Does BotGauge integrate with our existing toolchain?

Yes. BotGauge integrates with 60+ tools, including Jira, Xray, Jenkins, GitHub, CircleCI, and Slack, for end-to-end QA automation across the CI/CD pipeline.

Is BotGauge secure for enterprise use?

Yes. BotGauge is SOC 2 Type II compliant with independently audited security practices, encrypted test runs, data isolation, SSO/SAML sign-in, and fine-grained access control per project and resource.

What kinds of agents can it test?

RAG assistants, customer support agents with multi-turn conversations, voice agents with policy-aware real-time actions, multi-agent systems with coordinated workflows, and transactional agents performing refunds, approvals, and transfers.

BotGauge — Stop agent failures before they ship

BotGauge is an AI agent red-teaming and evaluation platform that helps teams find agent failures before they ship. It runs adaptive, real-world, and adversarial scenarios against agents to uncover unexpected behavior, traces the prompts, responses, tool calls, and execution paths behind every finding, and converts those discoveries into evaluations, policies, and guardrails that run on every subsequent release. The platform is framework- and model-agnostic — supporting OpenAI, Anthropic, and Hugging Face models; LangChain, LlamaIndex, and LangGraph frameworks; and CrewAI, AutoGen, and AutoGPT agent systems — and handles RAG assistants, customer support agents, voice agents, multi-agent systems, and transactional agents. Alongside agent testing, BotGauge offers QA-as-a-service test automation with AI-generated, self-held test cases, and holds a 4.6 rating on G2. It is SOC 2 Type II compliant with SSO/SAML and fine-grained access control.

Key Features

Adaptive red-teaming

Adaptive red-teaming that explores how agents behave under pressure across inputs, context, tools, policies, and multi-turn interactions — starting from a baseline and going deeper via custom strategies for the scenarios you care about.

Agent tracing

Full traces behind every result: inspect prompts, responses, tool calls, context, and execution paths to understand exactly how the agent reached a behavior.

Evaluations

Convert red-team discoveries into a repeatable evaluation set scored by LLM evaluators, code-based checks, and custom criteria such as goal completion, accuracy, and policy adherence.

Policies and guardrails

Carry standards into evaluation, monitoring, and runtime safeguards; in the product demo, a detected policy violation (agent attempting to change destination and skip approval) is automatically added to the suite and re-run on every future test.

Multi-agent-type coverage

Supports RAG assistants, customer support agents, voice agents with policy-aware real-time actions, multi-agent systems, and transactional agents performing real actions like refunds, approvals, and transfers.

Framework- and model-agnostic testing

Connects to your existing stack — OpenAI, Anthropic, and Hugging Face models; LangChain, LlamaIndex, and LangGraph frameworks; CrewAI, AutoGen, and AutoGPT agent systems — plus SDK, API, or custom tools, with no rewrites or architecture changes.

Toolchain integrations

60+ integrations including Jira, Xray, Jenkins, GitHub, CircleCI, and Slack to enable end-to-end QA automation across your CI/CD pipeline.

QA test automation

Per the pricing page: AI-generated test cases verified by QA experts, self-healing test cases, detailed bug reports, and on the Scale plan cross-browser/OS execution, CI/CD integration, code review for PRs, and an AI debugger.

How It Works

Run adaptive tests

Connect your agent and run adaptive, real-world, and adversarial scenarios; a change in model, prompt, tool, or context can send an agent down unexpected paths, and BotGauge explores those paths to surface behavior before users see it.

Investigate with traces

Inspect prompts, responses, tool calls, context, and execution paths behind every result to understand the behavior behind a finding.

Turn findings into coverage

Convert important discoveries into evaluations and safeguards that run with every release, building coverage as the agent evolves.

Pros & Cons

Pros

Red-team discoveries automatically become evaluation checks and safeguards that run on every release, so adversarial testing compounds into permanent regression coverage
Framework- and model-agnostic: test agents built on OpenAI, Anthropic, or Hugging Face models and LangChain, LlamaIndex, LangGraph, CrewAI, AutoGen, or AutoGPT stacks without rewrites
Deep observability — every finding can be inspected down to prompts, responses, tool calls, context, and execution paths
Enterprise-grade controls including SOC 2 Type II compliance, SSO/SAML, and fine-grained access control per project and resource
Low-friction commercial terms: free start, pay-per-test-case billing, cancel anytime with no fees, and 24/7 support even during the trial

Who It's For

Best for

Engineering teams shipping LLM agents — customer support, voice, transactional, multi-agent, or RAG — that need adversarial testing before r
Teams that want every red-team finding converted into an automated check that runs on each subsequent release
Startups without in-house QA: the Launch plan offers AI-generated test cases verified by QA experts with 100% critical test coverage within
Enterprises that need SOC 2 Type II compliance, SSO/SAML, and fine-grained access control around agent testing

Not ideal for

Teams that want upfront, publicly listed dollar pricing and fully self-serve checkout for advanced features (the Scale plan is custom-quoted

Use Cases

Red-teaming agents before release to catch failures like prompt injection, destination changes, or skipped approval steps

Converting discovered agent misbehaviors into evaluation checks that run on every future release

Tracing agent decisions — prompts, tool calls, and execution paths — to diagnose unexpected behavior

Building regression coverage for RAG assistants, customer support and voice agents, multi-agent systems, and transactional agents

Outsourcing QA test-case generation and maintenance (self-healing tests, bug reports) for teams without dedicated QA staff

Pricing

BotGauge markets outcome-based pricing ('pay based on outcomes, not overhead'). The Launch plan is pay-per-test-case with unlimited test executions and can be started for free; the Scale plan is custom-quoted. No dollar figures are published. Subscriptions can be canceled anytime with no cancellation fees (billing stops after the current cycle), and payment is accepted via major credit cards (Visa

Launch Plan

Pay per test case (start for free)

Positioned for startups. Includes AI-generated test cases verified by QA experts, 100% critical test coverage within a week, functional/UI/API and integration test coverage, unlimited test executions, self-healing test cases, detailed bug r

Scale Plan

Custom pricing based on your needs

Marked 'Most Popular'. Everything in the Launch plan plus cross-browser and OS execution, seamless CI/CD pipeline integration, code review for PRs with intelligent execution, an AI debugger for faster fixes, SOC 2 Type II compliance, 24×7 e

FAQ

What does BotGauge actually do?

BotGauge red-teams AI agents with adaptive, adversarial, real-world scenarios, traces the prompts, tool calls, and execution paths behind every result, and converts findings into evaluations, policies, and guardrails that run with every release. Its pricing page also describes QA-as-a-service test automation with AI-generated, self-healing test cases.

How is BotGauge priced?

Pricing is outcome-based: the Launch plan is pay-per-test-case with unlimited test executions and a free start; the Scale plan has custom pricing based on your needs. No dollar figures are published — you can start for free or book a demo.

Can I cancel my BotGauge plan anytime?

Yes. You can cancel at any time with no cancellation fees; you are billed only for the current billing cycle and no future charges apply after cancellation.

What payment methods does BotGauge accept?

BotGauge