Giskard vs OpenCompass

Giskard provides tools for assessing AI agents before production, with coverage for prompt injection, data disclosure, inappropriate content, hallucinations, contradictions, and omissions. Its Hub performs black-box testing of conversational text-to-text agents available through an API endpoint. An assessment produces a structured report with a deploy-or-fix verdict, ranked vulnerabilities, and functional scenarios; passing agents receive a Giskard Label. Giskard describes a remediation process in which its team qualifies findings, discusses severity, prioritizes issues, opens workflow tickets, and reruns tests to check fixes. Enterprise evaluation options include adversarial probes, domain-specific datasets, RAG quality metrics, customizable metrics, and human review interfaces. Deployment choices include on-premise, private cloud, and SaaS. Giskard lists EU or US data residency, access controls, audit trails, identity-provider integration, encryption, and a zero-training policy, alongside GDPR, SOC 2 Type II, and HIPAA compliance. The free plan includes a local open-source library, basic vulnerability scanning and RAG evaluation, community support, and best-effort maintenance.

OpenCompass is a free, open-source Python platform for evaluating large language models across knowledge, reasoning, coding, science, language, long-context and safety datasets. It describes support for models from OpenAI, Anthropic, Gemini, Qwen, GLM, DeepSeek and other providers, along with more than 100 datasets. Evaluations can use zero-shot, few-shot or chain-of-thought methods with standard or dialogue prompt templates. The system supports task division, distributed evaluation and data-parallel work across multiple GPUs. Configuration files record experiments, and results can be reported in real time. Its modular design can be extended with models, datasets, task-division strategies and cluster-management systems. OpenCompass evaluates API models as well as open-source models; installation documentation lists LMDeploy and vLLM inference backends and an API option. A VLMEvalKit integration supports multimodal dataset loading, OpenAI-compatible inference and VLMEvalKit metrics. It is intended for self-hosted use under the Apache-2.0 license. The README recommends Python 3.12, while pyext-backed code-evaluation datasets require Python 3.10. Some datasets and agent evaluations need extra dependencies that may conflict.