Install Giskard Checks

Install the Python package

Giskard requires Python 3.12 or higher. Install it together with the SDK for the LLM provider you will use as a judge: an LLM the library calls to grade your agent’s replies.

pip install "giskard[openai]"

No provider SDK ships with giskard itself, so installing it without a provider extra leaves LLM-based checks unable to reach a model.

Pick the extra that matches your provider:

Provider prefix	Install	SDK
`openai/`	`pip install "giskard[openai]"`	`openai`
`google/` or `gemini/`	`pip install "giskard[google]"`	`google-genai`
`anthropic/`	`pip install "giskard[anthropic]"`	`anthropic`
`azure/`	`pip install "giskard[azure]"`	`openai`
`azure_ai/`	`pip install "giskard[azure]"`	`openai`

Use pip install "giskard[all-llms]" for all the native SDKs at once.

For unsupported providers, install pip install "giskard[litellm]" and pass LiteLLMGenerator explicitly:

from giskard.agents.generators import LiteLLMGenerator

llm_judge = LiteLLMGenerator(model="bedrock/anthropic.claude-3-sonnet")

The default Generator does not use LiteLLM.

Paste the following into your coding agent:

Follow the instructions from https://docs.giskard.ai/oss/checks/installation.md and install Giskard in my project.

For reusable workflows that generate scenarios and evaluation suites, see Giskard Agent Skills.

Configure the default LLM judge model

A judge is an LLM that reads your agent’s reply and decides whether it satisfies a rule you wrote in plain language. Some checks need one (LLMJudge, Groundedness, Conformity). To use them, configure a provider SDK. The default Generator uses Giskard’s native provider SDK integrations; install the matching giskard extra, such as openai above. LiteLLM is optional through giskard[litellm].

When a judge runs, its prompt includes the test inputs and agent outputs. Those values are sent to the configured LLM provider, so use a provider and model that meet your data-handling requirements. A weak judge model can produce unreliable verdicts.

For OpenAI, set the OPENAI_API_KEY environment variable:

export OPENAI_API_KEY="your-api-key"

Keep these in a .env file rather than your shell profile. To load them in Python, install python-dotenv:

pip install python-dotenv

from dotenv import load_dotenv

load_dotenv()  # loads .env from the current directory

Then you can set your preferred LLM judge model like this:

from giskard.checks import set_default_generator

# The provider prefix picks the SDK: openai/, google/, anthropic/, azure/, azure_ai/
set_default_generator("openai/gpt-5-mini")

A model identifier string is wrapped in Generator automatically. Pass a Generator instance when you need further configuration. Use a capable judge model and review failures before acting on them.

Next steps

For a step-by-step lesson with no API key, try Your First Test first. Or head to the Quickstart for a single example.