validation, JSON serialization, and schema generation for models. This ensures that structured outputs from LLMs conform to expected data types and formats, enhancing the reliability of tests for structured data.
Source: assertllm.dev