Karate Agent is an enterprise-grade AI browser automation platform. LLM-powered UI testing that handles complex SPAs, adapts to UI changes, and runs entirely on your infrastructure — a modern Selenium alternative, Playwright alternative, and Cypress alternative for the AI era.
Bring your own LLM — Claude, GPT, Llama, Qwen, DeepSeek, Gemini, or any self-hosted model via Ollama. Your data never leaves your network.
~30s
Scripted flows
8–19 min
Pure LLM approach
72x
Fewer page scans
100% Self-Hosted · Bring Your Own LLM · Java 21 + Docker
Core Capabilities
Use visible text like {button}Submit instead of CSS or XPath. Locators match what users see, so they survive UI refactors without test maintenance.
Works with Claude, GPT, Gemma, Llama, or any local model via Ollama. Switch providers without changing your tests. Run fully air-gapped with on-premise models.
Structured JSON responses and look() diffing reduce page scans by 72x. Scripted flows consume zero tokens. The LLM is only called when recovery is needed.
Every session produces H.264 video at 8 fps for audit and QA review. Live noVNC dashboard lets you watch, pause, inject commands, and resume browser sessions in real time.
A single karate_eval tool works as an MCP server with Claude Code, VS Code Copilot, and other MCP-compatible clients for seamless developer workflows.
Purpose-built for complex enterprise applications like Guidewire, Salesforce, and ServiceNow. Cursor-pointer discovery handles dynamic widgets and shadow DOM elements.
Go deeper
Everything you need to evaluate, deploy, and scale Karate Agent for your team.
Display-text locators, BYO LLM, token efficiency, session recording, MCP integration, and enterprise SPA support.
View all featuresThe hybrid approach: scripted flows run at native speed, LLM invoked only for recovery. See the architecture in detail.
See the architectureEnterprise SPAs (Guidewire, Salesforce, ServiceNow), regression testing, visual verification, and CI/CD integration.
Explore use casesSetup guides, API reference, MCP integration, Docker deployment, and configuration reference.
Read the docsTransparent pricing for teams of all sizes. Free tier available for evaluation and small projects.
See pricingSee how Karate Agent compares to other AI testing tools and traditional automation approaches.
View comparisonsHow It Works
Scripted flows run at native JavaScript speed. The LLM is only invoked when a scripted step fails, combining deterministic reliability with AI-powered recovery.
Describe what to verify in plain English or use Interactive Mode to explore and discover locators.
Reusable .js flow files execute at native speed without LLM token consumption.
When a scripted step fails, the LLM analyzes the page and adapts. Token-efficient structured JSON keeps costs low.
Every session is recorded as H.264 video at 8 fps. Full audit trail with noVNC live view for debugging.
Most test steps are deterministic and should run fast. Karate Agent executes scripted flows at native JavaScript speed with zero LLM calls. When the UI changes unexpectedly, the LLM kicks in to recover the flow.
The look() diffing function reduces page scans by 72x compared to sending full page HTML on every step, keeping token costs minimal.
// scripted flow - zero tokens
click('{button}Submit')
waitFor('{div}Order Confirmed')
screenshot()
// LLM recovery - only on failure
if (failed) {
llm.analyze(look())
llm.recover(step)
}
Deployment
Self-hosted Docker deployment with full session isolation. Every browser runs in a dedicated container. Air-gap ready for regulated industries.
Deploy on your own servers. No data sent to external services.
Each session runs in a dedicated container with its own Chrome instance.
Run with local LLMs via Ollama. No internet connection required.
Standard REST API works with any pipeline. Trigger tests with a single curl command.
Enterprise
Enterprise single sign-on with role-based access. Integrate with your existing identity provider.
Every session recorded with H.264 video, step logs, and full execution history for compliance review.
Each test session runs in a dedicated Docker container with its own Chrome instance. Complete isolation between runs.
No per-seat installations. A single shared dashboard lets the whole team view results, watch recordings, and manage sessions.
What enterprise teams ask before adopting AI browser automation.
AI test automation uses large language models (LLMs) to drive real browsers and validate web applications the way a human would — navigating pages, filling forms, and verifying outcomes — without hardcoded selectors or brittle scripts. Karate Agent is an enterprise-grade AI test automation platform that works with Claude, GPT, Llama, Qwen, and self-hosted models, and runs entirely on your own infrastructure.
For many enterprise use cases, yes. Traditional tools like Selenium and Playwright rely on brittle CSS or XPath selectors that break when the UI changes. Karate Agent uses display-text locators and LLM-powered recovery to adapt to UI changes automatically, dramatically reducing test maintenance. Many teams use Karate Agent as a Selenium alternative or Playwright alternative, especially for complex enterprise SPAs like Guidewire, Salesforce, and ServiceNow.
Karate Agent is LLM-agnostic — bring your own LLM. It works out of the box with Anthropic Claude, OpenAI GPT-4, Google Gemini, and any open-source model served via Ollama (Llama, Qwen, DeepSeek, Mistral, Gemma, GLM, Kimi). You can also connect to any OpenAI-compatible endpoint, including self-hosted vLLM deployments.
Karate Agent is 100% self-hosted. It runs as a Docker container on your infrastructure — no SaaS dependency, no data exfiltration, no telemetry. Paired with a local LLM via Ollama, the entire stack runs air-gapped. This makes it suitable for regulated industries like financial services, insurance, and healthcare that require full data sovereignty.
Claude computer use is a cloud-hosted vision-based agent that sends screenshots to Anthropic’s servers. Karate Agent is self-hosted, DOM-based (not vision-based), and 10–50x more token-efficient. Your application data never leaves your network. Karate Agent also integrates natively with CI/CD pipelines via a standard REST API, produces deterministic HTML reports with video recordings, and works with any LLM provider — not just Claude.
Dramatically less than screenshot-based agents. Karate Agent reads the DOM directly — extracting structured elements, roles, and labels — rather than sending pixel images to the LLM. This is 10–50x more token-efficient. Combined with scripted flows that consume zero tokens and a look() diffing function that reduces page scans by 72x, typical enterprise runs spend a fraction of what pure-LLM browser agents consume.
Yes. Karate Agent is Docker-native. Each test session runs in its own container with a dedicated Chrome instance. A standard REST API triggers test runs, so a single curl command integrates with Jenkins, GitHub Actions, Azure DevOps, GitLab CI, or any pipeline. Kubernetes orchestration is supported for horizontal scale.
AI coding assistants like Cursor, GitHub Copilot, and Claude Code have accelerated development velocity — but traditional test automation can’t keep up. Karate Agent is built for this new cadence: AI-powered tests that adapt to UI changes without maintenance, run in CI/CD on every commit, and validate behavior the way a user would. Karate Agent also exposes a karate_eval tool via MCP, so developers can drive tests directly from Claude Code, Copilot, or Cursor.
AI-native UI verification that runs on your infrastructure. Get started today or talk to our team about enterprise deployment.