Giskard Oss
🐢 Open-Source Evaluation & Testing library for LLM Agents
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What is Giskard Oss?
Giskard OSS (Open Source) is a free Python library for testing and evaluating AI applications, specifically designed for LLM-based systems, RAG applications, and AI agents. It provides developers with automated tools to detect security vulnerabilities using LLM Scan and business logic failures using RAG Evaluation Toolkit (RAGET), ensuring quality and reliability in AI applications before deployment.
Key features include automated detection of security vulnerabilities such as hallucinations, harmful content, prompt injection, data leakage, and stereotypes. The library supports custom tests, local evaluations without external dependencies, and integrates with CI/CD pipelines for automated testing on every commit. It is compatible with major ML ecosystem tools including Hugging Face, MLFlow, Weights & Biases, PyTorch, TensorFlow, LangChain, OpenAI, Mistral, and Ollama.
Giskard OSS is designed for individual developers, data scientists, researchers, and teams just starting with AI testing. It is ideal for prototyping and research projects, projects with budget constraints, and developers who want local control over their data and models without external dependencies or data sharing. The library formed the basis for the Red Teaming LLM Applications course on DeepLearning.AI.
Giskard Oss pricing
Pricing model: Freemium
Giskard Open Source is completely free with no cost. It provides full access to basic testing capabilities including LLM Scan for security vulnerability detection and RAGET for business failure detection. The free tier includes local evaluations, custom tests, CI/CD integration, and community support via Discord. There are no paid plans for the Open Source version - upgrading to Giskard Hub (enterprise platform) requires contacting the team for enterprise pricing based on needs, which includes unlimited models & versions, state-of-the-art vulnerability detection, team collaboration features, SSO, SOC 2 compliance, and priority support with SLAs.
Giskard Oss pros
- Completely free open-source Python library with no cost
- Automated security vulnerability detection with LLM Scan
- Automated business logic failure detection with RAGET
- Detects hallucinations, prompt injection, and data leakage
- Supports custom tests with full flexibility
- Local evaluations keep data private and on-premise
- No external dependencies or data sharing required
- Seamless CI/CD pipeline integration for automated testing
- Compatible with Hugging Face, LangChain, PyTorch, TensorFlow
- Works with OpenAI, Mistral, Ollama, and local custom models
- Active community support via Discord
- Open-source contributions and transparency
- Basis for DeepLearning.AI Red Teaming course
- Quick setup with pip installation
- Programmatic interface for technical users
Giskard Oss cons
- Only basic coverage for security vulnerability detection
- Only basic coverage for business failure detection
- No continuous red teaming capability
- No tool or function calling tests
- Single user only - no multi-user access
- No role-based access control
- No centralized project management
- No team-wide dataset sharing
- No scheduled evaluation runs
- No alerting when issues are detected
- No evaluation comparison dashboard
- No SSO or 2FA support
- No audit trails for compliance
- Not SOC 2 certified
- Community support only - no dedicated SLAs
Frequently asked questions about Giskard Oss
What is Giskard Open Source?
Giskard Open Source is a free Python library for LLM testing and evaluation. It provides automated detection of security vulnerabilities using LLM Scan and business logic failures using RAG Evaluation Toolkit (RAGET) for LLM-based systems, RAG applications, and AI agents.
What vulnerabilities does Giskard detect?
Giskard detects hallucinations, harmful content, prompt injection, data leakage, stereotypes, biases, security holes, non-factual output, misinformation, and toxic content generation in LLM applications.
Is Giskard Open Source really free?
Yes, Giskard Open Source is completely free with no cost. It provides full access to basic testing capabilities, local control over data and models, no external dependencies, and community support.
What models are compatible with Giskard?
Giskard supports OpenAI, Mistral, Ollama, local LLMs, custom models, and integrates with Hugging Face, MLFlow, Weights & Biases, PyTorch, TensorFlow, and LangChain.
Can I use Giskard in CI/CD pipelines?
Yes, Giskard Open Source provides API for CI/CD integration, allowing you to run tests or scans on every commit to your repository to ensure new models don't introduce vulnerabilities.
What is the difference between Open Source and Hub?
Open Source is a free Python library for individual developers with basic testing and local evaluations. Hub is an enterprise platform with state-of-the-art detection, continuous red teaming, team collaboration, SSO, SOC 2 compliance, and priority support.
Does Giskard support team collaboration?
No, Giskard Open Source is single user only with no multi-user access, no role-based access control, and no team-wide dataset sharing. Team collaboration features are available only in Giskard Hub.
How do I install Giskard?
Install Giskard via pip using the pip install command. After installation, import the necessary modules into your Python script, initialize the LLM client, wrap your model, and run the LLM scan.
Can I create custom tests in Giskard?
Yes, Giskard Open Source provides full support for custom tests, allowing you to create your own testing logic and validation rules for your specific use cases.
Where can I get support for Giskard Open Source?
Giskard Open Source has community support through Discord for discussions and help. There are no dedicated support SLAs for the open-source version - enterprise-grade support is available only with Giskard Hub.