Giskard
Automated red-teaming and hallucination testing for LLM agents, with dashboards for non-coders.
What is Giskard?
Giskard is an AI red-teaming and quality platform that automatically generates attack scenarios and hallucination tests for LLM agents, with a Python SDK plus collaborative dashboards for non-engineers. It is bought by enterprises (Michelin, BNP Paribas, Decathlon are cited) that need proactive vulnerability detection before agents reach production. Differentiator: business-stakeholder dashboards let domain experts contribute test cases without writing code.
Tools for building, hosting, testing, observing, connecting, and giving memory or computer access to AI agents.
See the full Agent Infrastructure guide to compare more tools, buyer criteria, and related workflows.
Use cases to evaluate
Red-teaming chatbots for prompt-injection vulnerabilities
Detecting hallucinations and contradictions in RAG outputs
Building regression test suites from discovered failures
Letting domain experts contribute test cases via dashboards
Fit to evaluate
Enterprise AI teams in regulated industries
Security teams red-teaming LLM applications
Product teams that need non-engineer test contributors
ML engineers preferring open-source-first tooling
Business fit
Right for you if you ship customer-facing agents and need to prove you tested for prompt injection, PII leakage, and hallucinations before launch. Skip if you only run internal-tool LLMs where adversarial testing is overkill. The open-source library is genuinely usable solo and free forever. Enterprise pricing requires a sales call, which fits the regulated-industry buyers Giskard targets.
How to evaluate Giskard
Use this category when a business wants agents that do work across tools, APIs, browsers, and data sources.
Confirm the exact workflow
Map Giskard to one concrete workflow first, such as red-teaming chatbots for prompt-injection vulnerabilities. Avoid buying before the owner, trigger, output, and success metric are clear.
Check category fit
Compare tool-calling, memory, browser automation, evals, observability, and deployment controls.
Compare practical alternatives
Shortlist Giskard against Orgo, Browser Use, Browserbase so the decision is based on fit, effort, and workflow ownership rather than brand recognition alone.
Validate cost and rollout effort
Free: $0, open-source Python library for solo LLM experiments. Enterprise: custom pricing (contact sales), production deployment platform, dashboards for cross-team collaboration. No dollar amount published for Enterprise. Also confirm implementation time, support needs, and whether the technical setup matches your team.
Compare Giskard with alternatives
Use this quick comparison before booking demos or moving data into a new system.
| Primary workflow | Red-teaming chatbots for prompt-injection vulnerabilities, Detecting hallucinations and contradictions in RAG outputs |
|---|---|
| Best-fit team | Enterprise AI teams in regulated industries, Security teams red-teaming LLM applications |
| Implementation effort | Technical setup and maintenance profile |
| Pricing check | Free plan + paid plans |
| Closest alternatives | OrgoBrowser UseBrowserbaseHyperbrowser |
Giskard pricing
| Model | Free plan + paid plans |
|---|---|
| Snapshot | Free: $0, open-source Python library for solo LLM experiments. Enterprise: custom pricing (contact sales), production deployment platform, dashboards for cross-team collaboration. No dollar amount published for Enterprise. |
| Checked |
Common questions about Giskard
What is Giskard?
Giskard is an AI red-teaming and quality platform that automatically generates attack scenarios and hallucination tests for LLM agents, with a Python SDK plus collaborative dashboards for non-engineers. It is bought by enterprises (Michelin, BNP Paribas, Decathlon are cited) that need proactive vulnerability detection before agents reach production. Differentiator: business-stakeholder dashboards let domain experts contribute test cases without writing code.
What is Giskard used for?
Common use cases: Red-teaming chatbots for prompt-injection vulnerabilities; Detecting hallucinations and contradictions in RAG outputs; Building regression test suites from discovered failures; Letting domain experts contribute test cases via dashboards.
How much does Giskard cost?
Free: $0, open-source Python library for solo LLM experiments. Enterprise: custom pricing (contact sales), production deployment platform, dashboards for cross-team collaboration. No dollar amount published for Enterprise.
Who is Giskard best for?
Giskard fits Enterprise AI teams in regulated industries, Security teams red-teaming LLM applications, Product teams that need non-engineer test contributors, ML engineers preferring open-source-first tooling. Right for you if you ship customer-facing agents and need to prove you tested for prompt injection, PII leakage, and hallucinations before launch. Skip if you only run internal-tool LLMs where adversarial testing is overkill. The open-source library is genuinely usable solo and free forever. Enterprise pricing requires a sales call, which fits the regulated-industry buyers Giskard targets.
What are alternatives to Giskard?
Common alternatives to Giskard include Orgo, Browser Use, Browserbase, Hyperbrowser, Steel, Anchor Browser.