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davidberenstein1957 merged 2 commits into from
May 13, 2026
+14 −27
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fix(docs): correct typos and improve formatting in README.md #2448

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fix(docs): correct typos and improve formatting in README.md
henchaves May 12, 2026
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Merge branch 'main' into chore/update-readme
davidberenstein1957 May 13, 2026
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41 changes: 14 additions & 27 deletions README.md
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Expand Up @@ -6,13 +6,13 @@
<h3 align="center" weight='300' >Modular, Lightweight, Dynamic and Async-first </h3>
<div align="center">

[![GitHub release](https://img.shields.io/github/v/release/Giskard-AI/giskard)](https://github.com/Giskard-AI/giskard/releases)
[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://github.com/Giskard-AI/giskard/blob/main/LICENSE)
[![Downloads](https://static.pepy.tech/badge/giskard/month)](https://pepy.tech/project/giskard)
[![CI](https://github.com/Giskard-AI/giskard-oss/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/Giskard-AI/giskard-oss/actions/workflows/ci.yml/badge.svg?branch=main)
[![Giskard on Discord](https://img.shields.io/discord/939190303397666868?label=Discord)](https://gisk.ar/discord)
[![GitHub release](https://img.shields.io/github/v/release/Giskard-AI/giskard)](https://github.com/Giskard-AI/giskard/releases)
[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://github.com/Giskard-AI/giskard/blob/main/LICENSE)
[![Downloads](https://static.pepy.tech/badge/giskard/month)](https://pepy.tech/project/giskard)
[![CI](https://github.com/Giskard-AI/giskard-oss/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/Giskard-AI/giskard-oss/actions/workflows/ci.yml/badge.svg?branch=main)
[![Giskard on Discord](https://img.shields.io/discord/939190303397666868?label=Discord)](https://gisk.ar/discord)

<a rel="me" href="https://fosstodon.org/@Giskard"></a>
<a rel="me" href="https://fosstodon.org/@Giskard"></a>

</div>
<h3 align="center">
Expand All @@ -25,7 +25,7 @@
> [!IMPORTANT]
> **Giskard v3** is a fresh rewrite designed for dynamic, multi-turn testing of AI agents. This release drops heavy dependencies for better efficiency while introducing a more powerful AI vulnerability scanner and enhanced RAG evaluation capabilities. For now, the vulnerability scanner and RAG evaluation still rely on Giskard v2.
> **Giskard v2 remains available but is no longer actively maintained.**
> Follow progress → [Read the v3 Annoucement](https://github.com/orgs/Giskard-AI/discussions/2250) · [Roadmap](https://github.com/Giskard-AI/giskard-oss/issues/2252)
> Follow progress → [Read the v3 Announcement](https://github.com/orgs/Giskard-AI/discussions/2250) · [Roadmap](https://github.com/Giskard-AI/giskard-oss/issues/2252)

## Install

Expand All @@ -37,15 +37,15 @@ Requires Python 3.12+.

**Telemetry:** Libraries built on `giskard-core` (including `giskard-checks`) may send **optional, aggregated usage analytics** to help improve the product. No prompts, model outputs, or scenario text are included. See [what is collected and how to opt out](libs/giskard-core/README.md#telemetry).

______________________________________________________________________
---

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.

| Status | Package | Description |
|--------|---------|-------------|
| ✅ Alpha | `giskard-checks` | Testing & evaluation — scenario API, built-in checks, LLM-as-judge |
| 🚧 In progress | `giskard-scan` | Agent vulnerability scanner — red teaming, prompt injection, data leakage (successor of [v2 Scan](https://legacy-docs.giskard.ai/en/stable/open_source/scan/index.html)) |
| 📋 Planned | `giskard-rag` | RAG evaluation & synthetic data generation (successor of [v2 RAGET](https://legacy-docs.giskard.ai/en/stable/open_source/testset_generation/index.html)) |
| Status | Package | Description |
| -------------- | ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| ✅ Beta | `giskard-checks` | Testing & evaluation — scenario API, built-in checks, LLM-as-judge |
| 🚧 In progress | `giskard-scan` | Agent vulnerability scanner — red teaming, prompt injection, data leakage (successor of [v2 Scan](https://legacy-docs.giskard.ai/en/stable/open_source/scan/index.html)) |
| 📋 Planned | `giskard-rag` | RAG evaluation & synthetic data generation (successor of [v2 RAGET](https://legacy-docs.giskard.ai/en/stable/open_source/testset_generation/index.html)) |

## Giskard Checks — create and apply evals for testing agents

Expand All @@ -56,6 +56,7 @@ pip install giskard-checks
**[Giskard Checks](https://docs.giskard.ai/oss/checks)** is a lightweight library for creating evaluations (evals) that test LLM-based systems — from simple assertions to LLM-as-judge assessments. Unlike traditional unit tests, evals are designed for **non-deterministic outputs** where the same input can produce different valid responses.

Use Giskard Checks to:

- **Catch regressions** — verify your system still behaves correctly after changes
- **Validate RAG quality** — check if answers are grounded in retrieved context
- **Enforce safety rules** — ensure outputs conform to your content policies
Expand Down Expand Up @@ -87,7 +88,6 @@ scenario = (
.check(
Groundedness(
name="answer is grounded",
answer_key="trace.last.outputs",
context="France is a country in Western Europe. Its capital is Paris.",
)
)
Expand All @@ -99,7 +99,6 @@ result.print_report()

> The `run()` method is async. In a script, wrap it with `asyncio.run()`. See the [full docs](https://docs.giskard.ai/oss/checks) for `Suites`, `LLMJudge`, multi-turn scenarios, and more.


## Looking for Giskard v2?

Giskard v2 included **Scan** (automatic vulnerability detection) and **RAGET** (RAG evaluation test set generation) for both ML models and LLM applications. These features are not available in v3.
Expand Down Expand Up @@ -173,15 +172,3 @@ Follow the progress and share feedback:
🌟 [Leave us a star](https://github.com/Giskard-AI/giskard), it helps the project to get discovered by others and keeps us motivated to build awesome open-source tools! 🌟

❤️ If you find our work useful, please consider [sponsoring us](https://github.com/sponsors/Giskard-AI) on GitHub. With a monthly sponsoring, you can get a sponsor badge, display your company in this readme, and get your bug reports prioritized. We also offer one-time sponsoring if you want us to get involved in a consulting project, run a workshop, or give a talk at your company.

<h2 id="sponsors">💚 Current sponsors</h2>

We thank the following companies which are sponsoring our project with monthly donations:

**[Lunary](https://lunary.ai/)**

<img src="https://lunary.ai/logo-blue-bg.svg" alt="Lunary logo" width="100"/>

**[Biolevate](https://www.biolevate.com/)**

<img src="https://awsmp-logos.s3.amazonaws.com/seller-wgamx5z6umune/2d10badd2ccac49699096ea7fb986b98.png" alt="Biolevate logo" width="400"/>
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