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Lightweight MIT-licensed JavaScript chatbot UI widget for building support-agent chat interfaces on top of any LLM.
Unified API gateway to 500+ AI models (GPT, Claude, Sora, image/video) with OpenAI-compatible endpoints and discounted usage.
One-click bug-reporting tool that auto-captures console, network logs and repro steps for developers.
Test-automation platform for web, API, mobile, and desktop with no-code to full-code authoring and cloud execution.
Documentation and knowledge platform that keeps developer docs self-updating and queryable by AI agents.
No public pricing
No public pricing
Free trial available
No public pricing
Free trial available
Free trial available
- ✦MIT-licensed and free to use
- ✦Lightweight (~65kb) JavaScript widget
- ✦4 message display modes plus typewriter effect
- ✦Markdown support and chat history
- ✦Backend hook to integrate any LLM
- ✦Responsive, mobile-friendly, read-only mode
- ✦Single OpenAI-compatible API for 500+ models
- ✦Chat, image, video and audio models
- ✦Discounted per-request pricing
- ✦One dashboard for keys, quotas and usage
- ✦Multi-region routing and failover (99.9% uptime)
- ✦Multiple payment methods incl. crypto
- ✦One-click bug capture via browser extension
- ✦Automatic repro steps
- ✦Console, network and device logs
- ✦Instant replay of recent activity
- ✦Backend tracing and an AI debugger
- ✦Integrations with Jira, Linear, GitHub and Slack
- ✦No-code, low-code and full-code test authoring
- ✦Web, API, mobile and desktop testing
- ✦Test management and planning
- ✦Cloud-based test execution
- ✦Reporting and analytics
- ✦AI-assisted test generation and monitoring
- ✦Self-updating documentation
- ✦Web-based documentation editor
- ✦Custom domain hosting
- ✦Built-in search and API playground
- ✦MCP server for agent access
- ✦Authentication and access controls
- →Adding an AI chat UI to web apps
- →Building LLM-powered support agents
- →Embedding chat widgets in existing products
- →Prototyping chatbot interfaces
- →Accessing many AI models via one API
- →Cutting AI model API costs
- →Building multimodal AI apps
- →Consolidating AI billing and keys
- →Filing detailed bug reports
- →Reproducing issues faster in QA
- →Sharing debug context with engineers
- →Triaging support bug reports
- →Automate regression testing
- →Unify API, web and mobile tests
- →Manage manual and automated tests together
- →Scale test execution in the cloud
- →Publish and maintain developer documentation
- →Expose docs to AI agents via MCP
- →Host a branded docs site on a custom domain
- →Give teams a collaborative doc editor
