giskard-oss Agent工作流

Install

pip install giskard           # checks (+ agents, llm, core)
pip install "giskard[scan]"   # + vulnerability / quality scan
pip install "giskard[openai]" # provider SDK for LLM judges / generators

Requires Python 3.12+.

Telemetry: optional aggregated analytics via giskard-core. No prompts or outputs are sent. Opt out with export DO_NOT_TRACK=1 or export GISKARD_TELEMETRY_DISABLED=1 (or the same keys in a .env file in the working directory). Set them before import to skip creating ~/.giskard/id; setting them later still stops further sends. Details: giskard-core README.

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Giskard is an open-source Python library for testing and evaluating agentic systems. The v3 architecture is a modular set of focused packages — each carrying only the dependencies it needs — built from scratch to wrap anything: an LLM, a black-box agent, or a multi-step pipeline.

These build on three foundational libraries — giskard-core (shared utilities & telemetry), giskard-llm (provider-agnostic LLM routing), and giskard-agents (agent & workflow orchestration) — which are pulled in automatically and rarely used directly.

Quickstart

import asyncio
from giskard.checks import Scenario, Groundedness


def get_answer(inputs: str) -> str:
    return "Paris"  # replace with your model / agent


async def main() -> None:
    scenario = (
        Scenario("test_france_capital")
        .interact(inputs="What is the capital of France?", outputs=get_answer)
        .check(
            Groundedness(
                name="answer is grounded",
                context="France is in Western Europe. Its capital is Paris.",
            )
        )
    )
    result = await scenario.run()
    result.print_report()


asyncio.run(main())

Groundedness is an LLM judge — install a provider extra (e.g. pip install "giskard[openai]") and set the matching API key. Default model: openai/gpt-4o-mini.

See the full docs for Suites, LLMJudge, multi-turn scenarios, and more.

---

Quickstart

import asyncio
from giskard.scan import vulnerability_scan


async def my_agent(inputs: str) -> str:
    # Replace with your agent / model call
    return f"Echo: {inputs}"


async def main() -> None:
    await vulnerability_scan(
        target=my_agent,
        description="A customer support chatbot for an e-commerce platform.",
        languages=["en"],
    )


asyncio.run(main())

Scan generators also need an LLM provider extra and API key (same as Checks judges above).